Source: UNIV OF MARYLAND submitted to NRP
TRANSFORMING SHELLFISH FARMING WITH SMART TECHNOLOGY AND MANAGEMENT PRACTICES FOR SUSTAINABLE PRODUCTION
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
ACTIVE
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
1023149
Grant No.
2020-68012-31805
Cumulative Award Amt.
$10,000,000.00
Proposal No.
2019-08276
Multistate No.
(N/A)
Project Start Date
Sep 1, 2020
Project End Date
Aug 31, 2026
Grant Year
2020
Program Code
[A9201]- Sustainable Agricultural Systems
Recipient Organization
UNIV OF MARYLAND
(N/A)
COLLEGE PARK,MD 20742
Performing Department
Mechanical Engineering
Non Technical Summary
The United States has abundant suitable coastal land with great potential to achieve high-volume production of shellfish as a sustainable, eco-friendly, and healthy source of protein for the growing population. However, the US shellfish industry currently faces significant production bottlenecks due to outdated technology and tools. In light of today's advances in sensing and control, robotics, and artificial intelligence, which have led to transformative development in terrestrial agriculture, great opportunities have arisen to revolutionize shellfish aquaculture. The overarching goal of this project is to improve quality of life for farmers and society as a whole by implementing a smart sustainable shellfish aquaculture management (S3AM) framework to enhance nationwide shellfish production and economic viability of shellfish farm operations while maintaining environmental health. The specific objectives of this project include the following: i) develop and implement smart technologies and management solutions to improve farm productivity and profitability, ii) model and assess economic impacts of the S3AM framework in the East, West, and Gulf coastal regions, iii) build a nationwide extension network to engage stakeholders for broader impact, and iv) educate future generations of the workforce to address globally pressing issues of sustainability. It is expected that novel technologies and precision farming management practices will be developed as part of the S3AM framework, which will help farmers gain in-depth knowledge of their farm and crop conditions and provide them with highly efficient harvesting tools. S3AM will lead to sustainable shellfish production in the long term, enabling coastal communities and stakeholders to gain global competitiveness. S3AM will also promote economic development of rural coastal areas by creating new businesses to provide smart technology tools and management services for farmers. S3AM will further promote alternative diets focused on healthy seafood, offering substantial health benefits.
Animal Health Component
50%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
0%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
3070811202030%
3073723208020%
3077310106015%
4027299202020%
6015330301015%
Knowledge Area
601 - Economics of Agricultural Production and Farm Management; 307 - Animal Management Systems; 402 - Engineering Systems and Equipment;

Subject Of Investigation
3723 - Oysters; 5330 - Farm structures and related facilities; 7310 - Experimental design and statistical methods; 7299 - Research equipment and methods, general/other; 0811 - Shellfish;

Field Of Science
2020 - Engineering; 1060 - Biology (whole systems); 2080 - Mathematics and computer sciences; 3010 - Economics;
Goals / Objectives
The long-term goals of this project are to i) enhance nationwide shellfish farm production while preserving environmental heath, ii) sustain economic viability of shellfish farm operations, and iii) enhance quality of life for growers and society as a whole, through the development and implementation of a smart sustainable shellfish aquaculture management (S3AM) framework. To achieve these interrelated goals, an interdisciplinary team consisting of investigators with diverse expertise in a range of relevant fields (Engineering, Computer Science, Biology, Environmental Science, Aquaculture) and readily engaged stakeholders from three coastal regions (East, West, and Gulf) has been brought together to pursue five supporting objectives through integrated research, education, and extension activities as follows:1. Develop enabling technologies and management solutions for the S3AM framework to improve farm productivity and profitability in coastal regions nationwide. 2. Validate the S3AM technologies in the laboratory and conduct farm trials of the S3AM framework in East, West, and Gulf coastal regions. 3. Model and assess economic impacts of the proposed S3AM framework. 4. Integrate knowledge generated from the S3AM into education and outreach activities to prepare future generations of workforce to address globally pressing issues of sustainability. 5. Build a nationwide extension network to engage stakeholders, disseminate the S3AM, and assess its short, medium, and long-term outcomes.
Project Methods
The proposed smart sustainable shellfish aquaculture management (S3AM) framework entails an integrated, system-based approach to address the industry needs and tackle the challenge of establishing sustainable shellfish farming with significantly enhanced productivity and profitability. An interdisciplinary team consisting of investigators with diverse expertise and engaged stakeholders from three coastal regions (East, West, Gulf) is brought together to pursue integrated research, education, and extension activities. Detailed methods are the following:1. Development of Enabling Technologies and Management PracticesThe S3AM framework employs two novel technology tools to revolutionize farm management practices: S3AM monitoring and S3AM harvesting. In S3AM monitoring, the team will develop novel environmental sensing and imaging tools and AI-based mapping algorithms and implement them on an underwater drone to perform lease environmental monitoring and crop inventory monitoring. Before planting seeds, S3AM environmental monitoring will be used to map the water quality and bottom substrate conditions. This will provide farmers with accurate bottom lease conditions to enable precision planting, which will help reduce losses due to seed mortality and increase farm productivity. During growing seasons, S3AM crop inventory monitoring will be conducted multiple times to create high-precision crop inventory maps to help farmers improve inventory records, as well as make predictions on future farm productivity and profitability. By using the precise crop inventory maps created by S3AM monitoring, S3AM harvesting will be developed to create an optimized path for the dredging vessel to perform high-efficiency precision harvesting, which maximizes coverage, minimizes the dredging path, and reduces labor and energy used during harvest. In addition, the team will develop user-friendly farm management, data analysis, and visualization software based on the mapping data obtained with S3AM monitoring and existing farm management models. This software will help farmers better maintain their data assets, manage and predict the conditions of their crops, as well as develop and evaluate business plans for improving the economic viability for farming operations.2. Evaluation of S3AM in Laboratory and in Farm TrialsIn Year 1, the team will collect water quality data from sources including buoys and NOAA's System Wide Monitoring Program (SWMP) for 12 U.S. coastal states. The data will be analyzed to ensure wide-range applicability of S3AM monitoring across the Nation's bays and estuaries. In addition, the team will work with growers from three costal regions to obtain benthic habitats images to understand habitat complexity. In Year 2, the team will use the collected water quality and benthic images to recreate a range of environmental conditions and test the S3AM monitoring at Shellfish Aquaculture Innovation Laboratory, located in the Chesapeake Bay. In Years 3 and 4, field tests will take place in the Horn Point Laboratory Demonstration Farm, a 2.25 acres licensed lease used to evaluate shellfish gear and management practices. S3AM will be used to evaluate substrates within the farm as well as evaluate oyster densities in Sandy Hill Oyster Sanctuary. The results will be validated using side-scan sonar and coring sediment samples. In Years 3, 4 and 5, S3AM inventory monitoring and harvesting will be evaluated in active bottom leases of the three coast regions. During a single season, multiple scans of the same beds will be performed to track growth of individuals, confirm live and dead animals, and determine how growth rates vary within a single farm. An animal size and location map will be created to obtain a smart harvesting dredge route that enables growers to more efficiently target harvest of market-size animals and limit disturbance of young, smaller animals. The size distribution and catch per unit effort of smart harvest dredge routes will be statistically compared to traditional harvesting (dredging indiscriminately back and forth within the lease).3. Economic Modeling and Assessment of S3AMThe team will access the economic feasibility of S3AM, and identify economic barriers and opportunities through modeling. First, the team will engage with national stakeholders to develop economic cost models to reflect farming practices on the East, West, and Gulf Coasts. In addition to the annual cost models for each region/scale cost model, an investment analysis will be developed. The economic cost models and the associated performance metrics will constitute the base against which the economic performance of S3AM will be measured. Second, the team will input the performance data from S3AM lab test and field trials into each of the cost models developed. A number of metrics will be compared between models with and without S3AM. Effects of region and production scale on whether S3AM results in improved economic outcomes for farmers will be examined. In addition, analysis will be performed to evaluate the potential of providing S3AM as a service through a business instead of requiring each separate farm to purchase the equipment.4. Integration of S3AM into Education and OutreachBy leveraging UMD's strong AI/robotics program and UMES' proven success for engaging underrepresented minority and FGCs students, the team will pursue educational activities for undergraduate and graduate students through curriculum development in robotics and artificial intelligence as well as summer Undergraduate Research Programs. In addition, the team will create new opportunities for youth in summer activities (4-H summer camp) and robotics competitions (4-H robotics challenge and competition) to promote the sustainability theme of S3AM.5. S3AM Extension ActivitiesThe team will establish a nationwide coastal extension network through interactions with colleagues in aquaculture programs in USDA supported 1862 and 1890 institutions, which will help support the S3AM development and implementation. To engage the stakeholders, a National Advisory Board (NAB) will be established, which includes industry representatives, extension faculty, and researchers from East, West, Gulf coasts to provide ongoing advice related to industry needs and to aid in guiding S3AM development. Regional workshops will be organized to teach the use of S3AM technology to farmers and share results and data collected through S3AM development, testing, economic analysis and pilot applications. Publications and video products will be created to disseminate the results of S3AM.The evaluation will be based on a developmental evaluation approach, guided by the project logic model and metrics and following the timeline and milestones. Evaluation goals and methods are the following:Goal 1: Monitor progress of new technologies and management practices for S3AM through research results. Determine and gather critical outputs based on monthly on-line surveys aggregated across researchers and descriptive statistics (counts, percentages) data analysis.Goal 2: Assess the effectiveness of field trials for S3AM through research results. Determine and gather critical outputs from field trials based on monthly on-line surveys aggregated across trials and descriptive statistics data analysis.Goal 3: Assess the economic impacts of S3AM through economic modeling. Determine economic impact based on economic cost modeling and economic data analysis.Goal 4: Assess knowledge, attitudes, skills, and aspirations (KASA) changes in students and growers through program evaluation. Determine changes in KASA based on surveys of instructors, students, and growers and Descriptive and Inferential Statistics data analysis.Goal 5: Assess the capacity of the S3AM national Extension network. Determine level of capacity built in Extension network based on key informant interviews and Surveys and Descriptive Statistics data analysis.

Progress 09/01/23 to 08/31/24

Outputs
Target Audience: Robotics and Computer vision researchers who are interested in general robotics tasks, underwater robotic tasks (navigation, recognition),optimization-based control and state estimation of autonomous systems, underwater localization and 3D reconstruction. Researchers in underwater robotics, agricultural and environmental engineering, and aquaculture productions. Bottom culture oyster farmers in the USA, primarily on the east coast Environmental groups working on oyster reef restoration Non-Government Organizations (Chesapeake Bay Foundation, Oyster Recovery Partnership, Maryland Watermen's Association, Louisiana Oyster Task Force, East Coast Shellfish Growers Association, Pacific Coast Shellfish Growers Association, Oyster South, National Aquaculture Association, Shellfish Growers of Virginia, United States Aquaculture Society, National Shellfisheries Association) Oyster aquaculture industry (on-bottom oyster farmers, hatchery staff, equipment manufacturers, seafood processors, and investors) Oyster farmers and associations (East Coast Shellfish Grower's Association; Pacific Coast Shellfish Grower's Association; National Shellfisheries Association, World Aquaculture Society, National Aquaculture Association) Aquaculture researchers; Extension; state and federal aquaculture or other marine natural resource managers 4-H Members - Youth ages 8-18; K-12 students; Youth/students interested in STEM careers, especially robotics and aquaculture Project partners and National Advisory Board members Changes/Problems:We removed Louisiana State University from our collaborators because our Co-PI moved to another position and there was not a suitable candidate to transfer the work to. We have identified a collaborator at the University of Southern Mississippi Thad Cochran Marine Aquacultrue Center to fill this role as our Gulf coast collaborator. We are currently processing paperwork and expect to have them onboarded in the coming months. The evaluation specialist that we had identified at the University of Maryland College Park has left the university and was not able to continue their work on our project. We have identified Dantzker Consulting as our replacement and they have been cleared to begin work through UMD. What opportunities for training and professional development has the project provided?Goal 1: SCUBA training was completed for scientific diving to enable experiment setup in Chesapeake Bay in coming months Goal 2: We have been leading efforts to enable team members to acquire Scientific Diving certification which will both help with the progress this project and provide professional development to those participating in the certification. Goal 3: Professional development and training opportunities for PhD graduate student, Renu Ojha, to interact with commercial producers, industry leaders, Extension and academia. Opportunities to present research and findings at professional meetings. Goal 4: The project has provided excellent training and learning opportunities for students who are interested in machine learning and artificial intelligence. Due to support of this project, we were able to offer new courses such as ENEE 422 Introduction to Machine Learning to senior students (engineering and computer science). We were able to offer capstone design course and advise students to conduct research in oyster activity monitoring and underwater drone design. We were able to offer a summer REU type program that allows students to continue research in the summer. Goal 5: Maryland watermen's tradeshow Aquaculture America 2024 116th Meeting of the National Shellfisheries Association 2024 S3AM Summit and Expo S3AM Webinar Series S3AM Wise Newsletter How have the results been disseminated to communities of interest?Goal 1: Our robots and research were showcased at Maryland Day for general public to know more about how engineering and computer science can be used in aquaculture Goal 2: S3AM Webinar on water quality mapping Horn Point Lab open house (~700 local attendees) Local, regional, and national conference presentations Goal 3: Results on technology adoption scenarios have been presented at the 2024 Aquaculture America meeting and the 2024 National Shellfish Association Meeting. Goal 4: The research results accomplished by the summer REU students will be posted on the REU website hosted by Salisbury University. The Summer students will also conduct poster presentation etc to disseminate the results to other students, faculty at large. Goal 5: Special sessions on Advanced Technology in Aquaculture at Aquaculture America 2024 in San Antonio, TX and the 116th Meeting of the National Shellfisheries Association in Charlotte, NC. The 2024 S3AM Summit and Expo. This served as a team meeting, as well as a science fair and lab tours to the interested researchers and the public, as well as a report out to the national advisory board members to gain formative evaluation feedback. The S3AM Webinar series provided 3 webinars this reporting period. The project website www.S3AMoysters.com provides a project overview and access to our project videos and newsletters. The S3AM SamWise Newsletter provides quarterly project updates and announcements for upcoming events. The Ag Awareness day exposed Maryland's 7th graders to the S3AM project using 4H and Extension materials. Ongoing contact was maintained with oyster growers throughout the past years of the project through normal Extension programs and activities. This was carried out to engage them about their potential use of the developed products to build their businesses for more production and profit in the future. Use of active leased grounds was obtained by Extension faculty for the project research staff to test the newly developing systems in real world conditions and to gain input from farmers on the actual conditions encountered in managing their grounds. This involvement is a foundation of extension work and creates a strong relationship between farmers and research faculty in creating equipment and methods that are used to solve identified problems and build production and profitability to aid the food supply. Ongoing information will also be delivered electronically to growers on the state of the project and the goals sought to benefit profitable production of oysters using bottom culture methods and be included in all in-person educational programs developed by Extension faculty included in the project. Extension team worked directly with research personnel to support field development of equipment by communicating with oyster farmers and organizing them to utilize their vessels to tow harvest gear for trials. The field team then measured a suite of parameters from the harvest vessels while nearby research vessels tracked water quality and utilized aerial drones to document the harvest process and its effect on measurements during underwater operations. The team identified and obtained support from commercial harvesters in several locations to test gear in a variety of field conditions. The initial trials were in Maryland with additional ones added in Pacific areas of Washington to obtain data on a suite of tidal and water quality differences. Extension faculty identified and interacted with research faculty and oyster farmers with leased grounds in a suite of locations to obtain S3AM measurements of a variety of parameters and assisted engineering faculty in equipment development during field trials. Areas included leases in proximity to the UM Horn Point Lab that were used for dive and drone assessment of planted oysters. Other leases were used in open Chesapeake Bay waters as well as other locations nationally to provide a suite of parameters for the research. Grounds on Pacific coast were used for a variety of situations for equipment testing in very varied conditions. Field trials were used to develop underwater drone capabilities to determine oyster populations, their health and growth, while surface units were maneuvered by remote control. Faculty provided feedback on development of the equipment to industry members who allowed their leases to be utilized for research to ensure they felt part of the project development. Extension Faulty worked with the economics team to develop a list of needed items from other research partners to develop an economic model for the S3AM project. One publication was developed and published in Aquaculture Magazine about the project and the economic implications for the S3AM. As more field trials are conducted and information gathered, the economic model will be further refined. Project information was disseminated through a variety of Extension activities. These included presentations at regional and national conferences including the World and United States Aquaculture Societies and the National Shellfisheries Association to provide information about development of the research to colleagues and industry. Regional and state conferences were used to showcase equipment at booths and answer inquiries from industry as well as having videos of field trials during show times. These included the long running East Coast Commercial Fishermen's and Aquaculture Trade Expo in Ocean City MD and biennial Shellfish Aquaculture Conferences held for industry in Maryland and Delaware. Tours of the research facilities being used for equipment development were included for four groups and presentations were included at industry workshops held in Maryland. These educational programs included the goals and objectives of the project and current status of the tools being developed to aid producers of bottom cultured oysters. Demonstrations of surface and underwater drones were provided to four groups with videos taken and used for online information of the project. Extension faculty participated in all engineering faculty in virtual meetings of the S2AM on a biweekly basis to continue to be included in the development of the technology, provide challenges to development of the equipment and engage in discussions of ways to develop the project to provide useful data to industry while minimizing time and labor that is currently the case in conducting business. Faculty provided input on project development including the realities of industry needs and how producers will benefit from the developed technology. Faculty provided ongoing electronic information to a suite of growers to keep them apprised of research development and delivered feedback from industry to project research staff. What do you plan to do during the next reporting period to accomplish the goals?Goal 1: The next steps are to leverage our state estimators to develop a trajectory following controller to enable the ROV to follow specific paths for surveying and data collection. This will be implemented in practice at oyster leases. Mapping and 3D reconstruction using the data collected by ASV. This will enable us to generate a map of the leases in east coast as well as west coast to map out oyster density and size. More control-based algorithms will be developed to enable the ROV to hold certain depths, fixed positions and orientation to facilitate accurate imaging and sensing. Autonomous navigation of the ASV and ROV is planned to be implemented to follow certain trajectories in the presence of disturbance of the environment. Consider the dredge capacity and the dredge lift up time point in our algorithm. We will also collect some real harvesting paths. Moreover, we will apply our method on real oyster lease once we get the established real oyster distribution map. Optimize our new oyster segmentation technique. After this, the goal for next year would be to integrate the oyster segmentation software with the navigation software, so that we can obtain a map of the sea bottom painted with oyster density. Goal 2: Continue to support engineering and economic research as well as education and Extension activities. Goal 3: Complete economic model development and scenarios for S3AM as a service to farmers. Complete review paper on the trends of technology adoption in oyster aquaculture. Submit abstract to present project results at Agricultural and Applied Economics Association conference in 2025. Finalize parameters and attributes for discrete choice experiment on consumer willingness to pay for oyster products. Goal 4: We will continue to carry out the tasks outlined in the project goals, especially for educational purposes, for example, offering a new course "Artificial Intelligence" in the fall semester, and continue to offer capstone design courses in the fall 2024 semester. Goal 5: Continue to provide Extension consultation services to the oyster aquaculture industry Continue to conduct workshops, symposia, and conferences related to aquaculture and technology Maryland Aquaculture Conference, Annapolis, MD (Nov 2024) Watermen's Expo, Ocean City, MD (Jan 2025) Aquaculture 2025, New Orleans, LA (March 2025) 2025 S3AM Summit and Expo, TBD (Summer 2025) Continue developing S3AM video content, webinars, and the S3AM SamWise Newsletter Conduct evaluations of events and the project success.

Impacts
What was accomplished under these goals? Goal 1: Successfully interfaced a camera and multibeam sonar on the ASV to collect data while autonomously navigating in a farm field on both east coast (Chesapeake Bay) and west coast (Totten Inlet) Integrated a water quality sensor on the ROV to obtain a 3D water quality map, by leveraging the previously retrofitted DVL and GPS sensors on the ROV Used a water quality sensor to 3D map water quality using our state estimation techniques to successfully obtain measurements with the accuracy under a meter. Developed (in collaboration with UMIACS) and implemented a strategy to navigate the ROV based on oyster density to generate an efficient map. This involved usage of algorithms in robot control and imitation learning and results were presented and published at ICRA 2024. Developed novel algorithms for constrained optimization problems. The algorithms can be employed to solve autonomous control problems of underwater vehicles. Built an oyster distribution map simulator (ShellSim) to test and optimize the dredging path calculation algorithm in the absence of areal oyster density map usinga Variable Neighborhood Search (VNS) based approach with a novel simplification scheme called merge-and-filtering. The algorithm considers the boat turning radius in path planning.We demonstrated that our path-planning method is effective in various oyster distributions using our ShellSim simulator. Builta system for live harvest path recording and displaying, including an external RTX GPS (~1 ft accuracy) and live recording/displaying software. We integrated the GPS with the FindMyOyster App, which allows users to display a target path and boat position during harvest. Through on-boat field tests at HPL, we validated the viability of our framework for real-world applications. Developed a "Metaverse" solution to the vast underwater image collections needed for machine learning. Using mathematical modelling and Generative Adversarial Networks, we produce life-like synthetic images of the sea bottom, providing access to unlimited data to perform the learning of the segmentation technique and improve monitoring accuracy. Developed a model to estimate the density of oyster seed or substrate on the bottom based on the recorded boat path. Knowing the current density allows farmers to focus their resources on areas that are low and thus gain better resource utilization, potentially decreasing the amount of seed needed or increasing the yield per acre. Developedlearning techniques that learn sequential data by utilizing the underwater vehicle's acceleration to obtain underwater scene depth and by developing a robust solution to the camera pose estimation problem. Benchmarked different types of sonar for effective segmentation and mapping, and created a segmentation technique applicable to both virtual and real images. Developed machine learning techniques that leverage human feedback, including an intermediate representation that is agnostic to image type and object, facilitating various navigation tasks. Goal 2: PSI hosted S3AM team members for two data collection trials with sonar, underwater video and drone aerial imagery at an intertidal shellfish farm in Totten Inlet, South Puget Sound, WA. The ASV was used to collect data on oyster density, size and live/dead classification at the West Coast (Totten Inlet). Experiments validated our usage of successful sensor integration, autonomous navigation and perception. Extensive trials were conducted at various times of the year in different parts of the Chesapeake Bay to obtain a 3D map of water quality (salinity, dissolved oxygen, etc.). These experiments validated our state estimation approaches to localize the ROV, as well as integration of water quality sensors with the ROV. PSI continues to generate a variety of environmental data to inform smart technology management practices to maximize oyster harvest efficiency. PSI also developed and refines proof of concept workflows for processing and analyzing drone aerial imagery of intertidal shellfish. Goal 3: Refined the baseline model of technology adoption on bottom culture oyster farms. Refined the analytical framework for the economic assessment of S3AM on small (200 bushels), medium (2000 bushels), and large (6000 bushels) oyster farms. Interviewed with Maryland and Virginia oyster farmers about their preferences and perceptions of technology adoption. Initiated model development for Virginia bottom culture oyster farm model. Developed a discrete choice experiment to assess consumer willingness to pay for specific oyster attributes.Established a contract with QuestionPro for collecting responses of the discrete choice experiment. Presented economic assessments of technology adoption at Aquaculture America 2024 and National Shellfish Association (NSA) 2024 conference. Goal 4: UMES with Salisbury University continued a summer undergraduate research program focused on machine learning and artificial intelligence for underwater oyster farming applications. In summer 2024, 3 students conducted "Machine Learning and Image Processing for Shellfish Object Detection by Images and Videos". ENEE 422 Introduction to Machine Learning was offered in Spring 2024 semester to a group of students in engineering and computer science.Students worked on underwater fish identification and classification by machine learning using Python or Mathlab to develop algorithms for processing underwater camera images. Two senior capstone projects were completed in ENGE 476. "Design of a short baseline acoustic transponding system for positioning of an underwater drone" was completed by Ronnie Harmon and Olanrewaju Adeoye. As a result of this USDA project, one of the students registered Honors Thesis entitled "Evaluation of YOLOv8's capabilities to detect and track vehicles and pedestrian objectives" in spring 2024, and Alexander Mekovsky was admitted to the MS program in Data Science at Carnegie Mellon University. Student research entitled "Machine Learning and Computer Vision Techniques to Identify and Monitor Ostreidae Nondisruptively", was presented at the National Shellfish Association Conference in March 2024. 4-H Youth Underwater Robot and Oyster Curriculum - under development to be peer reviewed. PSI staff continue to support team understanding of existing shellfish related educational curriculum, including PSI's extensive offerings related to shellfish biology, nearshore ecology and topics related to shellfish aquaculture. PSI also continues to utilize our extensive existing collaborations with U.S. west coast shellfish farmers to disseminate S3AM activities and opportunities. Goal 5: UMD Extension continues to engage with oyster farmers and industry stakeholders for information transfer and research partnership. Faculty provided feedback on development of the equipment to industry members who allowed their leases to be utilized for research to ensure they felt part of the project development. Opportunities for research dissemination were created at national, regional, and local conferences, including special sessions on aquaculture technology. This includes the 2024 S3AM Summit and Expo. PSI has extensive existing collaborations with stakeholders, specifically U.S. west coast shellfish farmers. We utilize these collaborations to disseminate S3AM activities and opportunities.

Publications

  • Type: Journal Articles Status: Under Review Year Published: 2024 Citation: Wei-Yu Chen, Chiao-Yi Wang, Kaustubh Joshi, Yi-Hsuan Chen, Sandip Sharan, Senthil Kumar, Allen Pattillo, Miao Yu, Nikhil Chopra, Yang Tao, Ship Maneuvering and Dynamic Navigation for Precision Aquaculture based on Combined Nomoto-Dubins Model Ocean Engineering (under review)
  • Type: Journal Articles Status: Published Year Published: 2024 Citation: K. Chakrabarti and N. Chopra, "On Convergence of the Iteratively Preconditioned Gradient-Descent (IPG) Observer," in IEEE Control Systems Letters, vol. 8, pp. 1715-1720, 2024, doi: 10.1109/LCSYS.2024.3416337
  • Type: Journal Articles Status: Under Review Year Published: 2024 Citation: Wei-Yu Chen, Chiao-Yi Wang, Kaustubh Joshi, Yi-Hsuan Chen, Sandip Sharan Senthil Kumar, Nikhil Chopra, Yang Tao Ship Maneuvering and Dynamic Navigation for Precision Aquaculture based on combined Nomoto-Dubins model (under review, submitted to Elsevier Ocean Engineering)
  • Type: Journal Articles Status: Submitted Year Published: 2025 Citation: Chiao-Yi Wang, Guru Nandhan ADP, William Chen, Yi-Ting Shen, Sandip Sharan Senthil Kumar, Alexander Long, Alan Williams, Gudjon Magnusson, Allen Pattillo, Don Webster, Matthew Gray, Miao Yu, and Yang Tao ShellCollect: A Framework for Smart Precision Shellfish Harvesting Using Data Collection Path Planning (submitting to IEEE Access)
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: M Evanusa, S Shrestha, V Patil, C Ferm�ller, M Girvan, Y Aloimonos, Deep-Readout Random Recurrent Neural Networks for Real-World Temporal Data, SN Computer Science 3 (3), 1-12, 2022.
  • Type: Other Status: Other Year Published: 2024 Citation: Kaustubh Joshi, Tianchen Liu, Nikhil Chopra. Localization, Navigation and Autonomous Control of ROV. MRC Research Symposium (poster presentation).
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2024 Citation: Matthew W. Gray, Miao Yu, Michael Xu, Allen Pattillo, Yang Tao, Kaustubh Joshi, Nikhil Chopra, Don Webster, Matt Parker, Cathy Liu, Bobbi Hudson, Yuanwei Jin, Chiao-Yi Wang, Yiannis Aloimonos, Alan Williams, Gudjon Magnusson. Smart, Sustainable, Shellfish Aquaculture Management: Advancing Technological Development of Oyster Aquaculture In The USA Aquaculture Europe 2024.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2024 Citation: Lin, Xiaomin, Nare Karapetyan, Kaustubh Joshi, Tianchen Liu, Nikhil Chopra, Miao Yu, Pratap Tokekar, and Yiannis Aloimonos. "UIVNAV: Underwater Information-driven Vision-based Navigation via Imitation Learning." 2024 IEEE International Conference on Robotics and Automation (ICRA 2024).
  • Type: Conference Papers and Presentations Status: Under Review Year Published: 2024 Citation: Tianchen Liu, Kushal Chakrabarti, Nikhil Chopra. Variant Predictor-Corrector Method for Linear Predictive Control using Modified Uzawa Algorithm. 2024 63rd IEEE Conference on Decision and Control (CDC) (Under review).
  • Type: Other Status: Other Year Published: 2024 Citation: Tianchen Liu, Kushal Chakrabarti, Nikhil Chopra. Iteratively Preconditioned Gradient-Descent Approach for Moving Horizon Estimation Problems. MRC Research Symposium (poster presentation).
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2024 Citation: Chiao-Yi Wang, Alex Long, Guru Nandhan A D P, William Chen, Sandip Sharan Senthil Kumar, Allen Pattillo, Don Webster, Miao Yu, Yang Tao The Smart, Sustainable Shellfish Aquaculture Management (S3AM)  Smart Precision Harvesting: Simulation Model and Field Test Aquaculture America 2024 Feb., San Antonio, Texas. (oral presentation)
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Tao, Y., Y. Miao, Y. Aloimonos, N. Chopra, et al. 2023. Smart Sustainable Shellfish Aquaculture Management Program - Engineering. Smart Shellfish Aquaculture Summit. 2023, Kent Narrow Way, Grasonville, MD. Aug 23-24, 2023.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2024 Citation: Wu, Jiayi, Xiaomin Lin, Shahriar Negahdaripour, Cornelia Ferm�ller, and Yiannis Aloimonos. "MARVIS: Motion & Geometry Aware Real and Virtual Image Segmentation." In proceeding of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Lin, Xiaomin, Nitin J. Sanket, Nare Karapetyan, and Yiannis Aloimonos. "Oysternet: Enhanced oyster detection using simulation." In 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 5170-5176. IEEE, 2023.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2024 Citation: Hitesh. Kyatham, Michael Xu, Xiaomin. Lin, Shahriar Negahdaripour, Yiannis Aloimonos, Miao Yu. Maritime Operations Simulation Task (MOST). In proceeding of OCEANS 2024, Halifax. IEEE, 2024.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2024 Citation: Hitesh. Kyatham, Michael Xu, Xiaomin. Lin, Shahriar Negahdaripour, Yiannis Aloimonos, Miao Yu. Performance Assessment of Feature Detection Methods for 2-D FS Sonar Imagery. In proceeding of OCEANS 2024, Halifax. IEEE, 2024.
  • Type: Conference Papers and Presentations Status: Submitted Year Published: 2024 Citation: Tomer Atzili, Abhinav Bhamidipati, Yashveer Jain, William Wang Yang, Sri Kiran Kommaraju, Karthikeya Kona, Xiaomin Lin, Yantian Zha. AAM-SEALS: Developing Aerial-Aquatic Manipulators in SEa, Air, and Lands Simulator. submitted to (CORL 2024)
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2024 Citation: Lin, Xiaomin, Nare Karapetyan, Kaustubh Joshi, Tianchen Liu, Nikhil Chopra, Miao Yu, Pratap Tokekar, and Yiannis Aloimonos. "UIVNAV: Underwater Information-driven Vision-based Navigation via Imitation Learning." In proceeding of 2024 international conference on robotics and automation (ICRA 2024)
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2024 Citation: Karabatis, Yianni, Xiaomin Lin, Nitin J. Sanket, Michail G. Lagoudakis, and Yiannis Aloimonos. "Detecting Olives with Synthetic or Real Data? Olive the Above." In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4242-4249. IEEE, 2023.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Palnitkar, Aadi, Rashmi Kapu, Xiaomin Lin, Cheng Liu, Nare Karapetyan, and Yiannis Aloimonos. "Chatsim: Underwater simulation with natural language prompting." In OCEANS 2023-MTS/IEEE US Gulf Coast, pp. 1-7. IEEE, 2023.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Gaur, Akshaj, Cheng Liu, Xiaomin Lin, Nare Karapetyan, and Yiannis Aloimonos. "Whale detection enhancement through synthetic satellite images." In OCEANS 2023-MTS/IEEE US Gulf Coast, pp. 1-7. IEEE, 2023.
  • Type: Other Status: Accepted Year Published: 2023 Citation: Ojha, R., & van Senten, J. (2023, March 16). Smart Technology Adoption in Oyster Production: Economic Feasibility and Consumer Perceptions [Poster presentation]. CAIA Big Event, Virginia Tech, VA.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2024 Citation: Ojha, R., & van Senten, J. (2024, February 18-22). Ex-Ante Cost-Benefit Analysis of Smart Sustainable Shellfish Aquaculture Management (S3AM) Technology [Oral presentation]. Presented at Aquaculture America, San Antonio, Texas. Retrieved from https://wasblobstorage.blob.core.windows.net/meeting-abstracts/AA2024AbstractBook.pdf
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2024 Citation: Ojha, R., & van Senten, J. (2024). Assessing the Feasibility of Renting Smart Sustainable Shellfish Aquaculture Management (S3AM) Technology for Oyster Farms [Oral presentation]. Presented at the 116th National Shellfisheries Association Annual Meeting.
  • Type: Other Status: Published Year Published: 2023 Citation: Ojha, R., van Senten, J., & Parker, M. (2023). Cost Model for S3AM. S3AM Newsletter. Available at https://mailchi.mp/2156a64dae7e/s3am-newsletter-12607754?e=381c1c8d9c
  • Type: Other Status: Published Year Published: 2024 Citation: Ojha, R., & van Senten, J. (2024). Understanding Oyster Growers' Technology Needs: Insights from Maryland and Virginia. S3AM Newsletter. Available at https://us21.campaign-archive.com/?e=%5BUNIQID%5D&id=50337d9fe1&u=f2686d56f770fcd9c1dd4c029
  • Type: Other Status: Accepted Year Published: 2024 Citation: Ojha, R., van Senten, J., Pattillo, A. (2024). Is Technological Intervention Feasible for the US Shellfish Aquaculture Industry? A Case Study from Maryland Oyster Farms. World Aquaculture Magazine.
  • Type: Websites Status: Other Year Published: 2024 Citation: https://s3amoysters.umd.edu/
  • Type: Other Status: Published Year Published: 2023 Citation: Ojha, R., & van Senten, J. (2023, August 10). Economic Feasibility of S3AM Technology in Shellfish Farming. S3AM Webinar Series. Available https://www.youtube.com/watch?v=CplMOuzrfsk
  • Type: Other Status: Published Year Published: 2024 Citation: Magnusson, G. (2024, February). Precision Oyster Farm Seeding with FindMyOyster. S3AM Webinar Series. Available https://www.youtube.com/watch?v=m0s-wLwZDHQ
  • Type: Other Status: Published Year Published: 2023 Citation: Gray, M., K. Joshi, A. Williams., Chopra, N. (2023). Water Quality Monitoring and Environmental Sensing for Shellfish Aquaculture Emerging Technologies. S3AM Webinar Series. Available https://www.youtube.com/watch?v=dusisoVH_o8
  • Type: Conference Papers and Presentations Status: Other Year Published: 2022 Citation: New Research Paradigms in Robotics and AI, Zagreb, Croatia, June 2022. (Keynote Speaker)
  • Type: Conference Papers and Presentations Status: Other Year Published: 2022 Citation: CVPR 5th International Workshop on Visual Odometry & Computer Vision Applications Based on Location Clues, New Orleans, June 2022. (Keynote Speaker)
  • Type: Other Status: Other Year Published: 2024 Citation: Williams, A. and M. Gray. (2024). Monitoring Sediment Disturbance by a Remote Underwater Vehicle within an Oyster Lease. 2024 S3AM Summit & Expo, College Park, MD. August 15, 2024. (poster presentation)
  • Type: Other Status: Other Year Published: 2024 Citation: Wang, C., G. Nandhan, Y. Shen, W. Chen, S.S.S. Kumar, A. Long, A. Williams, G. Magnusson, D.A. Pattillo, D. Webster, M. Gray, M. Yu, Y. Tao. (2024). ShellCollect: A Framework for Smart Precision Shellfish Harvesting Using Data Collection Path Planning. 2024 S3AM Summit & Expo, College Park, MD. August 15, 2024. (poster presentation)
  • Type: Other Status: Other Year Published: 2024 Citation: Lin, X., N. Karapetyan, K. Joshi, T. Liu, N. Chopra, M. Yu, P. Tokekar, Y. Aloimonos. (2024). UIVNav: Underwater Information-Driven Vision-Based Navigation Via Imitation Learning. 2024 S3AM Summit & Expo, College Park, MD. August 15, 2024. (poster presentation)
  • Type: Other Status: Other Year Published: 2024 Citation: Magnusson, G. (2024). Find My Oyster: See Whats Hiding Under the Surface. 2024 S3AM Summit & Expo, College Park, MD. August 15, 2024. (poster presentation)
  • Type: Other Status: Other Year Published: 2024 Citation: Wu, J., X. Lin, S. Negahdaripour, C. Fermuller, Y. Aloimonos. (2024). MARVIS: Motion & Geometry Aware Real and Virtual Image Segmentation. 2024 S3AM Summit & Expo, College Park, MD. August 15, 2024. (poster presentation)
  • Type: Other Status: Other Year Published: 2024 Citation: Essandoh, J., N. Vukov, M. Straus, E. Lu. Y. Jin. (2024). Visual Identification of Oysters with Machine Learning. 2024 S3AM Summit & Expo, College Park, MD. August 15, 2024. (poster presentation)
  • Type: Other Status: Other Year Published: 2024 Citation: Ojha, R. and J. van Senten. (2024). What Oyster Growers in Maryland And Virginia Are Seeking from Technology. 2024 S3AM Summit & Expo, College Park, MD. August 15, 2024. (poster presentation)
  • Type: Other Status: Published Year Published: 2024 Citation: Pattillo, D.A. (2024). Winter 2024 S3AM Wise Newsletter. https://us21.campaign-archive.com/?e=%5BUNIQID%5D&id=50337d9fe1&u=f2686d56f770fcd9c1dd4c029
  • Type: Other Status: Other Year Published: 2024 Citation: Xu, M., N. Karapetyan, K. Rajasekaran, A. Williams, D.A. Pattillo, M. Gray, M. Yu. (2024). Sonar-Based Seabed Classification of Oyster Farms. 2024 S3AM Summit & Expo, College Park, MD. August 15, 2024. (poster presentation)
  • Type: Other Status: Other Year Published: 2024 Citation: Joshi, K. T. Liu, M. Gray, N. Chopra. (2024). Localization of Underwater Robot & Water Quality Mapping. 2024 S3AM Summit & Expo, College Park, MD. August 15, 2024. (poster presentation)
  • Type: Other Status: Published Year Published: 2023 Citation: Pattillo, D.A. (2023). Fall 2023 S3AM Wise Newsletter. https://us21.campaign-archive.com/?e=%5BUNIQID%5D&id=36fa30c4a4&u=f2686d56f770fcd9c1dd4c029
  • Type: Other Status: Published Year Published: 2024 Citation: Pattillo, D.A. (2024) Spring 2024 S3AM Wise Newsletter. https://mailchi.mp/2e8433e4ab00/s3am-newsletter-12693393
  • Type: Journal Articles Status: Under Review Year Published: 2024 Citation: Xu. M., N. Karapetyan, K. Rajasekaran, A. Williams, D.A. Pattillo, P. Tokekar, M. Gray, M. Yu. (Under Review). Machine-Learning Based Classification of On-Bottom Oyster Farms using a Mechanical Scanning Sonar. IEEE Sensors Journal.
  • Type: Journal Articles Status: Published Year Published: 2024 Citation: B. Ramdam, E. M. Abdelazim, H. -T. Kim, H. K. Fathy and M. Yu, "Dynamic Characterization of a Fast-Responding Nanophotonic Gas Sensor Using Optimization-Based System Identification," in IEEE Sensors Journal, vol. 24, no. 13, pp. 20777-20785, 1 July1, 2024, doi: 10.1109/JSEN.2024.3404247. keywords: {Sensors;Gas detectors;Sensor phenomena and characterization;Valves;Time factors;System identification;Sensor systems;Gas sensors;nanophotonics;sensor dynamics;system identification},
  • Type: Journal Articles Status: Published Year Published: 2024 Citation: Kim, Hyun-Tae, Bibek Ramdam, and Miao Yu. "Silicon ring resonator with ZIF-8/PDMS cladding for sensing dissolved CO2 gas in perfluorocarbon solutions." Sensors and Actuators B: Chemical 404 (2024): 135305.


Progress 09/01/22 to 08/31/23

Outputs
Target Audience:Target audiences include the following: 1. Oyster aquaculture industry (on-bottom oyster farmers, hatchery staff, equipment manufacturers, seafood processors, and investors) 2. National Aquaculture Extension Network 3. National Aquaculture Research Community 4. National Advisory Board members 5. Non-Government Organizations (Chesapeake Bay Foundation, Oyster Recovery Partnership, Maryland Watermen's Association, Louisiana Oyster Task Force, East Coast Shellfish Growers Association, Pacific Coast Shellfish Growers Association, Oyster South, National Aquaculture Association, Shellfish Growers of Virginia, United States Aquaculture Society, National Shellfisheries Association) 6. Project partners 7. Agricultural and environmental engineers 8. Underwater robotics research community 9. Computer Vision, robotics, controls, and automation researchers 10. Shellfish biology and environmental sciences community 11. Graduate and Undergraduate students (some from minority students from socially and economically disadvantaged regions) 12. 4-H members (age 9-18), youth and students interested in STEM careers, especially robotics and aquaculture (some from minority students from socially and economically disadvantaged regions) 13. UMD Extension audience/general public? Changes/Problems: Latentimpacts of COVID-19 that have impacted our projectincludestaffing shortages/turnover andsupply chain shortages. In particular, these issues have impacted our ability to purchase and acquire equipment needed for the project. Additionally, many of the suppliers of the cutting edge hardware technology are international companies, which has made their acquisition more difficult and the timeline longer than expected. Additionally, we have only recently been awarded S3AM project-specific collaborative space on the University of Maryland College Park main campus, which has made laboratory research discoveries more difficult to attain due to significant travel constraints among students. We expect that lab research findings will be more timely going forward. We have requested a budget reallocation to shift the funds allocated to boat rental fees in the amount of $112,000 toward funding the partial salary (0.33 FTE) of a communications specialist through the duration of the project. This individual maintains our project website, creates video content, and collaborates on the webinar series and written communication products. Our evaluation specialist at the start of the project moved to another university soon after funding. We identified another individual at UMD to provide external evaluation, which they had begun in spring and summer 2023. However, this individualresigned in August 2023and we are in the process of finding another partner to fill that role. To date, we have collected formative evaluation data internally, and will attempt to obtain the data collected by the previous external evaluator. Our Co-PD at Louisiana State University, Brian Callam, moved to another position and is no longer working with the S3AM project. We are in the process of moving this subcontract to another collaborator in the Gulf region. What opportunities for training and professional development has the project provided?Training: The project provided training and professional development for 4 postdoc researchers, 10 graduate students, and7 undergraduate students. The students and postdocs gained hands-on experience through working on sonar imaging devices, GPS devices, vision systems, AI/ML algorithms, underwater drone platform, econometric techniques, survey development, data analysis, and experiential learning in the oyster farm field. The project also provided training of 10 under represented undergraduate engineering students in Summer undergraduate research program and courses (ENEE 465Remote Sensing and Image Processing (Spring 2023); ENEE 440 Mechatronics (Spring 2023); ENEE 422 Introduction to Machinge Learning (Spring 2022). There were three S3AM project-specific summer research projects that enabled 7 undergraduate students to complete their capstone program. These research programs and courses enabled students to conduct research to explore emerging technologies to transform shellfish farming practices. Additionally, one of the UMES students was accepted into the Ph.D. program at UMD as A Clark Fellow by leveraging a Pathway Project. In summer 2023 three camps were conducted, with 215 youth attendingthe 4H ROV Summer Camp Pilot and34 youth attended summer programming at Grantsville and Swan Meadow Schools for a total of 249 youths in the4H ROV Summer Pilot program and 36 ROV's built. Developed curriculum around oyster biology and engineering concepts. Maryland State Fair robotics competition had 14 teams and about 70 4H members who did a project related to protecting Maryland waterways. S3AM webinar series wasestablished in August 2022 and is ongoingwith five webinars to date and acombined total of 220registered, 112attended virtually, 37 completing the post-webinar evaluation, and 182views on YouTube as of 8/28/2023.Post-webinar survey (n=37) indicated a 6% increase in interest (0.25/5) and 16% increase in knowledge (0.82/5) of the topics covered, based on a 5-point scale ranging from poor (1) to excellent (5). The program overview of the project was presented at local (n=3), regional (n=2), and national (n=2) conferences and meetings of the Maryland Aquaculture Coordinating Council. MD Watermen's Show, January 2023, Ocean City, MD -This conference included professionals from research, extension, non-government organizations, public agency, and oyster aquaculturists and covered topics including biofouling prevention, genetic selection, oyster hatcheries, and S3AMtechnology. The team provided various presentations, staffed a trade show booth featuring the S3AM ROV, and directly addressed farmer questions. Seed to $$$ Workshop, March 2023, Annapolis, MD -Thiswas an economics-focused workshop led by Matt Parker with participation from research, extension, non-government organizations, public agency, and oyster aquaculturists and covered topics including economics, regulations, farmer programs,and S3AM technology. The team provided various presentations, demonstrated the S3AM ROV,and directly addressed farmer questions. HACCP for Shellfish Shipper courses, April 13 and 27, 2023, Salisbury, MD - Thiswas a food safety-focused workshop led by Cathy Liu.The team provided various presentations, demonstrated the S3AM ROV,and directly addressed industryquestions. A number of tours to oyster hatcheries, farm locations, the Maryland seafood processing industry were organized. Professional Development Aquaculture America 2023, February 2023, New Orleans, LA - Participated in planning and execution of diversity and equity session and high school student immersion activities targeted at increasing diversity in the United States Aquaculture Society and ourup-and-coming generation of aquaculture professionals. How have the results been disseminated to communities of interest? Research findings were disseminated at various machine learning, computer vision, robotics, and aquaculture-focused conferences. Many of these presentations were converted to conference papers or peer-reviewed journal articles. Additionally, research findings were provided via the S3AM webinar series. Multiple undergraduate student courses and capstone projects, as well as summer research projects were conducted. The annual 4H summer camps and robotics competitions were conducted. Instructional videos and curriculum were developed and deployed online involving the DIY ROV build. Interstate Shellfish Sanitation Conference, September 2022, Ocean City, MD - presentations to academic and public agency personnel related to shellfish food safety. Maryland Shellfish Aquaculture Conference, November 2022, Annapolis, MD - presentations to oyster farmers, academic, public agency and non-government organizations related to oyster aquaculture in the Mid-Atlantic region. MD Watermen's Show, January 2023, Ocean City, MD -presentations to oyster farmers, academic, public agency and non-government organizations related to oyster aquaculture in the Mid-Atlantic region. Aquaculture America 2023, February 2023, New Orleans, LA -presentations to oyster farmers, academic, public agency and non-government organizations related to oyster aquaculture in the Mid-Atlantic region. Chaired a special session on robotics and technology in aquaculture. 115th National Shellfisheries Association Meeting, March 2023, Baltimore, MDpresentations to oyster farmers, academic, public agency and non-government organizations related to oyster aquaculture in the Mid-Atlantic region. Chaired a special session on robotics and technology in shellfish aquaculture. Seed to $$$ Workshop, March 2023, Annapolis, MD -presentations to oyster farmers, academic, public agency and non-government organizations related to oyster aquaculture in the Mid-Atlantic region. 2023 Smart Shellfish Aquaculture Summit, August 2023, Grasonville, MD - Summit meeting to update S3AM team and National Advisory Board members on the status of the S3AM project and conduct strategic planning for research and continued funding inthe coming year. A presentation was given at the National Science Foundation (NSF) to the First Lady - Dr. Jill Biden, Prime Minister of India - Narendra Modi and Director of NSF, Dr. Sethukumar Panchanathan about the activities of S3AM project. The ROV was also showcased at the NSF event. PSI has extensive existing collaborations with stakeholders, specifically U.S. west coast shellfish farmers. We utilize these collaborations to disseminate S3AM activities and opportunities. Engaged oyster farmers with numerous leases to allow field development trials for S3AM research personnel on several grounds. These included a lease near the UM Horn Point Oyster Hatchery for dive and drone assessment of planted oysters as well as leases used to develop both underwater drone capabilities for determining oyster populations and their health and growth as well as the independent surface vessel maneuvered by a remotely controlled unit. Provided information on project development through ongoing extension programs, tours and classes to include goals and objectives of the project as well as current state of research in creating tools to aid bottom culture of oysters. Incorporated S3AM demonstration and presentation into Sea Grant funded Seed to $$$ aquaculture business workshop. Participated in all virtual meetings of the National Advisory Board as well as biweekly virtual meetings of project personnel in engineering and extension. Provided input on project development and how industry needs would benefit from the developing technology. Provided ongoing electronic information to a suite of growers to keep them apprised of research development and delivered feedback from industry to project research staff. Article submitted to World Aquaculture magazine by G. Magnusson on the Find My Oyster software tool developed through the S3AM project for improving bottom culture of oysters to make farming more precise and profitable. Hosted four installments of the S3AM webinar series covering topics of computer vision, environmental water quality, undergraduate education, and aquaculture economics. Post-webinar evaluations indicate a general increase in interest and knowledge were measured. Provided information on S3AM in lecture on Bay Aquaculture provided to upper-level students at the University of Maryland in an annual lecture. Included handouts with photos and objectives of the project to develop oyster aquaculture. 48th East Coast Commercial Fishermen's and Aquaculture Trade Expo, Ocean City MD Convention Center, "Advancing Oyster Aquaculture with Robotics and Technology" in seminar program. Information also provided at the Mid Atlantic Aquaculture Extension booth on the show floor for the three days of the Expo. Chesapeake Agriculture Innovation Center Value-Added Summit, information provided to attendees at booth table to include the S3AM project information and current work. National Shellfisheries Association, Baltimore MD information provided through a presentation session organized for the S3AM information to the international audience attending, as well as material provide on the trade show booth. Presented "Sustainable Safe Seafood from Water to Plate" at the Society of Environmental Toxicology and Chemistry 2nd Annual Hudson-Delaware & Chesapeake-Potomac Regional Chapter Joint Spring Meeting.S3AM technology for oyster aquaculture was sharedduring the HACCP for Shellfish Shipper courses. Maintained the Maryland Aquaculture Facebook page to provide information on programs and activities that also include S3AM functions. Ongoing contact maintained with oyster culturists to engage them in allowing the use of their leased grounds for project research staff to test new systems in real world conditions and get input from growers on the actual conditions they contend with in managing their grounds. Provided information electronically to growers on the state of the project and the goals sought to benefit profitable production of oysters using bottom culture methods. Developed and published an ongoing quarterlyonline newsletter called "SAM Wise". Provided interviews and materials for on article entitled "Eyes on the Prize Catch" in Chesapeake Quarterly Magazine.https://storymaps.arcgis.com/stories/01d793ce0dba439787f21f9ef7e92749 Maryland Shellfish Aquaculture Conference, November 2022, Annapolis, MD - This conference included professionals from research, extension, non-government organizations, public agency, and oyster aquaculturists and covered topics including economics, regulations, oyster hatcheries, and cutting edge technology. The team provided various presentations, hosted panel discussions, and directly addressed farmer questions. What do you plan to do during the next reporting period to accomplish the goals?Goal 1: · We plan to integrate the Automated Surface Vehicle (ASV) into our tethered underwater remotely operated vehicle (ROV) research platform. This will improve our localization capabilities and flexibility in the field. · We plan to integrate a more farmer-accessible method of data collection, in which the sonar unit is mounted onto their farm vessels for manual data collection. This method is anticipated to generate greater farmer involvement and adoption. · The localization of the ROV will be further improved by using the optimization based moving horizon estimation (MHE) framework approach, thereby allowing for the development of precise and accurate oyster density maps. · With regard to control for underwater vehicles and surface vehicles, the goal for next year will be to develop efficient model predictive control algorithms that can manage model uncertainties and environmental disturbances. These controllers will then be used for robust and accurate navigation of the ROV. · For underwater localization, the newly developed optimization techniques will be deployed on the ROV to localize the ROV in real-time. · We will incorporate the dredge capacity and the dredge lift up time point in our smart harvesting algorithm. · We plan to integrate the oyster segmentation software with the navigation software, so that we can obtain a map of the sea bottom painted with oyster density. · We plan to combine a sonar sensor with a stereo visual system in order to improve the visibility in the visual images. This would be accomplished by utilizing the sonar measurements to estimate the scene depth and use this information to remove the backscattering effects. We expect this will not only enhance our oyster detection, but it will become a new tool for underwater robotics where there is bad visibility. Goal 2: We plan to conduct more robust environmental water quality assessments with better spatial and depth coverage to investigate the impact of dredging and the potential benefits of precision harvesting. We will collaborate with Horn Point Lab (HPL) Physical Oceanographer Dr. Joe Jurisa who has expertise in sediment plume dynamics using his high-performance acoustic doppler current profiler for tracking plumes (https://www.nortekgroup.com/products/signature-1000). This ADCP enables us to 'see' through the entire water column and track suspended sediments while the sensor sits near the water surface facing downward. · We will cooperate with oyster lease holders to test our live harvesting path record and display system, and collect some real harvesting paths. Goal 3: The economics team plans to continue refining the farm economic models. Once the farm production models have been finalized, the team will develop the "S3AM as a service" model. The team will continue to conduct interviews with producers in Virginia and Maryland; with the intent of wrapping up that activity by the conclusion of 2023. The team will develop attributes and attribute levels for the consumer choice experiment and finalize development of the survey instrument over the course of the next reporting cycle. Focus groups may or may not occur within the next reporting period, depending on when IRB approval of survey instruments is obtained. Goal 4: We will continue to produce curriculum and provide educational opportunities for both undergraduate students and 4H youth, and encourage them to participate in local, regional, and national events to showcase their learning. 4H Summer camps and robotics competitions will be held again during the summer. Goal 5: We will continue to engage the oyster aquaculture industry personnel. We plan to hold additional events to disseminate the research progress of the S3AM project to oyster farmers, private industry, public agency, academics, and non-government organizations. We will organize research presentation sessions in the 2024 Maryland Watermen's show, Aquaculture America 2024, and the 116th National Shellfisheries Association meeting. We will continue to hold the periodic webinars to discuss S3AM research and related projects. Additionally, we will hold our annual S3AM Summit meeting to share project updates with our National Advisory Board and plan the year's research, teaching, and extension goals.

Impacts
What was accomplished under these goals? Goal 1: Enhanced oyster detection by implementing new "image translation" and "style transfer" techniques from the field of Hyper-Dimensional Computing. Developed a new technique for underwater navigation based on imitation learning where first a human diver drives the underwater drone for the particular task under consideration. Deployed a new version of Android and IOS mobile app, which enables data collection in the field, and enabled integration with FarmOS. Data recorded in the app is automatically uploaded to the FarmOS dashboard. Published a Python library, which enables researchers to access raw data collected by the app and upload data that can be used by the app. Developed an accurate and efficient approach for nonlinear state estimation of a general autonomous system using an iterative preconditioning technique to accelerate the optimization step in the moving horizon estimation (MHE) framework that outperform other commonly used estimators and can be deployed for underwater and surface vehicles. Designed and built an autonomous surface vehicle (ASV) and a script was developed to control it in open water using feedback from GPS and compass headings. The ASV can navigate to locations specified by GPS coordinates. Developed a script for autonomous underwater navigation using Python and ROS (Robot Operating System) to give control commands to robots, enabling the user to give the robot precise control commands and reducing dependency on manual control and increasing accuracy and customization of navigation trajectories. Successfully integrated a Doppler Velocity Logger (DVL) on the ROV for real-time speed estimates of the ROV enabling more accurate sensor data for localization when fused with IMU measurements. Developed the ROV localization algorithms and tested them in simulations and lab experiments. Results were reasonable to determine the position of the ROV from IMU and control data when compared against the DVL as ground truth. The ROV was tested to follow specific patterns (squares, lawn mower pattern) developed and tested using Python and ROS scripts. Built an oyster density distribution map simulator to train our dredging path planning algorithms to determine the shortest path without overlapping and the minimum number of turns required by the harvesting vessel while considering its feasible turning radius. Built a system for recording and displaying the live harvesting path, which includes an external high accuracy RTX GPS (<1 ft) and live recording/displaying software. The software allows the users to load and display a target path to follow, display current position, and live record the current moving path. Goal 2: Generated environmental water quality data from stations across Washington state related to ocean acidification and temperature measurements relating to climate change and ecological regime shifts. Data was cleaned and uploaded to the publicly available NANOOS Visual System and synced with NOAA tidal height data to generate summary statistics on annual, seasonal, lunar, and tidal timescales. Developed methods of detecting and tracking sediment plumes, a fundamental requirement for quantitatively estimating the water quality impacts of traditional dredging and how these impacts compare to S3AM technology. Dredging was simulated by dragging an object behind a boat in an active lease adjacent to HPL and immediately deploying sensors to track the plume over space and time. Created environmentally analogous sand and shell substrate mesocosms in the lab for testing equipment and sensing techniques. Field tests were also conducted on an active oyster lease to test a variety of equipment under natural conditions. Collected and shared underwater video and aerial drone imagery to facilitate automation and AI programming. Developed proof of concept workflows for processing and analyzing drone aerial imagery of intertidal shellfish farms. Developed a process workflow for analyzing drone aerial imagery of intertidal shellfish farms that can be applied at farm-scale to optimize shellfish production, siting, and management within native eelgrass habitat. Goal 3: Developed a preliminary farm adoption model at 3 different scales of production for the S3AM technology. Preliminary models show that S3AM adoption is likely unprofitable at <200 bushels/yr but demonstrated positive net returns at >6,000 bushels/yr. Continued development of a review paper regarding technology adoption in the U.S. shellfish industry; in conjunction with farmer interviews to assess perceptions and preferences for technology adoption. Developed a consumer choice experiment to assess willingness to pay for oysters produced with smart technologies like S3AM. Presented a student economics research poster at the Virginia Tech "CAIA Big Event" and gave a webinar to share current modeling work findings. Goal 4: Developed new course/course modules for the following: "ENEE/ENCE 465 Remote Sensing and Image Processing" (Spring 2023) covered remote sensing platforms and methodologies, underwater imaging, and image processing, and machine learning technologies. "ENME/ENAE 440 Mechatronics" (Spring 2023) covered physical and mathematical modeling of mechanical, electrical, mechatronic systems, sensors and electronic measurement, feedback control algorithms, and mechatronic device applications. Four students were enrolled, most were minorities. Provided three projects for five UMES students taking "Senior Project Design" (ENGE 467/477): Leveraged existing NSF REU summer program to host "Machine Learning and Computer Vision Techniques to Identify and Monitor Oyster Nondisruptive" at Salisbury University. Three students conducted research and reported weekly activities. Developed curriculum, educational aids, competitions, and summer camps for teaching robotics and oyster aquaculture concepts to youth for STEAM exposure, engagement, and career track preparation. Products include electronic course curriculum, videos, oyster anatomy puzzles and interactive software, robotics competitions, and a do-it-yourself underwater robot platform with instructional construction video. Held the annual4-H Robotics Challenge at the Maryland State Fair.The theme this year was Enjoying our Waterways and each team (14 teams, ~70 4H members)conducted a service projectdealingwith protecting our waterways. Goal 5: Engaged with the oyster aquaculture industry and research community about S3AM activities, providing multiple workshops, conferences, symposia, site visits, interviews, and general oyster aquaculture information. Engaged one commercial shellfish farmer in the FindMyOyster app to field test the software interface and data collection process, providing critical user feedback. We experimented with an advertising campaign to reach new potential FindMyOyster app users.

Publications

  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: A. Palnitkar, R. Kapu, X.Lin, C. Liu, N.Karapetyan, Y. Aloimonos, ChatSim: Underwater Simulation with ChatGPT. In the proceeding of OCEANS 2023, Gulf Coast, IEEE, 2023.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: A. Gaur, C. Liu, X. Lin, N. Karapetyan, and Y. Aloimonos, SeaDroneSim 2.0: Simulation of Aerial Images for Detection of Maritime Objects. In the proceeding of OCEANS 2023 Gulf Coast, IEEE, 2023.
  • Type: Journal Articles Status: Submitted Year Published: 2023 Citation: X. Lin, A. Gaur, C. Liu, C. Singh, N. Karapetyan, and Y. Aloimonos, Simulating Aerial and Satellite Images for Detection of Maritime Objects, submitted to 2023 RA-M, Special Issue of Marine Robotics, IEEE, 2023
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Jin, Y. Image Processing and Computer Vision Algorithms for Sustainable Shellfish Farming at the 115th Annual Meeting of the National Shellfish Association, Baltimore, MD, March 27th, 2023. (poster)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Magnusson, G. Find My Oyster  A software tool for improved bottom culture shellfish farming. Aquaculture America 2023. New Orleans, Louisiana. February 23, 2023. (poster)
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: T. Liu, K. Chakrabarti, N. Chopra, Iteratively Preconditioned Gradient-Descent Approach for Moving Horizon Estimation Problems, the 62nd IEEE Conference on Decision and Control (CDC 2023).
  • Type: Other Status: Published Year Published: 2023 Citation: T. Liu, K. Chakrabarti, N. Chopra, Iteratively Preconditioned Gradient-Descent Approach for Moving Horizon Estimation Problems, MRC Research Symposium 2023. (poster presentation)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: T. Liu, K. Chakrabarti, N. Chopra, Accelerating the Iteratively Preconditioned Gradient-Descent Algorithm using Momentum, the Ninth Indian Control Conference (ICC-9).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Michael Xu, K. Rajasekaran, A. Williams, D.A. Pattillo, M. Gray, M. Yu. Seabed Classification of Oyster Farms Using a Single Beam Scanning Sonar with Machine Learning. 115th Meeting of the National Shellfisheries Association. Baltimore, MD, USA. March 27, 2023. (Oral Presentation).
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: L. Zhao, H. Kim, and M. Yu, Structural Luneburg lens for broadband ultralong subwavelength focusing, Mechanical Systems and Signal Processing, 182, p.109561, 2023
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: L. Zhao, C. Bi, and M. Yu, Structural lens for broadband triple focusing and three-beam splitting of flexural waves, International Journal of Mechanical Sciences, 240, 107907, 2023
  • Type: Websites Status: Published Year Published: 2022 Citation: www.S3AMoysters.com
  • Type: Other Status: Published Year Published: 2022 Citation: Gudjon Magnusson. Find My Oyster: Farm management application for your bottom culture shellfish farm. 2022 S3AM Summit, Doubletree Hotel, Annapolis, MD, United States. (September 12, 2022 - September 14, 2022). (poster)
  • Type: Other Status: Published Year Published: 2022 Citation: Tianchen Liu, Miao Yu, Nikhil Chopra. Invariant Extended Kalman Filter for Underwater Localization. 2022 S3AM Summit, Doubletree Hotel, Annapolis, MD, United States. (September 12, 2022 - September 14, 2022). (poster)
  • Type: Other Status: Published Year Published: 2022 Citation: Pattillo, D.A. and M. Yu. Transforming Shellfish Farming with Smart Technology and Management Practices for Sustainable Production (Poster). 2022 S3AM Summit, Doubletree Hotel, Annapolis, MD, United States. (September 12, 2022 - September 14, 2022). (poster)
  • Type: Other Status: Published Year Published: 2022 Citation: Tianchen Liu, Miao Yu, Nikhil Chopra. Learning-based Autonomous Underwater Vehicle Navigation Following Human Actions in Confined Environment. 2022 S3AM Summit, Doubletree Hotel, Annapolis, MD, United States. (September 12, 2022 - September 14, 2022). (poster)
  • Type: Other Status: Published Year Published: 2022 Citation: Joshua Comfort and Ian Rudy. Image Processing and Computer Vision Algorithms for Sustainable Shellfish Farming. 2022 S3AM Summit, Doubletree Hotel, Annapolis, MD, United States. (September 12, 2022 - September 14, 2022). (poster)
  • Type: Other Status: Published Year Published: 2022 Citation: Xiaomin Lin, Nitin J. Sanket, Nare Karapetyan, Yiannis Aloimonos. Where is my Oyster: Enhancing Oyster Detection through Shape Modeling. 2022 S3AM Summit, Doubletree Hotel, Annapolis, MD, United States. (September 12, 2022 - September 14, 2022). (poster)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Williams A, Gray M. Monitoring Water Quality Impact of Remote Underwater Vehicle Use Within an Oyster Lease, Aquaculture America 2023. New Orleans, Louisiana. February 23, 2023. (poster)
  • Type: Other Status: Published Year Published: 2022 Citation: Alan Williams and Matt Gray. Mitigating Water Quality Impact of Shellfish Aquaculture with Remote Underwater Vehicles. 2022 S3AM Summit, Doubletree Hotel, Annapolis, MD, United States. (September 12, 2022 - September 14, 2022). (Poster)
  • Type: Other Status: Published Year Published: 2022 Citation: Chiao-Yi Wang, Wei-Yu Chen, Ravidu P. Hevaganinge, Miao Yu, Yang Tao. Simulation Model of Underwater Oyster Precision Harvesting with Path Planning Techniques. 2022 S3AM Summit, Doubletree Hotel, Annapolis, MD, United States. (September 12, 2022 - September 14, 2022). (poster)
  • Type: Other Status: Published Year Published: 2022 Citation: Yiannis Aloimonos, Nikhil Chopra, Matthew Gray, Xiaomin Lin, Tianchen Liu, Keshav Rajasekaran, Behzad Sadrfaridpour, Talita da Silva, Yang Tao, Chiao-Yi Wang, Alan Williams, Miao Yu. S3AM Progress: Monitoring and Harvesting Technologies. 2022 S3AM Summit, Doubletree Hotel, Annapolis, MD, United States. (September 12, 2022 - September 14, 2022). (poster)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Yiannis Aloimonos, Nikhil Chopra, Matthew Gray, Xiaomin Lin, Tianchen Liu, Keshav Rajasekaran, Behzad Sadrfaridpour, Talita da Silva, Yang Tao, Chiao-Yi Wang, Alan Williams, Miao Yu. S3AM Progress: Monitoring and Harvesting Technologies. USDA SAS/CAP 2022 Meeting, Kansas City, MO, April 18-20, 2022 (poster)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Pattillo, D.A. and Miao Yu. Transforming Shellfish Farming with Smart Technology and Management Practices for Sustainable Production. USDA SAS/CAP 2022 Meeting, Kansas City, MO, April 18-20, 2022 (poster)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Liu, C. C., Pattillo, A., Webster, D, Parker, M. Capacity Building for Future Smart Sustainable Shellfish Aquaculture Management. The 21ST IUFOST World Congress of Food Science and Technology. (Poster), Singapore. (October 2022 - November 2022). (poster)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: T. Liu, Miao Yu, N. Chopra, Underwater Localization Using Invariant Extended Kalman Filtering, MRC Research Symposium 2022. (poster)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Lin X, Jha N, Joshi M, Karapetyan N, Aloimonos Y, Yu M. OysterSim: Underwater Simulation for Enhancing Oyster Reef Monitoring. OCEANS 2022, Hampton Roads. 2022 Oct 17:1-6.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Lin X, Liu C, Pattillo A, Yu M, Aloimonos Y. SeaDroneSim: Simulation of Aerial Images for Detection of Objects Above Water. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision 2023 (pp. 216-223). https://doi.org/10.48550/arXiv.2210.16107
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Lin X, Sanket NJ, Karapetyan N, Aloimonos Y. Oysternet: Enhanced oyster detection using simulation. Accepted to 2023 IEEE International Conference on Robotics and Automation (ICRA) https://doi.org/10.48550/arXiv.2209.08176
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: NJS Levi Burner, C Ferm�ller, Y Aloimonos, Fast Active Monocular Distance Estimation from Time-to-Contact, 2023 IEEE International Conference on Robotics and Automation (ICRA)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: CD Singh, R Kumari, C Ferm�ller, NJ Sanket, Y Aloimonos, WorldGen: A Large Scale Generative Simulator , 2023 IEEE International Conference on Robotics and Automation (ICRA), 9147-9154.
  • Type: Conference Papers and Presentations Status: Submitted Year Published: 2024 Citation: X. Lin*, N.Karapetyan*, P. Tokekar, Y. Aloimonos, UIVNAV: Underwater Information-driven Vision-based Navigation via Imitation Learning, submitted to 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Y. Karabatis, X.Lin, N. Sanket, M. Lagoudakis, Y. Aloimonos Detecting Olives with Synthetic or Real Data? Olive the Above. In the proceeding of IEEE/RSJ international conference on intelligent robots and systems (IROS), Detroit, IEEE, 2023.


Progress 09/01/21 to 08/31/22

Outputs
Target Audience:Target Audiences Oyster aquaculture industry (on-bottom oyster farmers, hatchery staff, equipment manufactureres,seafood processors, and investors) National Aquaculture Extension Network National Aquaculture Research Community National Advisory Board members Non-Government Organizations (Chesapeake Bay Foundation, Oyster Recovery Partnership,Louisiana Oyster Task Force, East Coast Shellfish Growers Assocation, Pacific Coast Shellfish Growers Association, Oyster South, National Aquaculture Association, Shellfish Growers of Virginia) Project partners Agricultural and environmental engineers Underwater robotics research community Computer Vision, robotics, controls, andautomation researchers Shellfish biology and environmental sciences community Graduate and Undergraduate students (some from minority students from socially and economically disadvantaged regions) 4-H members (age 9-18), youth and students interested in STEM careers, especially robotics and aquaculture(some from minority students from socially andeconomically disadvantaged regions) UMD Extension audience/general public Efforts Engaging with oyster farmers and related stakeholders (one-on-one, field days/tours, on-farm data collection) Undergraduat/Graduate student education (new course curriculum development, classroom and lab teaching, Research Experience for Undergraduates (REU)program) 4-H youth STEM challenges/activities (Lego challenge, robotics competition, DIY ROV build) Website and video content development (S3AMoysters.com) S3AM webinar series Quarterly National Advisory Board meetings Extension and research presentations at national conferences Changes/Problems: Experimental studies were delayed due to the COVID pandemic. Additionally, effects of COVID pandemic including supply chain/international trade issues, job market conditions, and administrative staff turnover were major impediments to the progress of the project, particularly for obtaining critical project equipment (e.g., Oculus multibeam sonar unit, advanced underwater ROV) and hiring high quality post-docs (e.g., job market competition and compensation) to conduct research activities. Due to changes in ownership of critical research vessels, we encountered difficulty in finding a contractor with an adequate boat to charter. There have been some issues with the camera of the ROV located in the Horn Point Laboratory at UMCES, which has delayed several different experiments and altered how specific experiments were conducted. Our evaluation specialist named on the project changed jobs during the first year of funding. We have finally hired someone to fill that position. We plan to reallocated some of funds to hire our communications specialist to help with website, video, audio, photography, and written communications for this project. What opportunities for training and professional development has the project provided? The project provided training and professional development for three postdoc researchers,11 graduate students, and 9 undergraduate students. The students and postdocs gained hands-on experience through working on sonar imaging devices, GPS devices, vision systems, AI/ML algorithms, underwater drone platform, econometric techniques, survey development, data analysis, and experiential learning in the oyster farm field. The project also provided training of undergraduate students in Summer undergraduate research program and courses (ENEE 422 Introduction to Machine Learning, ENEE 452 Artificial Intelligence,Student entrepreneurship in capstone design -ENGE 476/477). These research program and courses enabled students to conduct research to explore emerging technologies to transform shellfish farming practices. The project provided training of K-12 students through participation in 4H virtual STEM camp (80 participants), 4H LEGO robotics challenge (40 participants), and 4H Robotics Engineering Challenge (30 participants). The 2022 4-H STEM symposium also provided a booth display of theDIY ROV to train others in 4-H. Through the project, a professional development field trip to the University of Delaware oyster hatchery was organized during the 2022 Mid-Atlantic Sea Grant meeting allowing for 10 aquaculture extension specialists to learn and discuss plans for incorporating technology into the oyster hatchery renovation plans. S3AM webinar series were established in August 2022. There were 27 registered participants, 18 real-time viewers, and 30 views on YouTube as of 8/22/2022. Post-webinar survey (n=7) indicatedinterest scores increased from 4.0/5 to 4.43/5(9% increaseon average). Knowledge increased by 17% on average (knowledge scores increasing from 2.29/5 to 3.14/5). The program overview of the project was presented at a number of national conferences and meetings of the Maryland Aquaculture Coordinating Council. Tours to oyster hatcheries, farm locations, the Maryland seafood processing industry were organized (n = 9) How have the results been disseminated to communities of interest? The research results have been disseminated to the scientific communities through conference paper/presentation and journal publications. The research results were also disseminated to Gulf of Mexico farmers in the form of general program progress updates indicating that environmental conditions (and which specific parameters) are being monitored for technology development. Magazine and online articles were developed based on multiple 4-H events. The extension team connected researchers and industry personnel through farm visits, conferences, webinars, and one-on-one interactions. Project information and development were disseminated at various meetings and conferences: quarterly meetings of the Board of the Chesapeake Agriculture Innovation Center; quarterly meetings of Sea Ahead/Blue Tech Maryland development project; bi-monthly meetings of the Maryland Aquaculture Coordinating Council; and Board of Directors meetings for the Harry R. Hughes Center for Agro-Ecology. The S3AM webinar series were established to disseminate the project related knowledge and information to dozens of robotics and computer vision engineers. Educational programs were provided at the 47th Annual East Coast Commercial Fisherman's and Aquaculture Trade Expo and the semi-annual Chesapeake Oyster Alliance meeting. The website of the UM College of Agriculture and Natural Resources was redesigned to include a new section for this Project. The materials related to the project development were distributed timely through the website. Program updates were provided to industry members and advisory board members during advisory board meetings. What do you plan to do during the next reporting period to accomplish the goals?Goal 1 For ROV based monitoring, we plan to develop a fully autonomous navigation system for underwater vehicles that can operate without failure under common scenarios and develop a complete SLAM system for the oyster farms.We also plan to complete the field tests of sonar imaging systems for classification ofsubstrate types and oyster populations. In addition, we will optimize our new oyster segmentation technique for oyster detection and integrate the oyster segmentation software with the navigation software. This will enable us to obtain a map of the sea bottom pwith oyster density information. For smart harvesting, we plan to incorporate turning angle in our path planning algorithm. Field tests for boat and dredger localization will be pursued after the localization method is fully developed. For software development, we will continue the app development and based on the experience and feedback from the farmers, we will work on improve the app. Goal 2 We plan tocontinue environmental monitoring and data analysis to ensure technology development proceeding with all relevant information. Goal 3 We are currently in the process of quantifying the costs of the S3AM system. In addition, the team at Virginia Tech is developing a new proposal to assess consumer preferences and willingness to pay for oysters that are harvested with improved technology (such as the S3AM system). The target sponsor for that proposal submission is Virginia Sea Grant under their Aquaculture Fellowship program; in the fall of 2022. Virginia Tech is also engaged in developing a review of technology adoption in the oyster industry, which will be developed into a manuscript for publication. Goal 4 We plan to offera number of courses related to the project, including a "Machine Learning" class to senior engineering studentsand "Artificial Intelligence" class; two sessions of "Senior Project Design" to seniors with two projects that are specifically tied with the project. We will continue to offer a summer research program in machine learning for shellfish farming. The 4-H team will continue organize the proposed 4-H activities and will put together 100 DIY ROV kits for use with 4-H programming across Maryland. Goal 5 We plan to disseminate the project progress to the oyster aquaculture community at the following state, regional, and national meetings: 72nd Interstate Seafood Seminar, Ocean City, MD, September 7-9, 2022 2022 S3AM Summit, Annapolis, MD, September 12-14, 2022 2022 Pacific Coast Shellfish Growers Association Conference, Wenatchee, WA, September 20-22, 2022 2022 Maryland Aquaculture Conference, Ocean City, MD, November 15, 2022 Aquaculture America 2024, New Orleans, LA, February 23-26, 2023 Chairing special session on Technology in Aquaculture Will organize oyster tour of LSU partner facility in Grand Isle, LA 115th National Shellfisheries Association Annual Meeting, Baltimore, MD, March 26-30, 2023 Hosting special session on Robotics in Shellfish Aquaculture We plan to develop a promotional video or series of videos to provide some background and justification for the project and promote the use of the technology being developed. Annual update videos on the project success were be developed. We will continue S3AM webinar series with 4-6 presentations annually. We will continue organizing aquaculture seminar (series) and tour with seafood industry stakeholders (consumers, producers, processors, value-added, etc. We plan to continue engaging regional industry members on program progress and generating interest in field testing participants.

Impacts
What was accomplished under these goals? Goal 1 Localization and navigation ROV Developed accurate and robust filter-based algorithm for underwater localization with standard marine sensors. Developed learning-based navigation algorithm for underwater drone autonomous navigation through complicated scenes. Smart harvesting Built oyster distribution map simulator and developed path planning algorithm for generating better dredging paths. Constructed laboratory system for boat and dredger localization. Monitoring Investigated sonar imaging based commercial devices as well as acoustic metamaterial enhanced sonar localization. Developed sonar imaging algorithm for oyster farm substrate classification and oyster population classification. Conducted field trials of sonar imaging and optical imaging in Chesapeake Bay oyster farms Created synthetic images of the sea bottom and oyster shells, and improved image segmentation abilities. Software development Deployed Android and IOS mobile app which is a part of the farm management system. Published a webpage for the farm management system Conducted field trial of the farm management mobile app at an oyster lease in Chesapeake Bay. Goal 2 Continued environmental data collection for farming areas in the Pacific Northwest, Gulf of Mexico, and Chesapeake Bay. Completed preliminary steps towards editing and packaging the water quality data from raw to more accessible formats. Calculated basic statistics for key water quality parameters at the Lummi Fish Hatchery from 2015 to early 2021 to find trends and relationships among variables, including the daily predicted NOAA tidal elevation. Interacted with oyster farmers at industry meetings, small groups, and one-on-one (n = 12) meetings where updates on program progress and eventual field trials were relayed. Goal 3 Developed baseline economic model for bottom culture oyster farms. Developed framework for assessment of capital and operating costs for S3AM technology. Communicated with other research partners to gather data on S3AM technology costs. Goal 4 Outreach activities: Conducted 4H virtual STEM camp (80 participants), 4H LEGO robotics challenge (40 participants), and 4H Robotics Engineering Challenge - Shellfish Theme (30 participants). Developed and tested DIY ROV (10 test kits created). Summer undergraduate research (REU) program: A total of 6 students funded for machine learning and artificial intelligence for underwater oyster farming applications Curriculum development: Developed ENEE 422 Introduction to Machine Learning and ENEE 452 Artificial Intelligence (Both are senior level electrical and computer engineering specialization courses). Student entrepreneurship in capstone design: Offered projects on: a) underwater acoustic positioning system to real-time position an underwater drone, b) propelled binary floating platform underwater acoustic positioning system, c) oyster activity detection system using Machine Learning and Artificial Intelligence. Goal 5 Developed project website (www.S3AM oysters.com) Established the project National Advisory Board and conducted quarterly meetings. Provided growers with information about research progress and relayed farmer feedback to researchers. Conducted 24 oyster farmer and stakeholder interview to gauge farmer interest in the S3AM technology and software. Disseminated the project development at various meetings: 47th Annual East Coast Commercial Fisherman's and Aquaculture Trade Expo, quarterly meetings of the Board of the Chesapeake Agriculture Innovation Center, quarterly meetings of Sea Ahead/Blue Tech Maryland development project, bi-monthly meetings of the Maryland Aquaculture Coordinating Council, semi-annual Chesapeake Oyster Alliance meeting, and Board of Directors for the Harry R. Hughes Center for Agro-Ecology. Delivered guest lecturers for Animal Science students and in the Introduction to Aquaculture class at the University of Maryland. Organized and conducted tours to oyster hatcheries, oyster farms, and Maryland seafood processing industry. Developed publicizing/communicating materials for the oyster aquaculture themed 4H robotics competition and the 2022 Maryland State Fair

Publications

  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2022 Citation: Pattillo, D.A., D. Webster, and M. Parker. Developing Technology for Oyster Aquaculture. 2022 Maryland Watermans Show. January 15, 2022.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2022 Citation: Pattillo, D.A., M. Yu, M. Parker, D. Webster, C. Liu, Y. Tao, Y. Aloimonos, N. Chopra, M. Gray, B. Callam, B. Hudson, and J. van Senten. Smart, Sustainable Shellfish Aquaculture Management Program. Aquaculture 2022, San Diego, CA, March 4, 2022
  • Type: Other Status: Other Year Published: 2022 Citation: Pattillo, D.A. Smart, Sustainable Shellfish Aquaculture Management Program. 2022 Fraunhofer, USA Brown Bag: Shellfish Sustainability. April 27, 2022.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2022 Citation: Pattillo, D.A., M.Yu, M. Parker, D. Webster, C. Liu, Y. Tao, Y. Aloimonos, N. Chopra, M. Gray, B. Callam, B.Hudson, and J. van Senten. Smart, Sustainable Shellfish Aquaculture Management Program. 2022 National Aquaculture Extension Conference, Portland, ME, June 13-17, 2022
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2022 Citation: Pattillo, D.A. Development of Smart Sensor Networks for Oyster Aquaculture. National Science Foundation Convergence Accelerator 2022 Expo. July 28, 2022.
  • Type: Other Status: Accepted Year Published: 2022 Citation: Yiannis Aloimonos, Nikhil Chopra, Matthew Gray, XiaominLin, Tianchen Liu, Keshav Rajasekaran, Behzad Sadrfaridpour, Talita da Silva, Yang Tao, Chiao-Yi Wang, Alan Williams, Miao Yu. S3AM Progress: Monitoring and Harvesting Technologies. USDA SAS/CAP 2022 Meeting, Kansas City, MO, April 18-20, 2022
  • Type: Other Status: Accepted Year Published: 2022 Citation: Pattillo, D.A. and Miao Yu. Transforming Shellfish Farming with Smart Technology and Management Practices for Sustainable Production. USDA SAS/CAP 2022 Meeting, Kansas City, MO, April 18-20, 2022
  • Type: Other Status: Accepted Year Published: 2022 Citation: Webster, D., A. Pattillo and M. Parker. Advancing Oyster Aquaculture For The 21st Century. 33 slide ppt. 47th Annual East Coast Commercial Fishermans and Aquaculture Trade Expo 15 Jan 22; Chesapeake Oyster Alliance 02 June 22
  • Type: Other Status: Submitted Year Published: 2022 Citation: Liu, C. 2022 IUFoST World Food Congress. The title of the abstract is: CAPACITY BUILDING FOR FUTURE SMART SUSTAINABLE SHELLFISH AQUACULTURE MANAGEMENT.
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: K. Chakrabarti, N. Gupta and N. Chopra, "On Preconditioning of Decentralized Gradient-Descent When Solving a System of Linear Equations," in IEEE Transactions on Control of Network Systems, vol. 9, no. 2, pp. 811-822, June 2022, doi: 10.1109/TCNS.2022.3165089.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2022 Citation: T. Liu, Miao Yu, N. Chopra, Learning-based Autonomous Underwater Vehicle Navigation following Human Actions in Confined Environment, IEEE OCEANS Conference 2022.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2022 Citation: T. Liu, Miao Yu, N. Chopra, Underwater Localization Using Invariant Extended Kalman Filtering, MRC Research Symposium 2022. (poster)
  • Type: Conference Papers and Presentations Status: Other Year Published: 2022 Citation: Chiao-Yi Wang, Wei-Yu Chen, Ravidu Parakrama Hevaganinge, Hassaan Mastoor, Yang Tao, Simulation Model of Underwater Oyster Precision Harvesting with Path Planning Techniques. North Atlantic Agriculture and Biological Engineering Conference (NABEC) 2022 (oral presentation).
  • Type: Conference Papers and Presentations Status: Other Year Published: 2021 Citation: Tao, Y. 2021.Application of Imaging and Vision-Guided Intelligence in Developing Automated Oyster Sorting and Crabmeat Picking Systems. 2021 Atlantic and Gulf Seafood Technology Conference. June 22, 2021. (Invited Panel Speaker).
  • Type: Conference Papers and Presentations Status: Other Year Published: 2022 Citation: Tao, Y. 2022. Machine Vision-Guided Robotic Automation for Smart Manufacturing - including Oysters. 2022 International Workshop on Applied Computing in Agriculture (AgriApp 2022). March 4-5, 2022. Invited distinguished speaker.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2022 Citation: Tao, Y. 2022. AI and Imaging-Guided Smart food Processing Lines  including oyster farming and processing. ASABE Annual Meeting, Houston, TX, July17-20, 2022. Invited Speaker.
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Hyun-Tae Kim and Miao Yu, "On-Fiber Multiparameter Sensor Based on Guided-Wave Surface Plasmon Resonances," J. Lightwave Technol. 40, 2157-2165 (2022)
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Liuxian Zhao, Timothy Horiuchi, and Miao Yu, "Broadband acoustic collimation and focusing using reduced aberration acoustic Luneburg lens", Journal of Applied Physics 130, 214901 (2021) https://doi.org/10.1063/5.0064571. This paper was selected to be promoted as Editors Pick. https://aip.scitation.org/doi/full/10.1063/5.0064571
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Liuxian Zhao, Timothy Horiuchi, and Miao Yu, "Broadband ultra-long acoustic jet based on double-foci Luneburg lens", JASA Express Letters 1,114001 (2021) https://doi.org/10.1121/10.0006817 This paper was chosen for the cover for the November 2021 issue of JASA Express Letters. https://asa.scitation.org/doi/full/10.1121/10.0006817
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Liuxian Zhao, Timothy Horiuchi, and Miao Yu , "Acoustic waveguide based on cascaded Luneburg lens", JASA Express Letters 2, 024002 (2022) https://doi.org/10.1121/10.0009386
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: M Evanusa, S Shrestha, V Patil, C Ferm�ller, M Girvan, Y Aloimonos, Deep-Readout Random Recurrent Neural Networks for Real-World Temporal Data, SN Computer Science 3 (3), 1-12, 2022.
  • Type: Journal Articles Status: Submitted Year Published: 2023 Citation: X. Lin, N. J. Sanket, N. Karapetyan, C. Fermuller ,M. Yu, Y. Aloimonos, Simulation Augmented Oyster Segmentation, ICRA 2023 (under submission)
  • Type: Journal Articles Status: Submitted Year Published: 2022 Citation: L Burner, NJ Sanket, C Ferm�ller, Y Aloimonos, Fast monocular distance estimation from time to contact, arXiv preprint arXiv:2203.07530, 2022. (submitted to Science Robotics).
  • Type: Conference Papers and Presentations Status: Other Year Published: 2022 Citation: CM Parameshwara, G Hari, C Ferm�ller, NJ Sanket, Y Aloimonos, DiffPoseNet: Direct Differentiable Camera Pose Estimation, Proceedings of the IEEE/CVF Conference on Computer Vision and Patterr Recognition CVPR, 2022.
  • Type: Websites Status: Published Year Published: 2022 Citation: findmyoyster.com
  • Type: Websites Status: Published Year Published: 2022 Citation: https://www.gearsinc.org/sam/
  • Type: Other Status: Other Year Published: 2022 Citation: Pattillo, D.A. (2022) Engineering Technology for Bottom Culture of Oysters. https://www.youtube.com/watch?v=ag5tlWXg9H8


Progress 09/01/20 to 08/31/21

Outputs
Target Audience:The target audiences include the following: Oyster aquaculture industry; 4-H members (age 9-18); Underwater robotics community; Shellfish biology and environmental sciences community; Graduate stduents, Undergraduate students (some from minority students from socially and economically disadvantaged regions); General audience Changes/Problems:Major challenges were delays caused by COVID-19 restrictions for hiring candidates, entering research facilities, and collecting data. At the University of Maryland Eastern Shore (UMES), we were not able to hire all 4 students as budgeted during the first year, but plan to hire students in the fall of the coming year with the unused funds. At Virginia Tech (VT), the postdoc and grad students were not yet hired for the first reporting period, although candidates have been recruited to start in the second period. Because this candidate was not recruited, however Van Senten (VT) completed the work during the first period, but this effort was not charged to this grant. The research associate at the Louisiana State University (LSU) was not hired during the first year because of the COVID-19 related hiring freeze. The Pacific Shellfish Institute (PSI) was not able to perform the expected field work during the first reporting period due to COVID-19, and thus did not charge the full budgeted salary for the report period. At Fraunhofer, COVID-19 related delays have caused a shift in workload from Year 1 to Year 2. There has been some reluctance by the industry to engage in the project thus far, partially due to COVID-19 related losses in business and labor and partially due to skepticism/delayed adoption of new technology. As the project moves forward and shows successful implementation it is expected that more growers will become engaged. The evaluation specialist at the University of Maryland (UMD) (Teresa McCoy) moved to another university and her position has not yet been filled. In addition, the team decided it would be prudent to develop a website for the proposed Smart Sustainable Shellfish Aquaculture Management(S3AM) program, although it was not originally budgeted for in the proposal. This website will increase the national visibility of the program. This will require the reallocation of some funds (e.g., travel funds) not used during Year 1 due to COVID-19. What opportunities for training and professional development has the project provided?The project provided training and professional development for 3 postdoc researchers, 4 graduate students, and 14 undergraduate students. The students and postdocs gained hands-on experience through working on sonar imaging devices, GPS devices, vision systems, and underwater drone platform. One of the postdocs presented a paper on oyster imaging and identification at a leading robotics conference, International Conference on Robotics and Automation (ICRA). Undergraduate students were provided training through new curriculum development and research activities (REU, ENEE 422 and ENGE 476). Training opportunities were also provided to K-12 students through 4-H activities (virtual camp & robotics competition). In addition, the 2020 Maryland Shellfish growers Conference was conducted by Parker and Webster (https://www.cbf.org/events/maryland/maryland-shellfish-aquaculture-conference.html). Oyster hatchery facility tours were conducted by UMCES at the Horn Point Lab. General oyster aquaculture questions were fielded by the UMD Extension staff. How have the results been disseminated to communities of interest?Research efforts were disseminated to scientific communities through conference papers/presentations and journal articles. A conference paper on machine learning based oyster detection was published and presented in the 2021 IEEE Int'l Conference in Robotics and Automation (ICRA), the premier event in Robotics. One journal article on shellfish aquaculture economic was published and another journal article on environmental sensors was submitted. At the 2020 Maryland Shellfish Growers conference, Co-PD Webster spoke on the panel entitled "New Horizons: Tech & Innovation in Aquaculture" where he discussed the potentials for the oyster robotics program for revolutionizing the on-bottom oyster aquaculture industry. In addition, program awareness was built through press releases entitled "Multi-institutional Research Team Receives $10M to Transform Shellfish Farming with Smart Technology" (https://robotics.umd.edu/release/marylandled-multiinstitutional-research-team-receives-10m-to-transform-shellfish-farming-with-smart) and "Research team receives $10M to transform shellfish farming with smart technology" (https://www.umces.edu/news/research-team-receives-10m-to-transform-shellfish-farming-with-smart-technology). The Bay Journal wrote an article about the project entitled "In Chesapeake oysters' future: underwater drones, shellfish barges?" (https://www.bayjournal.com/news/fisheries/in-chesapeake-oysters-future-underwater-drones-shellfish-barges/article_9c16064a-9183-11eb-9a20-3f01f19056c7.html). The USDA recorded a podcast series about the project including "Using Robotics to Improve Oyster Farming" (https://www.usda.gov/media/radio/weekly-features/2020-11-10/using-robotics-improve-oyster-farming) and "What Can Robotics Do for Oyster Farmers?" (https://www.usda.gov/media/radio/weekly-features/2020-11-10/what-can-robotics-do-oyster-farmers). What do you plan to do during the next reporting period to accomplish the goals?Plans for the next reporting period are continued execution of efforts highlighted in the project goals and objectives, specifically: Continue the development of multiparameter environmental sensors and imaging systems. (Goal 1) Carry out underwater environmental monitoring and oyster imaging experiments. (Goal 1) Develop a machine learning algorithm to predict numbers of oysters through learning numerosity without segmenting and counting individually. (Goal 1) Implement a Simultaneous Localization and Mapping (SLAM) algorithm into underwater navigation. (Goal 1) Introduce the S3AM framework to oyster farmers and elicit feedback for improvements to software design. (Goal 1 and Goal 5) Prototype an oyster farm management software. (Goal 1) Setup a secure data repository for the research team to collect and organize field data. (Goal1 and Goal 2) Continue the development of economic models. (Goal 3) Publish and present findings at industry conferences and workshops. (Goal 5) Offer a new course "Artificial Intelligence" in the fall semester, and continue to offer capstone design courses in the fall 2021 semester. (Goal 4) Pursue the virtual/in-person summer camp and state-wide youth robotics competition by UMD 4-H. (Goal 4) Create the National Advisory Board - quarterly meetings with three virtual and one in-person in MD annually. (Goal 5) Create the Industry Advisory Council - quarterly meetings with regular communication updates. (Goal 5) Develop the S3AM website and tie into existing UMD Aquaculture Extension website and activities. (Goal 1-5) Provide regular updates to the oyster aquaculture community about the S3AM project at state, regional, and national meetings. (Goal 5) Develop an initial video or series of videos to provide background on the project for industry and the public and promote use of the technology being developed. Include interviews with shellfish farmers and provide annual update videos on the project success. (Goal 5)

Impacts
What was accomplished under these goals? The accomplishments under each goal are summarized as follows. Goal 1 Completed preliminary development of an underwater drone monitoring system based on a remotely operated vehicle (ROV) with on-board imaging systems and sensors. Investigated visual inertial odometry system for autonomous navigation and environmental mapping model. Developed Machine Learning algorithms for oyster detection. Collected images of oysters with the underwater drone crop monitoring system on an oyster farm. Developed an ultracompact on-fiber multiparameter sensor that employs multiple guided-wave surface plasmon resonances and machine learning based signal processing for simultaneous salinity and temperature sensing. Investigated sonar imaging based commercial devices as well as acoustic metamaterial enhanced sonar localization. Developed basic implementation of a simulation environment of autonomous underwater navigation with ROS programing. Conducted survey of existing oyster farming management software and developed mockups for farm management software design. Designed technical architecture for data collection and sharing. Goal 2 Gathered environmental data from three different coastal regions: East, Gulf, and West coasts. Performed preliminary laboratory tests of the underwater drone monitoring system at the Shellfish Aquaculture Innovation Laboratory of the University of Maryland Center for Environmental Science (UMCES). Performed field tests of the GPS equipment within active oyster farm leases. Goal 3 Completed data collection and analysis and developed baseline economic cost models for shellfish aquaculture. Goal 4 Conducted online summer undergraduate research program and in person at University of Maryland Eastern Shore and Salisbury University, which is focused on machine learning and artificial intelligence for underwater farming applications. Two students were funded to develop programs for shellfish identification and testing underwater camera and computer processor equipment. Developed a new course ENEE 422 - Introduction to Machine Learning. Coursework includes electrical, sensor, and computer engineering, and machine learning using Python and Mathlab to develop processing algorithms for underwater images. Incorporated underwater robotics and AI solutions for shellfish aquaculture into senior design capstone course. Two students completed the senior design capstone course ENGE 476, with five total UMES students involved. "Design of an autonomous pix-hawk based Underwater Drone" designed an underwater drone that is able to navigate a series of waypoints in the Chesapeake Bay without having to resurface, thus enabling a crucial measuring device to ensure the health and life of certain oyster beds. "Design of Oyster Activity Detection System" developed machine learning algorithms to monitor oyster activities in an underwater environment. Carried out virtual 4-H Summer Camp activities in robotics, which incorporated shellfish into hands-on curricular activities in the areas of thrust, buoyancy, and underwater optics. There were 110 4-H participants registered, with 90 receiving the activity materials kits. Developed 2021 4-H robotics competition. The 2021 4-H robotics competition will be held at the Maryland state fair in August 2021. Goal 5 Created a preliminary list of participants for the National Advisory Board and Industry Advisory Council and gauged their interest in participating in the project. Made presentations to disseminate the project related technologies. The 2020 Maryland Shellfish Growers Conference included presentations by Parker and Webster (https://www.cbf.org/events/maryland/maryland-shellfish-aquaculture-conference.html). Webster presented information about the project on the panel entitled "New Horizons: Tech & Innovation in Aquaculture" where he discussed potential for oyster robotics to revolutionize on-bottom oyster aquaculture. (https://www.cbf.org/news-media/multimedia/video/2020-maryland-shellfish-aquaculture-conference/new-horizons-tech-innovation-in-aquaculture.html) (UMD Extension)

Publications

  • Type: Journal Articles Status: Submitted Year Published: 2021 Citation: H.T. Kim and M. Yu, "On-fiber multiparameter sensor based on guided-wave surface plasmon resonances with machine learning empowered signal processing," submitted to Scientific Reports, 2021.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: B. Sadrfaridpour, Y. Aloimonos, M. Yu, Y. Tao, and D. Webster. Detecting and Counting Oysters, the 2021 IEEE International Conference on Robotics and Automation (ICRA), May 30 - June 5, 2021, Xi'an, China.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Engle, C.R., J. van Senten, M. Parker, D. Webster, and C. Clark. 2021. Economic trade-offs and risk between traditional bottom and container culture of oysters on Maryland farms. Aquaculture Economics & Management. https://doi.org/10.1080/13657305.2021.1938295.