Progress 06/15/20 to 06/14/24
Outputs
Target Audience:Over the course of this project (2020-2024), we engaged a wide range of target audiences, including agricultural professionals, the scientific community, K-12 students, and the general public. Through conferences, workshops, seminars, internships, and outreach events, we focused on sharing knowledge, advancing adoption of the FieldDock system, and fostering engagement in STEM fields. Below is a summary of the key target audiences and how they were reached: 1. Agricultural Industry Professionals Our primary target audience included agricultural companies, commercial breeders, and technology specialists who play a key role in adopting advanced technologies in agricultural operations. Our outreach to these groups focused on demonstrating the practical benefits of FieldDock for real-time crop monitoring, autonomous UAVs, and advanced phenotyping: InfoAg Conference (July 2022): FieldDock was presented to leaders in precision agriculture, showcasing its capacity for real-time, automated crop data collection and its potential to enhance crop management and breeding strategies. Precision Breeding Academy at Bayer Crop Science (March 2023): We introduced FieldDock to commercial breeders and tech specialists, explaining how UAV-driven phenotyping could accelerate the breeding process by providing timely, accurate data on crop traits. Sorghum Innovators Meeting (February 2024): Engaged with sorghum breeders and agronomists to highlight how FieldDock's ability to monitor environmental conditions and crop performance in real time can drive improvements in sorghum breeding and resilience. 2. Scientific Community and Academia A significant focus was on engaging scientists, researchers, and graduate students. This group benefited from insights into the technical aspects of the FieldDock system, including sensor integration, UAV deployment, and data processing for crop phenotyping. Our activities in this area included: CROPS Conference at HudsonAlpha (June 2022, 2024): Presented FieldDock's platform to plant genomics researchers, demonstrating how real-time field data collection could accelerate genetic studies and improve breeding programs. University of Missouri Interdisciplinary Plant Group Seminar (October 2022): Discussed FieldDock's role in integrating genetic and environmental data for more comprehensive crop research, sparking collaborations with plant scientists. Taylor Geospatial Institute Research Day (April 2023): Presented the system's integration of geospatial data with plant phenotyping, highlighting its potential for broader agricultural applications in geospatial research. UC Berkeley Plant Gene Expression Seminar (March 2024): Introduced FieldDock to plant biologists, emphasizing its potential to transform data collection in phenotyping experiments through its cyber-physical infrastructure and machine learning integration. These selected events allowed us to disseminate the technical capabilities of FieldDock to a broad academic audience, facilitating collaboration and knowledge sharing on real-time phenotyping and crop improvement. 3. K-12 Students and Educational Outreach Engaging young students, particularly those from underrepresented groups in STEM, was a key component of the project. Our outreach efforts focused on providing hands-on learning experiences that introduced students to plant science, sensor technologies, and UAVs: FieldDock Lab Internships (2022-2024): Hosted high school students from local schools for internships in the Shakoor lab. These internships offered hands-on experience with sensor technology, data collection, and plant phenotyping. Several students from underrepresented backgrounds continued working with us through summer internships, gaining valuable STEM experience. Danforth Center PlantTech Jam (May 2023, 2024): Participated in this community outreach event, engaging over 500 participants annually through interactive demonstrations with drones and environmental sensors. Attendees were able to see firsthand how FieldDock could be used for real-time crop monitoring. Hydro-Heroes: Sorghum Sprint Sprout (2024): Developed an interactive game that introduced students (Grades 2-12) to IoT technologies, sensor integration, and real-time data visualization in agriculture. This game was featured in various outreach programs and provided a fun, educational experience that promoted interest in smart agriculture and STEM fields. St. Joseph's Academy Senior Service Project (May 2022): Hosted high school students for a senior service project in the Shakoor lab. The program provided hands-on training in plant phenotyping, environmental monitoring, and data analysis. One minority student continued working in the lab through a paid summer internship, gaining further research experience. Overall Impact Throughout the project, we engaged a broad spectrum of audiences, ensuring the dissemination of science-based knowledge across different sectors: Knowledge Transfer: Agricultural professionals and scientists gained insights into how real-time data collection through autonomous systems can revolutionize precision agriculture and phenotyping. Workforce Development: Through internships, workshops, and educational programs, postdoctoral researchers, students, and interns developed critical skills in sensor technology, cloud computing, data analysis, and UAV operations. STEM Engagement: By engaging K-12 students, particularly those from underrepresented communities, we helped inspire the next generation of scientists and technologists, contributing to diversity and inclusion in STEM fields. Through these activities, we have successfully reached a diverse group of individuals and organizations, ranging from professional scientists and engineers to students and the general public. The following is a more comprehensive list of the invited seminars, panels, and presentations given by PI Shakoor about the FieldDock project. Speaker, Sixth International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS), Lincoln, NE, October 7th, 2024 Speaker, 2024 DOE Joint Genome Institute Genomics of Energy & Environment meeting, Walnut Creek, CA, October 1st, 2024. Speaker, Sorghum Improvement Conference of North America (SICNA), Oklahoma City, OK, April 3rd, 2024 Panelist, NSF Cyber-Physical Systems Annual PI Meeting, Nashville, TN, March 20th, 2024 Panelist, Geo-Resolution Conference, Saint Louis, MO, September 28th, 2023 Speaker, AMSAC Mexican Seed Trade Association Annual Conference, Ixtapa-Zihuatanejo, Mexico, Aug. 3rd, 2023 Speaker, AgriTech Thursdays at T-Rex, Saint Louis, MO, August 10th, 2023 Workshop participant and presenter, NSF Research Agenda for Industry 4.0 Technology Implementation, Virtual presentation, July 20th, 2023 Speaker, Taylor Geospatial Institute Research Day, Saint Louis University, Saint Louis, MO, April 24th, 2023 Speaker, Precision Breeding Academy at Bayer Crop Science, Global Breeding Organization, Virtual presentation, March 29th, 2023 Speaker, UC Berkeley Plant Gene Expression Center Seminar Series, Virtual presentation, March 2nd, 2023 Speaker, ASA - CSSA - SSSA Annual Meeting, Baltimore, MD, November 9th, 2022 Speaker, University of Missouri Interdisciplinary Plant Group Seminar Series, Columbia, MO, October 23rd, 2022 Panelist, InfoAg Conference, Saint Louis, MO, July 26-27th, 2022 Speaker, CROPS Conference, HudsonAlpha Institute for Biotechnology, Huntsville, AL, June 15th, 2022 Speaker at the Salk Institute Suberin Club Seminar Series, Virtual presentation March 23rd, 2021 Speaker at the KWS Worldwide Digital Agriculture and Innovation Internal Webinar Series, March 2nd, 2021 Speaker at the University of Florida Plant Science Symposium - Big Data in Plant Science, Gainesville, FL, January 30-31st, 2020 Speaker at the Plant and Animal Genome Conference, Plant Phenotypes Session, San Diego, CA, January 11-15th, 2020 Changes/Problems:During the course of the FieldDock project, several challenges arose that required adjustments to the original approach. These changes were necessary to address unforeseen issues, delays, and evolving project needs. Despite these challenges, the core objectives of the project were met, and many of the adjustments led to new insights and improvements that enhanced the final outcomes. 1. Supply Chain Delays and Impact on Hardware Development One of the most significant challenges encountered during the project was supply chain disruptions, particularly in acquiring key hardware components for the UAV systems and environmental sensors. Global supply chain issues, exacerbated by the COVID-19 pandemic, led to delays in receiving parts necessary for sensor integration and UAV development. Impact on Project Timeline: These delays impacted the initial timeline for hardware deployment in field trials, pushing back certain milestones related to the installation of sensor networks and testing of autonomous UAV operations. As a result, the full deployment of the FieldDock system was delayed by approximately six months. Adjustments: To mitigate this delay, the project team focused on software development and data analysis tools during the interim period. This allowed for progress in developing the backend system, data pipelines, and machine learning integration, ensuring that the software side of the project continued on schedule. Despite the hardware delays, the adjustments allowed us to keep the project on track and ultimately achieve full deployment once the necessary components were received. 2. Modifications to UAV and Sensor Network Designs Initial testing of the UAV and sensor networks revealed some design limitations that required changes to improve system performance. Specifically, the autonomous precision landing system and sensor connectivity protocols required further refinement to ensure reliable field operation. Precision Landing System: Early field tests showed that the precision landing mechanism, which relied on Aruco markers and RTK GPS, did not consistently achieve the desired accuracy in certain weather conditions, particularly high winds. This affected the ability of the UAVs to autonomously dock and recharge. Adjustments: To address this issue, the team reworked the landing algorithms, integrating computer vision-based corrections and refining the use of geo-referencing data. These changes significantly improved landing precision and reduced the number of failed docking attempts, particularly in adverse weather conditions. This modification delayed some UAV testing but resulted in more robust performance during later field trials. Sensor Network Adjustments: The original sensor communication design used a combination of Wi-Fi and cellular networks for data transmission. However, field conditions in remote areas with poor cellular coverage led to connectivity issues. The project team adjusted the system to include low-power wide-area network (LoRaWAN) protocols for more reliable long-distance communication between sensors and the base station, ensuring continuous data collection. 3. Expansion of Machine Learning Capabilities While the original project focused on developing machine learning models for phenotypic data analysis, the growing complexity of the datasets collected by FieldDock led to an expansion of the machine learning framework to include more sophisticated analysis techniques. Data Complexity: The large volume of data collected from UAVs, environmental sensors, and field trials required more advanced data processing and storage capabilities than originally anticipated. In response, the project team expanded the machine learning platform to support edge computing for preliminary data analysis in the field, reducing the burden on cloud-based systems. New ML Models: In addition to the planned Genome-Wide Association Studies (GWAS) models, new machine learning models were developed to process real-time data streams and predict crop health metrics, such as biomass production and water usage efficiency. This shift allowed for more dynamic and real-time crop assessments during field trials. While this change expanded the scope of the project's data analysis capabilities, it also led to delays in integrating the expanded machine learning framework into the final FieldDock system. However, thanks to the no-cost extension phase, these additions ultimately enhanced the platform's functionality and provided richer insights for crop breeding and phenotyping. Conclusion and Final Impact Despite these challenges and necessary adjustments, the FieldDock project successfully met its overall goals, delivering a fully operational, integrated platform for precision agriculture and real-time phenotyping. The adjustments made during the project allowed for the refinement of the UAV and sensor systems, resulting in a more robust, reliable, and scalable platform. In addition, the expansion of machine learning capabilities and the improved design of the sensor networks provided more detailed insights into crop performance, enhancing the platform's impact on crop breeding programs. These outcomes position FieldDock as a valuable tool for the agricultural industry, research institutions, and smaller farming operations alike, helping to advance sustainable farming practices and improve food security in the face of climate change. What opportunities for training and professional development has the project provided?This project provided a wide range of training and professional development opportunities for the individuals involved, from postdoctoral researchers and graduate students to high school interns and early-career professionals. The training activities focused on enhancing proficiency in areas such as sensor integration, UAV operation, machine learning, data analysis, and agricultural technology development. Additionally, the project offered various professional development activities, including workshops, conferences, seminars, and one-on-one mentorship, fostering skill advancement in cutting-edge agricultural research and technology. Training Activities Postdoctoral Researchers and Graduate Students Hands-on Training in Sensor Integration and UAV Operation: Postdoctoral researchers and graduate students working on the FieldDock project received extensive training in the integration and operation of advanced sensor systems and autonomous UAVs. This included the installation and configuration of environmental sensors for tracking soil moisture, temperature, and humidity, as well as multispectral UAVs for real-time crop phenotyping. The hands-on experience helped participants gain technical expertise in sensor calibration, UAV mission planning, and data collection in agricultural settings. One-on-One Mentorship: Each postdoctoral researcher and graduate student was paired with a senior scientist or engineer from the project team for individualized mentorship. This mentorship included guidance on research design, data interpretation, and the practical application of sensor networks and machine learning tools for agricultural research. Mentors also assisted participants in developing their career goals and preparing for future leadership roles in precision agriculture and crop science. Edge Computing and Cloud-Based Data Management: Researchers were trained in the use of edge computing systems and cloud-based platforms for managing and analyzing the large datasets generated by the FieldDock system. This training focused on using tools such as Docker, OpenDroneMap, and machine learning algorithms for real-time data processing, improving their proficiency in data science and remote sensing technologies. Undergraduate and High School Interns Summer Internships: The project hosted several undergraduate (4) and high school interns (3) who gained hands-on experience working with the FieldDock platform. These students were trained in basic sensor technology, UAV operation, and data analysis, offering them valuable STEM exposure. Interns participated in field data collection, worked on research projects, and presented their findings at the end of their internships. Mentorship in Agricultural Technology: High school and undergraduate interns were paired with postdoctoral researchers and graduate students, who provided them with one-on-one mentorship throughout their internships. This mentorship focused on cultivating research skills, improving their understanding of agricultural technologies, and guiding their academic and career development. Interactive Learning Modules: Interns were introduced to interactive learning modules on agricultural IoT (Internet of Things) technologies. These modules, integrated into the Hydro-Heroes: Sorghum Sprint Sprout game, taught students about real-time data collection, sensor integration, and data visualization. This helped students gain practical experience in IoT systems, enhancing their understanding of modern agriculture technologies. Professional Development Activities Participation in Conferences and Workshops Sorghum Improvement Conference of North America (SICNA) Conference (2024): Project team members (4) presented at the SICNA Conference, which provided professional development opportunities through interactions with plant scientists and genomics researchers. Attendees learned about the application of the FieldDock platform in plant genomics and gained insights into the latest trends in crop breeding and phenotyping technologies. Taylor Geospatial Institute Research Day (2023): Several graduate students and researchers participated in this event, which focused on geospatial technologies in agriculture. They gained exposure to advanced geospatial data processing techniques and participated in workshops on integrating UAV data with crop phenotyping, enhancing their expertise in the intersection of agriculture and geospatial technologies. Mentorship and Career Development Career Development Support: Throughout the project, postdoctoral researchers and graduate students were offered career development opportunities, including guidance on grant writing, manuscript preparation, and research dissemination. PI Shakoor provided support in preparing participants for career advancement in academia, industry, and research institutions. Drone Pilot Certification Support: Several team members, including graduate students and research assistants, were given the opportunity to complete an online drone pilot certification course. This training provided them with the necessary skills to safely and legally operate UAVs for agricultural research, increasing their qualifications and expertise in field data collection. Collaborative Learning and Networking Research Collaborations: Team members had the opportunity to collaborate with researchers from other institutions, such as Saint Louis University and Clemson University, on projects that utilized aspects of the FieldDock platform for crop breeding and data collection. These collaborations provided professional development in cross-disciplinary teamwork and exposed participants to different perspectives on agricultural research. Outreach and Public Engagement: Project participants were actively involved in public outreach events, such as the Danforth Center PlantTech Jam and STEM Days at local schools, including Saint Louis University. These experiences allowed them to engage with the broader public, enhancing their communication skills and their ability to explain complex agricultural technologies in accessible ways. Summary of Key Training and Professional Development Outcomes: Skill Development: Participants gained advanced skills in UAV operations, sensor integration, machine learning, data processing, and real-time phenotyping, improving their technical proficiency in precision agriculture technologies. Mentorship and Career Growth: Researchers and students benefited from one-on-one mentorship, which supported their academic and professional growth, helping them develop research projects, improve communication skills, and prepare for careers in academia and industry. Hands-on Learning for Interns: Undergraduate and high school interns were provided with STEM learning opportunities through internships and hands-on exposure to agricultural technologies, inspiring future careers in science and engineering. Networking and Collaboration: Participation in conferences, workshops, and research collaborations provided project members with networking opportunities and exposure to the latest trends in agricultural technology, enhancing their professional development and career prospects. How have the results been disseminated to communities of interest?The results of the FieldDock project have been disseminated through a wide range of activities, targeting various communities of interest including scientific, academic, agricultural professionals, K-12 students, and the general public. The goal of these dissemination efforts was to not only inform relevant stakeholders about the project's progress but also to inspire interest in science, technology, and sustainable agriculture practices. Below are the key dissemination strategies used during the project. 1. Scientific and Academic Communities The scientific and academic communities were key audiences for this project. Dissemination activities focused on sharing findings from the FieldDock platform, promoting its integration of cyber-physical systems, machine learning (ML), and autonomous data collection technologies for crop phenotyping and breeding. Conferences and Seminars: Results were presented at high-impact conferences such as the ASA-CSSA-SSSA Annual Meeting (2022), CROPS Conference at HudsonAlpha (2022, 2024), and the Taylor Geospatial Institute Research Day (2023). These conferences provided a platform to share innovations in real-time phenotyping, ML integration, and the role of UAVs and sensors in crop management? . At these events, presentations highlighted the ability of FieldDock to accelerate crop breeding and improve precision agriculture by providing real-time, high-throughput phenotyping. NSF Cyber-Physical Systems Meeting (2024): The cyber-physical infrastructure of FieldDock was showcased to researchers and academics. Presentations included details on the integration of real-time data from UAVs and sensors, demonstrating the value of FieldDock in supporting large-scale field trials. *See "Target Audience" section for a more comprehensive list of Conferences and Seminars 2. Industry and Agricultural Professionals The results of FieldDock were also disseminated to industry professionals and agricultural stakeholders, particularly those in the agtech and commercial breeding sectors. These efforts aimed to encourage the adoption of FieldDock's technologies in real-world agricultural practices. InfoAg Conference (2022): The project team shared FieldDock's potential for reducing the time and labor required for field trials by automating UAV-based data collection and analysis. This presentation was directed at agricultural technology companies and professionals interested in precision agriculture . Precision Breeding Academy at Bayer Crop Science (2023): The project team demonstrated the platform's integration of machine learning for improving crop breeding decisions. This presentation reached commercial breeders and technology specialists, highlighting how the system could streamline large-scale breeding efforts . *See "Target Audience" section fora more comprehensive list of Industry engagement presentations. 3. Public Outreach and K-12 Education To reach broader audiences and inspire the next generation of scientists, the project engaged in extensive public outreach. These activities focused on increasing public understanding of advanced agricultural technologies and encouraging K-12 students to explore careers in STEM. Danforth Center PlantTech Jam (2023, 2024): The FieldDock team organized interactive booths at this public event, attracting over 500 participants each year. The event featured UAV demonstrations, drone flying tutorials, and real-time sensor monitoring activities. Attendees, particularly K-12 students, were introduced to agricultural technologies and learned how drones and IoT sensors can optimize crop production?. STEM Outreach through IoT Learning: FieldDock also launched the Hydro-Heroes: Sorghum Sprint Sprout game, an educational tool designed to introduce K-12 students to IoT technologies and real-time data collection. This game was demonstrated at the SLU ISCORE Camp (2024), where students learned about sensor integration, data visualization, and precision farming?. 4. Online Platforms and Open-Source Resources To ensure accessibility and facilitate collaboration, project results were made available online through multiple channels. These resources are designed to provide open access to the FieldDock system's tools and educational materials, supporting continued learning and application of the technology. FieldDock Project Website: The project website (https://fielddock.org) hosts information about FieldDock, including source code, educational resources, and data analysis tools. Researchers and educators can access these materials to support their own work in precision agriculture and phenotyping . GitHub Repositories: The source code for the FieldDock backend and frontend, along with the educational IoT learning tools, were made available on GitHub. These resources allow developers, researchers, and educators to integrate FieldDock into their work, enabling further innovation and collaboration in agricultural research. 5. News Releases and Public Dissemination of FieldDock The FieldDock project has received significant media coverage and public attention, highlighting its role in advancing sustainable agriculture through smart-farm technologies. Several key news releases and articles have shared the project's progress and its potential to transform crop management and phenotyping. Below are some of news outlets that have covered FieldDock: Danforth Plant Science Center (July 2020): A press release announced the $1.4 million grant from the National Institute of Food and Agriculture and the National Science Foundation for developing the FieldDock platform. The release emphasized FieldDock's capabilities, such as autonomous UAV deployment, real-time data collection, and its integration of machine learning to improve crop resilience while reducing water and energy inputs. This release served as a key communication piece to the scientific and agricultural communities, outlining the impact FieldDock would have on both research and farming practices. The Spoon (July 2020): An article featured FieldDock as a cutting-edge system that integrates multiple technologies for real-time crop monitoring. It highlighted FieldDock's sensor networks, autonomous UAVs, and renewable energy components designed to support sustainable farming. The coverage in this technology-focused outlet brought awareness to how the system can revolutionize data collection for plant breeders and farmers alike. EurekAlert! (August 2024): Following further developments, a 2024 news release on EurekAlert! discussed FieldDock's fully functional prototype and its role in enhancing crop breeding and environmental monitoring. The article also highlighted collaborations with commercial partners and research institutions such as the Taylor Geospatial Institute and Agrela Ecosystems. This release marked the project's progress and highlighted its significance in precision agriculture and sustainability efforts. Seed Today (June 2024): Another article from 2024 announced the official deployment of FieldDock in research fields, focusing on its practical applications in crop performance measurement and its potential for widespread use in farming operations. This release emphasized FieldDock's role in reducing the labor and complexity associated with traditional field trials, while providing actionable data for improving crop yields. These select news releases have been instrumental in disseminating FieldDock's achievements to both the scientific community and the general public. By leveraging media coverage in both research-focused and technology-driven outlets, the project has successfully raised awareness about the impact of data-driven farming technologies. These efforts, along with participation in high-profile conferences and the establishment of open-source resources, have ensured that FieldDock's results reach a wide array of stakeholders, from scientists and breeders to tech developers and farmers. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
Key Goal 1: Develop a Fully Automated, Scalable Integrated Crop Breeding and Smart-Farm Platform Major Activities Completed: Hardware Architecture: We designed and deployed the physical infrastructure of the FieldDock platform, including a UAV docking station equipped with autonomous charging capabilities. The system includes an array of sensors to monitor environmental conditions, such as temperature, humidity, soil moisture, and wind speed. The hardware also integrates edge computing capabilities, allowing for real-time data processing directly in the field, while also linking to cloud storage for long-term analysis. Software Architecture: The platform's software was built using Django for backend data management and ReactJS for a user-friendly frontend interface. The system enables real-time communication between drones, sensors, and the user interface using MQTT protocols. The FieldDock API allows users to access data and analytics from anywhere, providing real-time field monitoring and mission planning capabilities. UAV Integration: We deployed high-resolution multispectral cameras on UAVs, which are capable of capturing detailed imagery across multiple spectral bands, providing critical insights into crop health and phenotypic traits. UAVs are equipped with RTK GPS for high-precision navigation and landing, and a computer vision-based docking mechanism ensures automated recharging between missions. Data Collected: The system collected extensive multispectral imaging data from field trials, including key phenotypic traits such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge (NDRE), and other plant health indicators. The environmental sensors provided continuous data on weather conditions, soil moisture, and other key variables. This comprehensive dataset forms the backbone of FieldDock's crop performance analysis. Summary Statistics and Results: Over the course of multiple UAV missions, the platform captured thousands of high-resolution images across experimental fields. Analysis of the UAV data showed strong correlations between multispectral indices and key phenotypic traits, allowing for real-time assessment of plant health, growth rates, and drought resilience. Soil sensors provided critical data on moisture retention and soil temperature, which were used to help evaluate the environmental impact on crop performance and ground truth the results of water stress observed in the reflectance data. The integration of UAV imagery with ground-based sensor data enabled more precise phenotyping, improving the reliability of crop performance assessments. Key Outcomes or Accomplishments Realized: Change in Knowledge: FieldDock users--including agricultural scientists, breeders, and research teams--gained a deeper understanding of how integrating real-time UAV and environmental sensor data can significantly improve the accuracy and speed of crop phenotyping. They learned new techniques for remote monitoring and advanced data processing, improving their ability to make informed breeding decisions. Change in Action: The adoption of the FieldDock platform resulted in more efficient field trials, with our research team able to perform crop assessments remotely and autonomously, reducing the need for labor-intensive manual phenotyping. FieldDock enabled breeders to make faster, data-driven decisions, leading to the adoption of advanced UAV-based phenotyping tools across multiple research projects. Change in Condition: FieldDock reduced the operational costs and labor associated with field phenotyping by automating data collection and processing. The platform also improved collaboration between research institutions by providing a standardized system for data sharing, allowing research teams to access real-time field data from different locations and make informed decisions more quickly. Key Goal 2: Reduce Obstacles in Large-Scale Collaborative Crop Breeding Through Data-Driven Tools Major Activities Completed: ML Model Development: Several machine learning models, including Genome-Wide Association Studies (GWAS), were developed and integrated into the FieldDock platform. These models are designed to process phenotypic and genetic data in real time, helping to identify genetic markers that are associated with desirable traits such as drought tolerance and biomass production. The models are currently being made available to collaborators through the FieldDock platform, allowing them to upload their datasets and use pre-built ML models for analysis. FieldDock API and Open-Source Collaboration: The FieldDock API was designed to be accessible to external collaborators, providing them with the tools to upload datasets, perform analysis, and generate reports using the platform's ML models. The open-source nature of the platform enables cross-institutional collaborations, allowing researchers to share data, methods, and results in real time, facilitating large-scale collaboration in crop breeding. Data Collected: Large datasets were processed using the FieldDock platform, combining UAV-derived imaging data with environmental sensor data and genetic information from field trials. This comprehensive dataset was analyzed using the platform's ML models to identify key genetic markers related to plant performance under various environmental conditions. Summary Statistics and Results: The ML analysis processed over 500 gigabytes of field data, leading to the identification of more than 50 significant genetic markers in sorghum populations. These markers were linked to traits such as drought tolerance, various vegetation indices, and biomass production, and the results were shared in a number of presentations at multiple research institutions to accelerate crop improvement efforts. The integration of ML with real-time phenotypic data provides actionable insights that can improve the speed and precision of breeding decisions. Key Outcomes or Accomplishments Realized: Change in Knowledge: Researchers gained new knowledge about how ML models can be applied to real-time phenotypic data to accelerate genetic research and crop improvement. FieldDock's integration of ML models with field data allowed for more precise identification of genetic markers, improving the overall understanding of plant traits and their environmental interactions. Change in Action: The use of the FieldDock platform's ML tools led to the adoption of more advanced data analysis techniques by our breeding program, allowing us to optimize their selection processes based on real-time field data and genetic insights. This resulted in more targeted breeding efforts aimed at improving crop resilience and yield. Change in Condition: By reducing the time required to identify desirable traits and streamline breeding processes, FieldDock contributed to more efficient and cost-effective crop improvement programs. The platform's open-source nature enabled collaboration across research institutions, increasing the overall speed and scale of agricultural innovation. Please refer the following links for more information and source codes: Project website: https://fielddock.org Source code of Python-based FieldDock's API implementation: https://github.com/shakoorlab/fielddock-backend.git Source code of ReactJs-based FieldDock frontend implementation: https://github.com/shakoorlab/FieldDock.git? IoT learning activity for grade 3-12 students: https://github.com/Shakoor-Lab-Organization/learn_ioat.git
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
Gano, B., Bhadra, S., Vilbig, J. M., Ahmed, N., Sagan, V., & Shakoor, N. (2024). Drone-based imaging sensors, techniques, and applications in plant phenotyping for crop breeding: A comprehensive review. The Plant Phenome Journal, 7(1), e20100.
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
Roy, B., Sagan, V., Alifu, H., Saxton, J., Ghoreishi, D., & Shakoor, N. (2024). Soil Carbon Estimation From Hyperspectral Imagery With Wavelet Decomposition And Frame Theory. IEEE Transactions on Geoscience and Remote Sensing.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Ahmed, N., Esposito, F., & Shakoor, N. (2024, March). Bridging IoT Education Through Activities: A Game-Oriented Approach with Real-time Data Visualization. In 2024 IEEE Integrated STEM Education Conference (ISEC) (pp. 1-6). IEEE.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Ahmed, N., Esposito, F., Okafor, O., & Shakoor, N. (2023, November). SoftFarmNet: Reconfigurable Wi-Fi HaLow Networks for Precision Agriculture. In 2023 IEEE 12th International Conference on Cloud Networking (CloudNet) (pp. 212-220). IEEE.
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Progress 06/15/22 to 06/14/23
Outputs
Target Audience:During this reporting period, our project efforts have reached a variety of target audiences across different platforms and activities: Commercial Breeders, Technology Specialists and AgTech Companies: In March, 2023, we shared information about the FieldDock project with commercial breeders and technology specialists with a presentation at the Precision Breeding Academy at Bayer Crop Science's Global Breeding Organization. As an invited panelist at the InfoAg Conference in July 2022, PI Shakoor and project team members also reached a target audience of diverse agtech and precision agriculture companies. Scientific Faculty and Graduate Students: PI Shakoor connected with scientific faculty and graduate students as an invited speaker at multiple conferences and seminar series, including the UC Berkeley Plant Gene Expression Center Seminar Series, the ASA - CSSA - SSSA Annual Meeting, the University of Missouri Interdisciplinary Plant Group Seminar Series, and the CROPS Conference at the HudsonAlpha Institute for Biotechnology. PI Shakoor also presented and discussed the FieldDock project at the Taylor Geospatial Institute Research Day at Saint Louis University in April 2023. K-12 and General Public: In May 2023, the FieldDock team hosted a demonstration booth at the Danforth Center PlantTech Jam, a large community outreach event focused on robotics, engineering, and plant science. The FieldDock team showcased project activities and developed interactive displays with field drones and environmental sensors. We engaged with over 500 attendees of all ages, providing them with the opportunity for hands-on science and engineering experiences. Overall, our project's outreach efforts have successfully reached diverse sectors of society, ranging from students at different education levels, to professionals in academia and industry, and the broader public. Changes/Problems:In the project's initial two years, progress was significantly hindered by global supply chain disruptions due to COVID-19. Several vital FieldDock hardware system components were either unavailable or challenging to source for many months. However, we've since found alternative parts for the initial FieldDock prototype. COVID-19-related on-site work restrictions also significantly delayed hiring key project personnel in 2020 and 2021. The granted No-Cost Extension (NCE) has proved crucial, allowing us to recruit two early-career engineers to assist in completing project tasks. Despite the early Covid-related project delays, we're making steady progress and remain confident in achieving our project goals. What opportunities for training and professional development has the project provided?Through participation in several conferences and seminars in the field, this project has provided training and professional development for four postdoctoral researchers, one graduate student, one early career engineering research scientist, and one masters level engineering research associate. Additional training material for members of the project team has included online coursework for drone pilot certifications and career development workshops. How have the results been disseminated to communities of interest?During the reporting period, the results of the FieldDock project have been disseminated fairly extensively to various communities of interest through a multi-faceted approach, to not only inform about the project's progress but also to inspire interest in science and technology. Scientific and Academic Communities: In addition to publications, the project's findings were disseminated to scientific faculty, graduate students, and other academics through presentations and talks at various events. These included the UC Berkeley Plant Gene Expression Center Seminar Series, the ASA - CSSA - SSSA Annual Meeting, the University of Missouri Interdisciplinary Plant Group Seminar Series, and the Taylor Geospatial Institute Research Day, among others. Industry and Technology Specialists: The FieldDock project's relevance to AgTech and precision agriculture companies was highlighted at the InfoAg Conference and the Precision Breeding Academy at Bayer Crop Science's Global Breeding Organization. These activities provided an opportunity to discuss the project's latest developments with industry professionals and gain valuable feedback. Public Outreach and Education: The most significant outreach effort undertaken was the organization of a demonstration booth at the Danforth Center PlantTech Jam in May, 2023. This event, attended by over 500 participants of all ages, provided an interactive platform to explain the project's objectives and methodologies, demonstrate the use of drones and sensors, and inspire interest in science and technology. What do you plan to do during the next reporting period to accomplish the goals?The final phase of the FieldDock project will necessitate a comprehensive end-to-end systems integration, demonstrating interoperability between front-end/back-end software, user-defined parameters entered into the GUI, and reliable remote connectivity to cellular networks. The integration will also encompass autonomous flight mission planning at the edge by the system controller, a fully functional drone garage with robust electromechanical components capable of successfully deploying a drone, and a custom drone with a custom payload that can execute an autonomous flight mission. The process includes imaging the field based on user preferences, successfully returning to FieldDock, landing autonomously, offloading raw imaging data via WiFi to the edge computer in the drone garage, and performing an edge-computed image processing procedure. This procedure derives the necessary data products that can provide guidance for real-time breeding -- all powered by solar energy. Each of these components has been successfully demonstrated or has been deemed feasible based on the results of research and development to date. The development of the front-end/back-end is nearly complete and is functioning as designed. Remote mission planning will be developed and deployed by utilizing already widely-adopted, de-risked technologies such as QGroundControl, MavLink, and Pixhawk. Remote connectivity to cellular networks can be achieved using global SIM cards, or multi-carrier SIMs, to auto-connect to the best available network at any given moment. Companies like SIMetry offer a Tier 1 multi-carrier SIM that connects to Verizon, AT&T, and T-Mobile -- the three largest networks in the US -- as well as global SIMs that connect to the dominant networks in over 190 countries. The integration of a cellular modem has already been accomplished in this project and continues to send/receive data without issue. The drone garage will be one of the last components to be developed, as well as the power generation/distribution system, because there are still components of the FieldDock system that need development, and all related power requirements must be defined. However, a drone garage concept has already been fully designed and built to better understand the complexities of such a structure. This concept tested various electromechanical components, such as using a single "lead screw" to drive multiple moving parts, enabling the drone to take off and land safely from the platform/landing pad. The individual steps of the edge computer image processing pipeline have all been developed and successfully demonstrated. Work is currently underway to combine these processes into a single process, with completion anticipated soon. The main challenge for this FieldDock component is power consumption. The FieldDock team is exploring the integration of Field-Programmable Gate Arrays (FPGAs) to offload as much image processing work from the Nvidia Nanos as possible to conserve power. If integration proves too complex and resource-intensive, a larger power system/photovoltaic array will need to be installed at each FieldDock site to handle the power requirements of charging the drone, powering the electromechanical hardware components and edge processing, sensor measurements, network connectivity, and cloud data reporting.
Impacts
What was accomplished under these goals?
During the FieldDock project's third year, we made major adjustments in the development of fundamental components to better accommodate autonomous systems and enhance complex feature interoperability. Consequently, we hired an early-career engineer and a master's level associate at the Danforth Center. These strategic shifts improved and expedited the development of key features. We've enhanced hardware and software interoperability, streamlined system communication, and made considerable progress in autonomous flight and landing. We've successfully identified and tested all wired sensors, which are now fully functional. The development of wireless sensors, based on early research and development results from the FieldDock project, has commenced and is progressing rapidly. We expect to start testing these wireless sensors early in the upcoming project year. Further, a novel system controller, which manages all sensor-based technology, has been developed and validated through numerous indoor and outdoor experiments. We've begun developing a new iteration of the FieldDock backend cloud infrastructure, designed for flexibility in managing diverse data types, including incoming sensor data, fixed imaging data, processed drone flight data, system diagnostic data, and all other data captured by FieldDock. We are concurrently building a new FieldDock GUI, that is currently being integrated with the backend. This frontend software offers users control and a basic view of FieldDock features. In addition, we're developing a backend management interface for FieldDock administrators, and enhancing the frontend with additional data visualization components to improve user interaction with sensor data. FieldDock Sensors:Ambient air measurements will now be recorded at the FieldDock station using a high-accuracy central air sensor, with remote air measurements enabled by wireless sensors. These wireless sensors will measure the same parameters as the FieldDock station, serving as a means of validation and comparison between conditions outside and inside the crop canopy. Our standard air measurements include temperature, relative humidity, barometric pressure, and LUX for relative intra-canopy light measurements. Wind measurements will be recorded using a high-accuracy ultrasonic anemometer, and rainfall will be gauged by two high-accuracy rain sensors. Indirect validation of the rain measurements will be done by comparing them with soil moisture measurements at the FieldDock station and in the field where wireless sensors are deployed. Soil probes at the FieldDock station and those connected to the wireless sensors will provide detailed data on local soil moisture, temperature, and salinity conditions in each experimental plot. A unique system controller has been developed with a "Root" (Master) and "Leaf" (Slave) architecture. This controller will manage all FieldDock sensors and allow for easy integration of new sensors and an increase in the number of sensors reporting back to the nearest FieldDock. The system controller uses a combination of single board computers (SBC) for the Root and microcontrollers for the Leaves, facilitating an expandable, flexible hardware/software architecture. We have developed and successfully deployed prototypes for this system architecture. It can be utilized not only for remote sensor stations reporting back to the nearest system controller, but also for various electromechanical hardware components of the FieldDock Drone Garage. FieldDock Station Hardware and Power Distribution and Management:The overall design and development of the FieldDock structure, including the sensor tower and drone garage, was paused for the majority of the third year due to a shift in our approach to several key FieldDock system components. Our focus shifted to the edge image processing pipeline and the hardware it requires, system controller and sensor network, system interoperability, and the development of both the frontend and backend as well as autonomous drone flight. We also put the development of power distribution systems and hardware on hold as we focused on developing other key components. We have recorded power requirements for all new systems to guide the development of a remote solar power system in Year 4 (current NCE phase). Image processing at the Edge:Multispectral imaging data from the drone is now being automatically extracted and processed on the edge computer. Processes such as orthorectification, radiometric calibration, mosaicking, auto plot segmentation, calculation of plant multispectral indices, and automated plot scoring, which are all parts of the broader edge computing image process pipeline, have been individually developed, tested, and proven to be largely successful. We are currently working on combining these processes into a single, automated pipeline. This is being developed and tested with various edge computing concept systems. Each edge computing concept system leverages both novel (FPGA) and existing (Nvidia Nano Jetson) technologies, enabling us to identify trade-offs and determine the best path forward for achieving an optimal power consumption-to-graphics processing speed ratio. UAV Hardware:In Year 3, we decided to employ custom drone frame kits to expedite and enhance the testing of various facets of autonomous flight during the Software in the Loop (SITL) and Hardware in the Loop (HITL) testing phases. The use of small, affordable drone kits, adaptable for varying payload weights and types, has significantly accelerated the development of autonomous drone flight. The drone payload has largely remained as identified in the second year, with the exception of the wireless charging. The integration of the WiBotic charging platform was not successful because the power transfer between the transmitter and receiver requires extremely precise alignment and proximity. These requirements are not feasible for remote autonomous operations, primarily guided by GPS coordinates, and in some experiments, RTK. Consequently, we are currently researching and testing different methods for drone battery charging, which potentially include automating a mechanical connection between the drone and the charger upon landing. Autonomous Avionics Development:We have achieved success with autonomous drone landing under various heights, approaches, and outdoor conditions (high vs. low wind). This progress will serve as a foundation for full flight mission planning and execution. Testing on user-defined mission flight parameters through the GUI will soon commence. These parameters will be sent to the system controller on FieldDock via cellular connectivity. Subsequently, a mission will be autonomously planned and executed based on several factors, including GPS coordinates of the field of interest, size of the field, required altitude for desired imaging, drone battery life in relation to flight time, landing, wireless data offloading to the edge computer, and weather conditions. FieldDock Graphic User Interface (GUI): TheFieldDock GUI has been redesigned and redeveloped for better compatibility, serviceability, and ease of optimization with contemporary cloud infrastructure modules, software, and services. The previous GUI concept, which utilized a relatively unknown and infrequently used framework called Phoenix/Elixir, was initially chosen due to its purported reliability and inherent security. However, due to its limitations and developmental challenges, it was decided to abandon this framework for a more comprehensible one with a broader array of online resources. The original FieldDock GUI, constructed with HTML, CSS, and JavaScript, was repurposed and seamlessly integrated into the React framework. All intended features are operating as designed, and a substantial portion of the data collected by the drone and remote sensors is now streaming into the GUI.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Dilmurat, Kamila, Vasit Sagan, and Stephen Moose. "Ai-Driven Maize Yield Forecasting Using Unmanned Aerial Vehicle-Based Hyperspectral and Lidar Data Fusion." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 3 (2022): 193-199.
- Type:
Journal Articles
Status:
Accepted
Year Published:
2022
Citation:
Bhadra, S., et al. "Automatic Extraction of Solar and Sensor Imaging Geometry from Uav-Borne Push-Broom Hyperspectral Camera." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 3 (2022).
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2023
Citation:
Gano, Boubacar, Nurzaman Ahmed, and Nadia Shakoor. "Machine learning-based prediction of sorghum biomass from UAV multispectral imagery data." 2023 4th International Conference on Computing and Communication Systems (I3CS). IEEE, 2023.
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Progress 06/15/20 to 06/14/21
Outputs
Target Audience:Due to covid restrictions in 2020-2021, extension and outreach efforts were limited to virtual event presentations. In February 2021, the FieldDock project was presented at a Science in St. Louis Public Seminar series hosted by the Academy of Science Saint Louis, St. Louis County Libraries and Zonta Club of St. Louis's Girls Can STEM Initiative. The presentation was recorded for YouTube and shared with the Saint Louis Science Center and their YES Teens youth group. There were a total of 85 recorded participants, with representation from Saint Louis community residents and primary participation from middle and high school students. PI Shakoor also presented and discussed the FieldDock project at two student career panels including the NSF REU Graduate Student Career Panel (July, 2021) and BioBash: Exploring Scientific Career Paths (May, 2021), both hosted by the Donald Danforth Plant Science Center. The REU presentation was made to a group of approximately 30 undergraduate students pursuing a diversity of STEM careers. The BioBash event was attended by scientific trainees (graduate students, technicians and postdoctoral associates) at the Danforth Center and local agtech and biotech companies. Changes/Problems:?The project was and continues to be significantly challenged by the global supply chain disruptions. Several critical components of the FieldDock hardware system were unavailable and continue to be very difficult, if not impossible to source. For example, one of the critical processors (STM32L072RZT6 ARM32) and transceiver (THVD1450DGKR) of the main environmental sensor stack continue to be unavailable at the time of this report. We have started sourcing alternative parts that we can use for the initial prototype version of the FieldDock, however these systems will not have optimal design-to-manufacture features. As part of our TTP goals, we are using design-to-manufacture principles to ensure that the FieldDock prototype built by the end of the project will be optimized for commercial development. Our engineers have been in contact with component manufacturers who expect these supply issues to ease somewhat in late 2022 but there will be shortages off and on into 2023 and 2024 for some components. What opportunities for training and professional development has the project provided?Through participation in several conferences and seminars in the field, this project has provided training and professional development for three postdoctoral researchers and one research technician. How have the results been disseminated to communities of interest?As described in an earliersection, due to covid restrictions in 2020-2021, extension and outreach efforts were limited to virtual event presentations. In February 2021, the FieldDock project was presented at a Science in St. Louis Public Seminar series hosted by the Academy of Science Saint Louis, St. Louis County Libraries and Zonta Club of St. Louis's Girls Can STEM Initiative. The presentation was recorded for YouTube and shared with the Saint Louis Science Center and their YES Teens youth group. There were a total of 85 recorded participants, with representation from Saint Louis community residents and primary participation from middle and high school students. PI Shakoor also presented and discussed the FieldDock project at two student career panels including the NSF REU Graduate Student Career Panel (July, 2021) and BioBash: Exploring Scientific Career Paths (May, 2021), both hosted by the Donald Danforth Plant Science Center. The REU presentation was made to a group of approximately 30 undergraduate students pursuing a diversity of STEM careers. The BioBash event was attended by scientific trainees (graduate students, technicians and postdoctoral associates) at the Danforth Center and local agtech and biotech companies. What do you plan to do during the next reporting period to accomplish the goals?In year two of the project, we will engage in the discovery phase of minimal computing resources needed for the GWAS/modeling scripts and aim to make significant progress in the integration of the FieldDock's edge computing device for GWAS/Modeling. The FieldDock prototype and minimally viable product (Version 1) will be nearly complete as well. We will continue FieldDock system optimization for autonomous UAV development and inter-platform data analysis, and the first autonomous FieldDock flight is planned for September, 2022. We will establish the data transfer and feature extraction pipeline to process raw data and generate derived data products, and we will generate calibrated and mosaicked data processing methods for the drone collected imagery. Project Impact:Successful development of remote autonomous UAV platforms pose many challenges. The FieldDock system requires that enough solar power be captured and stored in order to regularly facilitate and power a fully charged UAV battery capable of ~20 minutes of flight with a reasonable sensor payload, wireless and wired communication of various system operations, wireless transfer of large datasets and imaging files, edge processing of large imaging files, image processing and downsampling of large image files, environmental sensor measurements at a high temporal resolution, operations of all electro-mechanical components, and cellular connectivity to the cloud. There are also many challenges associated with remote autonomous flight that the FieldDock project is addressing such as navigation, plot identification, and UAV landing/taking off safely aided by computer vision techniques, just to name a few. If successful, the FieldDock project will validate the possibility of remote autonomous UAV platforms capable of capturing and processing large amounts of data daily/weekly facilitated entirely by solar power. Remote infrastructure requirements for plant phenotyping equipment and the need for manual human-collected measurements will no longer be necessary. This daily capture of unbiased information will give tremendous insight into how crops respond to environmental conditions in real time throughout their life cycle. Researchers and crop breeders will have direct and daily access to remote field and crop conditions that could accelerate and improve global crop breeding strategies.
Impacts
What was accomplished under these goals?
At the end of year 1, the project is well on its way to successfully developing a prototype 'smart farm' system, or FieldDock. The project is on track to conduct its first autonomous flight in a crop research field in September, 2022. The FieldDock's onboard sensor suite, UAV technical specifications and power needs, solar charge and battery capacity requirements, onboard edge computational hardware and gateway hardware have been identified and are currently being sourced and/or developed. Sourcing of FieldDock onboard sensors: FieldDock mechanical and electrical infrastructure has been largely designed and will be developed to be modular. Modularity will allow the easy swapping of system component replacements as well as accommodate the integration of existing and/or any new 3rd party sensors for the foreseeable future. Universal connectors for the FieldDock system have been developed allowing custom sensor enclosures, 3rd party sensors and other ancillary system components to connect anywhere on the system bus. Temperature, humidity and air pressure sensors have been selected and will be housed in a small, custom waterproof enclosure that will allow for optimum ambient air measurements. Multiple instances of this "sensor module" will be connected to the FieldDock system in order to validate sensor accuracy including long term sensor drift as well as demonstrating reliability and resiliency to measurements crucial to flight command. This sensor module will also be used for the wireless sensor network, taking further advantage of the developed electronic architecture.Wind, rain, PAR and soil sensors have also been identified and acquired for integration. Design/development of aluminum hardware for FieldDock shell: The FieldDock physical mechanical structure will be made of aluminum and powder coated for long term weather and corrosion resistance. All enclosures are being designed to ensure no ingress will occur of water or other natural elements that could compromise internal electronics.The pole structure that all environmental sensors, battery boxes and the system controller/gateway mounts to has been fully designed and 5 prototype units have been manufactured. These units will be used to test form and fit of mechanical manufacturing specifications, water ingress in indoor/outdoor experiments, and the form and fit of the companion electrical components. Design/development of the UAV garage has also begun and the initial prototype will be made of 80/20 aluminum. This will allow the UAV garage to be easily adjusted without having to do a complete redesign in the event of a UAV model/size change later in development. Likely, the garage will open as a "drawer" with a scissor lift to elevate the UAV platform up above the UAV garage roof line to ensure minimum water ingress while maximizing the efficiency and simplicity of all moving parts. Minimizing power needs for these moving parts is essential due to the system's solar power requirements. Design/development of system electronics: Many iterations of various FieldDock electrical system PCB's have been developed and tested such as sensor module boards, cellular modem daughter boards (for compatibility with the system controller), battery pack and solar charge controller board, sensor connector board (junction board) and signal conditioning adapter modules for sensor plug-and-play functionality. Power distribution and management testing: charge controller components crucial to operations are still on backorder until a later date. Small quantities of these components acquired before the global shortage are being used to continue development and prototyping. However, the final FieldDock system power distribution system will require additional numbers of these components. Delivery is expected Q2 2023. Lithium ion battery size/type and solar panel selection have been identified and acquired for development. Custom PCB's for solar charge control and overall system power distribution have been designed and multiple prototype iterations have been acquired and tested. UAV communication integration with FieldDock software/hardware and testing: The FieldDock edge computer system will extract images from the MicaSense Altum camera via the camera's HTTP interface over WiFi. From here, the edge computer will perform the data reduction, image mosaicking, field plot auto-segmentation and vegetative index calculations. The UAV mission computer (Pixhawk) will receive its mission file from the system controller via WiFi as well through the Mavlink serial interface. It will control the phases of flight and transmit mission telemetry while in flight during the testing phase only. Sourcing of UAV hardware: Aegis (formally Emergent RC) Intense Eye V2 Quadcopter became the choice for our preliminary drone testing/prototyping due to its compatibility with WiBotic's wireless battery charging technology. While we test our SITL (software in the loop) and eventually HITL (hardware in the loop) with this UAV, we will outfit it with temporary 3D printed components that have the same size, shape, and weight of the actual components we intend to integrate for safety purposes. Identification of payload sensors: The UAV payload with a MicaSense multispectral camera has been identified and sourced based on weight, physical size, power consumption, data resolution, project research requirements and its ability to integrate with the custom avionics box that will likely house a Pixhawk. A downwelling radiation sensor, WiFi/cellular antenna, wireless charging antenna and battery will also be mounted to the UAV. Sensor integration and testing: To date, our ambient air sensors (temperature, humidity, air pressure) have been integrated and undergone periodic testing. Further integration of other sensors will happen at a later date once other system components are brought online. Initial testing of the MicaSense Altum has been carried out during preliminary UAV flights conducted by Co-PI Sagan's group at Saint Louis University. Autonomous avionics development: Development of autonomous flight control software is underway in addition to the FieldDock electrical, power and computational system to ensure firmware and systems communications are consistent, well-understood and validated throughout the development phase. SITL and HITL testing with the Aegis UAV have begun and will continue throughout year 2 of the FieldDock project. Graphic User Interface: A preliminary working version of the Graphic User Interface (GUI) for FieldDock has been completed. The GUI is a web application demonstrating up-to-date responsive design principles so that users can view the GUI on a variety of device platforms (desktop, tablet, smartphone, etc.). The GUI will be hosted within the backend (AWS) and use the Phoenix web framework and Elixir coding language. With this initial prototype, software engineers can begin best practices for connecting all FieldDock collected data in the cloud database to the GUI for user interaction. As new FieldDock derived data products are successfully sent and stored in the cloud, they will subsequently be connected to features within the GUI for user viewing and downloading. Version 2 of the GUI will begin development in year 2 based on feedback from software engineers and is expected to be completed in Q4 2021.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Maimaitijiang, M., et al. "A fully automated and fast approach for canopy cover estimation using super high-resolution remote sensing imagery." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 3 (2021): 219-226.
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