Source: UNIVERSITY OF FLORIDA submitted to NRP
NRI: INT: COLLAB: HIGH THROUGHPUT MULTI-ROBOT WEED MANAGEMENT FOR SPECIALTY CROPS
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
COMPLETE
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
1021701
Grant No.
2020-67021-30761
Cumulative Award Amt.
$793,997.00
Proposal No.
2019-04777
Multistate No.
(N/A)
Project Start Date
Apr 1, 2020
Project End Date
Mar 31, 2025
Grant Year
2020
Program Code
[A7301]- National Robotics Initiative
Recipient Organization
UNIVERSITY OF FLORIDA
G022 MCCARTY HALL
GAINESVILLE,FL 32611
Performing Department
Agricultural and Biological Engineering
Non Technical Summary
Most agrochemicals (e.g. herbicides) are applied uniformly, despite the fact that distribution of target pests, pathogens and weeds is typically patchy. Uniform application wastes valuable agrochemicals by applying where little or no problems exist. The result is increased costs, risk of crop damage, pest resistance to chemicals, environmental pollution and contamination of the edible products.The main goals of this project are to (i) develop further a low-cost, high throughput, and smart technology to simultaneously scout and spray a variety of weeds with different herbicides; (ii) develop low-cost and multi-crop autonomous vehicles equipped with the precision spray technology; (iii) design and develop a high-level task planning and control system for the autonomous precision sprayers; and (iv) quantify the economic parameters necessary for the system to be commercially successful.
Animal Health Component
50%
Research Effort Categories
Basic
20%
Applied
50%
Developmental
30%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
4021460202060%
4041421114040%
Knowledge Area
404 - Instrumentation and Control Systems; 402 - Engineering Systems and Equipment;

Subject Of Investigation
1460 - Tomato; 1421 - Cucumber;

Field Of Science
2020 - Engineering; 1140 - Weed science;
Goals / Objectives
The main goals of this project are to (i) develop a low-cost precision sprayer to simultaneously treat a variety of weeds; (ii) develop a fleet of low-cost and multi-crop robotic platforms equipped with the precision sprayer for specialty crops (e.g. tomato, pepper); (iii) develop an intelligent and adaptive multi-robot coordination system to optimize spraying application for several specialty crops; and (iv) quantify the economic parameters necessary for the system to be commercially successful.
Project Methods
Develop further a low-cost, high throughput, and smart technology to simultaneously scout and spray a variety of weeds with different herbicidesDevelop low-cost and multi-crop autonomous vehicles equipped with the precision spray technologyDesign and develop a high-level task planning and control system for the autonomous precision sprayersConduct comprehensive economic analyses of the proposed multi-robot system.

Progress 04/01/20 to 03/31/25

Outputs
Target Audience:Vegetable growers and allied industry; students and faculty, extension agents. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?This project has offered a wide range of training and professional development opportunities for graduate students, postdoctoral researchers, and undergraduate students, helping to build a highly skilled agricultural engineering and AI-enabled workforce. Graduate Student and Postdoctoral Training: Several PhD students and postdoctoral researchers were actively involved in this project and had the opportunity to present their research findings at local, national, and international scientific conferences and workshops, including the American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting. These presentations not only enhanced their scientific communication skills but also expanded their professional networks and increased their visibility within the agricultural technology research community. To strengthen their technical expertise, team members enrolled in paid, self-paced online courses in areas such as deep learning, real-time image processing, and control systems through platforms like Coursera and Udemy. These courses complemented the hands-on experience gained during the development and deployment of the robotic smart spraying system. As a result, students improved their proficiency in AI model training, machine vision, robotic integration, and precision agriculture technologies. One PhD student successfully completed and defended a dissertation directly supported by this project, which focused on the development of real-time weed detection and precision spraying using robotics and machine learning. This research contributed novel insights to the field and helped establish a foundation for future innovations in autonomous crop management. Undergraduate Research and Mentorship: The project team also offered valuable research opportunities to undergraduate students through the Summer Undergraduate Research Experience (SURE) program. Four students participated in the 8-week immersive research experience, working closely with PhD students and postdoctoral scholars. They gained hands-on experience in data collection, sensor integration, AI model testing, and field experiments on robotic platforms. These activities exposed them to interdisciplinary research spanning agricultural engineering, computer vision, and environmental sustainability, helping to inspire potential future careers in STEM and precision agriculture. Professional Outreach and Leadership Development: Principal Investigator (PI) Dr. Ampatzidis actively participated in various outreach and extension events to disseminate project findings and engage with stakeholders, including growers, technology companies, and fellow researchers. These events provided mentoring opportunities for students, who were often invited to co-present and engage with diverse audiences, enhancing their ability to translate complex technical content into accessible language. Collectively, these efforts have created a supportive training environment that equips participants with technical knowledge, research skills, and professional experience essential for leadership in agricultural automation and AI-driven crop management. How have the results been disseminated to communities of interest?Dr. Ampatzidis (PI) actively disseminated the findings of this project through more than 70 extension, educational, and outreach presentations, reaching over 3,000 stakeholders. These venues included a wide range of audiences, growers, extension agents, industry representatives, and educators. Examples of such engagements include: Transforming vegetable production with AI and robotics. Spring Vegetable Field Day, Southwest Florida Research and Education Center, Immokalee, FL, May 21, 2025. Future of Robotics and AI in Agriculture. Lead Panel on Robotics and AI, Executive Seminar on Strategic Dialogues for the Future: Advancing Agricultural Science to Deliver Societal Value in Latin America, the Caribbean, and the U.S. Organized by FONTAGRO (The Regional Fund for Agricultural Technology), UF, and the Crop Transformation Center, Gainesville, FL., May 13-14, 2025. AI and Robotics in Agriculture. A Virtual Visit to an AI University, UF AI² Center, Gainesville, FL, May 12, 2025. Robotics, automation, and AI for specialty crops. Florida AgTech and AI Expo, Punta Gorda, FL, December 14, 2023. Integrating AI and robotics for enhanced crop management. IFAS AI and Data Science Seminar Series, Gainesville, FL, March 26, 2025. Emerging and commercially available AI-driven technologies for specialty crop production. AgTech Show, Southwest Florida Research and Education Center, Immokalee, FL, March 25, 2025. Emerging AI-enabled technologies in agriculture. ABE seminar for Stare, Gainesville, FL, March 13, 2025. Advancing sustainability in agriculture with AI-driven robotic technologies. Internet of Things for Precision Agriculture ERC (IoT4Ag) Seminar Series, Gainesville, FL, February 14, 2025. Transforming Specialty Crop Management with AI and Robotics. BioComplexity Seminar Series, Agricultural and Biological Engineering Department, Gainesville, FL, February 11, 2025. Robotics and AI-enabled emerging technologies for precision agriculture. AI Application in Agricultural Water, Soil, and Crop, In-service-training (IST), online, January 14, 2025. Robotics and artificial intelligence applications in agriculture. Department of BiomedicalEngineering & Science Seminar, Florida Tech University, Melbourne, FL, November 1, 2024. AI-enhanced emerging technologies for precision vegetable management. Sensing and emerging artificial intelligence technologies in vegetable production, Southwest Florida Research and Education Center, Immokalee, FL, October 2, 2024. Artificial intelligence and robotics for specialty crop production. Agrotechnology Workshop (Taiwan ICDF), Coral Gables, FL, September 3, 2024. Precision Agriculture and Smart Machines. Thai Ministry of Agriculture and Cooperatives through the Cochran Fellowship Program- USDA Foreign Agricultural Service (FAS), Gainesville, August 6, 2024. Emerging enterprises and research commercialization (panel discussion). UF/IFAS Research Forum, Gainesville, FL, April 30, 2024. AI and remote sensing for precision crop management. Center for Remote Sensing, IFAS/UF, April 26, 2024. AI and robotics in agriculture. AI In-service-training (IST), Wimauma, FL, April 9, 2024. AI applications for crop monitoring and predictive analytics. (co-presented with Dr. Congliang Zhou). AI In-service-training (IST), Wimauma, FL, April 9, 2024. Robotics and AI for vegetable production. Florida Citrus Show, Fort Pierce, FL, April 3, 2024. AI-enabled technologies for precision crop management. Hort3213 Fruit and Nut Production, Oklahoma State University, February 26, 2024. Robotics, automation, and AI for specialty crops. Florida AgTech and AI Expo, Punta Gorda, FL, December 14, 2023. Steam weeding in sugarcane production - An alternative tool for weed management. Florida Crystals webinar, December 7, 2023. Potential technologies to maximize drone use. South Florida Veg Growers Meeting: Whitefly Management, Immokalee, FL, November 29, 2023. AI-enabled technologies for precision management of specialty crops. USDA ARS-Northeast Area retreat, November 14, 2023. Drones and AI for Precision Crop Management. Florida Ag Expo, Gulf Coast Research and Education Center, Wimauma, FL, November 9, 2023. Artificial intelligenceenhanced technology for precision citrus production. Cold Hardy Citrus Field Day, Quincy, FL, October 26, 2023. Commercializing your AI: Lessons and challenges from spinning off AI companies. UF AI Days, Gainesville, FL, October 18, 2023. Collaborative research: NRI: High throughput multi-robot weed management for specialty crops. Foundational Research in Robotics (FRR) -National Robotics Initiative (NRI) Principal Investigators' (PI) Meeting, Arlington, Virginia, May 1-3, 2023. Advancing Florida's Specialty Crop Production and Harvesting using Mechanization, Precision Agriculture, Drones, Robotics, and Artificial Intelligence. UF ABE Centennial Seminar Series, Gainesville, FL, April 20, 2023. Artificial Intelligence and precision agriculture in vegetable production. Citrus Show, Vegetable Session, Fort Pierce, April 13, 2023. UAVs, Sensors, & Other Applied Research Technology. Research Center Administrators Society (RCAS) Meeting, Gulf Coast REC, February 6, 2023. AI-enhanced smart machinery for precision scouting and spraying. National Alliance of Independent Crop Consultants (NAICC) 2023 Annual Meeting and AG PRO EXPO, Nashville, TN, January 23-27, 2023. AI-enhanced pest and disease management in vegetable production. South Florida Vegetable Growers Meeting: Research Updates on Weed and Disease Management. Immokalee, January 12, 2023. Robotics and artificial intelligence in Agriculture. ABE2012C: Intro to Biological Engineering (Guest Lecture), UF. October 12, 2022 AI-enhanced precision management in specialty crops. USDA National Agricultural Statistics Service, Research and Development Division (virtual seminar). September 29, 2022. Robotics and AI in specialty crop production. 4-H robotics team, Exploding Bacon (virtual seminar). September 26, 2022. Robotics and artificial intelligence in Agriculture. ABE2012C: Intro to Biological Engineering (Guest Lecture), UF. October 12, 2022 AI-enhanced precision management in specialty crops. USDA National Agricultural Statistics Service, Research and Development Division (virtual seminar). September 29, 2022. Robotics and AI in specialty crop production. 4-H robotics team, Exploding Bacon (virtual seminar). September 26, 2022. Emerging technologies for specialty crops. LaBelle Rotary Club, LaBelle Fl, November 16, 2021. Smart and data-driven farming. ABE2012C: Intro to Biological Engineering (virtual lecture), October 6, 2021. How artificial intelligence can enhance precision agriculture. Institute for Learning in Retirement, Life-Long Learning programs at Oak Hammock (virtual lecture), October 5, 2021. Artificial intelligence in agriculture. UF ABE Florida Farm Bureau Board, UF ABE, Gainesville, September 22, 2021. Smart and data-driven agriculture. UF Frontiers of AI (virtual lecture), September 21, 2021. Artificial intelligence in crop production. UF Foundation National Board Assembly (virtual event), September 17, 2021. Emerging technologies for precision management in vegetables. SWFREC Vegetable seminar, Immokalee, May 28, 2021. AI applications for Florida crop production. AFIS AI Forum, Virtual Seminar, May 26, 2021. Emerging Technologies and AI for BMP. UF/IFAS BMP Virtual Summit. May 18, 2021. Artificial Intelligence in Agriculture. UF AI Town Hall, May 13, 2021. Artificial intelligence for precision agriculture applications. UF/IFAS Extension Symposium, May 3-7, 2021. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? Objective 1 - Development of a Low-Cost Precision Sprayer for Weed Control: Significant advancements were made toward developing a smart, low-cost, precision sprayer for the simultaneous treatment of diverse weed species in specialty crops. The weed detection system was enhanced with an industrial-grade camera, and image capture parameters were optimized via software and manual control. The dataset was expanded to include more representative images of broadleaf, sedge, and grass weeds across tomato and pepper fields, and class balance was achieved using data augmentation and pruning of non-relevant categories. Light Deep Learning models were retrained and evaluated, achieving high detection performance, with F1 scores exceeding 98% for crop plants and above 80% for weed classes. The machine vision system was evaluated under field conditions, achieving real-time operation speeds of 22-25 FPS. The AI model successfully classified and localized weed and crop types in continuous video streams using a Python-based implementation. Parallelly, the smart spraying system was substantially improved. Experiments analyzed system response under varying flow rates and pressure conditions. The system was upgraded with three additional pumps to ensure sufficient flow capacity. A Proportional-Integral (PI) controller was designed and implemented to maintain system pressure under dynamic spraying conditions, achieving stability with minimal overshoot and low settling time. The nozzle selection process involved rigorous testing under variable wind, pressure, height, and velocity conditions. A 15° even-flat-fan nozzle was selected as optimal, with the best performance achieved at 15 psi and spray height under 25 inches. Spray coverage and drift resistance were optimized while maintaining a low spray footprint, achieving over 88% recall and 89% precision for weed spraying. Objective 2 - Development of Multi-Crop Robotic Platforms with Precision Sprayer: A new robotic platform was developed and deployed in vegetable fields. The updated design features a larger wheelbase and four independently driven motorized wheels for improved mobility in uneven terrain. Electrical and electronic systems were reorganized for efficient layout, and a higher-capacity battery was integrated to support longer operation time. Safety was enhanced through multi-layered electrical redundancies. The entire precision spraying system, including machine vision, pumps, PI-controlled valves, and nozzles, was mounted on this robotic platform. Field trials on tomato and pepper beds demonstrated effective integration and coordination of detection and actuation systems. Objective 3 - Development of Adaptive Multi-Robot Coordination System: The capacitated arc routing model (CARM) was further refined and evaluated to support optimized multi-robot coordination in weed spraying across large specialty crop fields. The model incorporates UAV-derived weed maps generated using the trained detection models, accounting for uncertainty in spatial weed distribution. Compared to prior models, the updated CARM demonstrated improved efficiency in route planning and resource allocation for coordinated autonomous spraying, particularly in high-density and heterogeneous weed scenarios. Objective 4 - Economic Analysis of System for Commercial Viability: Preliminary economic assessments were initiated to identify key cost drivers and performance metrics affecting commercial adoption. The updated spraying and robotic platform designs were evaluated for cost-efficiency in real-world field trials. Emphasis was placed on the reduction of herbicide usage, labor costs, and system maintenance.

Publications

  • Type: Peer Reviewed Journal Articles Status: Under Review Year Published: 2025 Citation: Vijayakumar V., Ampatzidis Y., Lacerda C., Burks T., Lee W.S., Schueller J.K., 2025. AI-driven real-time weed detection and robotic smart spraying for optimized performance and operational speed in vegetable production. Biosystems Engineering (under review).
  • Type: Peer Reviewed Journal Articles Status: Under Review Year Published: 2025 Citation: Vijayakumar V., Neto A.D.C., Ampatzidis Y., Schueller J.K., Lee W.S., Burks T., 2025. Design and evaluation of a PI-controlled robotic sprayer for precision herbicide applications with multi-nozzle integration. Precision Agriculture (under review).
  • Type: Peer Reviewed Journal Articles Status: Published Year Published: 2023 Citation: Vijayakumar V., Ampatzidis Y., Schueller J.K., Burks T., 2023. Smart spraying technologies for precision weed management: a review. Smart Agricultural Technology, 6, 100337, https://doi.org/10.1016/j.atech.2023.100337.
  • Type: Book Chapters Status: Published Year Published: 2022 Citation: Kakarla S.C., Ampatzidis Y., Park S., Adosoglou G., and Pardalos P., 2022. Emerging sensing technologies for precision agriculture. Book: Information and Communication Technologies for AgricultureTheme I: Sensors. Springer Nature, ISBN: 978-3-030-84143-0.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2025 Citation: Vijayakumar V., Neto A.D.C., Ampatzidis Y., 2025. AI-powered autonomous smart sprayer for precision weed management: Advancing sustainable agriculture through machine vision, automation, and control systems. 8th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL 2025), Davis, CA, USA, August 27-29, 2025.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2025 Citation: Vijayakumar V., Neto A.D.C., Ampatzidis Y., 2025. AI-driven autonomous spraying for precision weed management in specialty crop production. 15th European Conference on Precision Agriculture, Barcelona, Spain, June 29  July 3, 2025.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Ampatzidis Y., 2023. Emerging and advanced technologies in agriculture. Link (Linking Industry Networks through Certifications; High School Teachers Training) Conference, Daytona Beach, FL, October 10-12, 2023.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Ampatzidis Y., 2023. AI-Enhanced Technologies for Precision Management of Specialty Crops. Sustainable Precision Agriculture in the Era of IoT and Artificial Intelligence, Bard Ag-AI Workshop, Beer Sheva, Israel, July 18-20, 2023.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Vijayakumar V., Ampatzidis Y., Silwal A., Kantor G., 2023. 2023. Development of a machine vision and spraying system of an autonomous robotic sprayer for specialty crops. ASABE Annual International Meeting, Omaha, Nebraska, USA, July 8-12, 2023.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Ampatzidis Y., 2023. Solutions to critical issues facing field and specialty crop production. Integrative Precision Agriculture  Local Solutions Through Global Advances International Conference, Athens, Georgia, May 18-19, 2023.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Vijayakumar V., Ampatzidis Y., Silwal A., Kantor G., 2023. Specialty crop-specific robotic precision smart sprayer based on machine vision and PWM-controlled spraying system. AI in Agriculture Conference: Innovation and Discovery to Equitably meet Producer Needs and Perceptions, Orlando, FL, April 17-19, 2023.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Ampatzidis Y., 2023. AI-enhanced smart machinery for precision scouting and spraying. Annual Meeting and Ag Expo of the National Alliance of Independent Crop Consultants (NAICC), Nashville, TN, January 23-27, 2023.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Ampatzidis Y., 2022. AI-enhanced smart farming. The 6th IEEE UV2022, International Conference on Universal Village, Virtual and main venue in Boston, USA, October 22-25, 2022 (Keynote Speaker).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2025 Citation: 15. Vijayakumar V., Neto A.D.C., Ampatzidis Y., 2025. AI-powered autonomous smart sprayer for precision weed management: Advancing sustainable agriculture through machine vision, automation, and PI control systems. AI in Agriculture Conference: The Role of AI in Autonomous Agricultural Systems and Socioeconomic Effects. Starkville, MS, March 31 to April 2, 2025.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Vijayakumar V., Neto A.D.C., Ampatzidis Y., 2024. A robotic precision smart sprayer based on machine vision and PI-controlled spraying system for specialty crops. ASABE Annual International Meeting, Anaheim, California, USA, July 28-31, 2024.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Ampatzidis Y., 2024. Can AI and automation transform specialty crop production? 16th International Conference on Precision Agriculture (ICPA), International Symposium on robotics and Automation, Manhattan, Kansas, USA, July 21-24.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Ampatzidis Y., Vijayakumar V., Pardalos P., 2024. AI-enabled robotic spraying technology for precision weed management in specialty crops. Optimization, Analytics, and Decision in Big Data Era Conference (in honor of the 70th birthday of Dr. Panos Pardalos), Halkidiki, Greece, June 16-21.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Vijayakumar V., Ampatzidis Y., 2024. Development of a machine vision and spraying system of a robotic precision smart sprayer for specialty crops. 3rd Annual AI in Agriculture and Natural Resources Conference, College Station, TX, April 15-17, 2024.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Zhu Z., Ampatzidis Y., Silwal A., Pardalos P., 2022. Robust route planning for autonomous vehicles. 2022 ASABE Annual International Meeting, July 17-20, 2022, 2200118, doi:10.13031/aim.202200118.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Vijayakumar V., Partel V., Ampatzidis Y., Silwal A., Kantor G., 2022. Precision weed management based on machine vision and AI integrated to an autonomous robotic smart sprayer for specialty crops. ASABE Annual International Meeting, Houston, Texas, July 17-20, 2022.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Vijayakumar V., Ampatzidis Y., Silwal A., Kantor G., Burks T., 2022. Machine vision and AI-equipped multi-crop robotic smart sprayer for precision weed management of three weed types in vegetable crops. FASABE Annual Conference and Trade Show, Clearwater Beach, FL, May 19-22.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Vijayakumar V., Ampatzidis Y., Silwal A., Kantor G., 2022. Machine vision and AI precision weed management using an integrated autonomous robotic smart sprayer for specialty crops. Envisioning 2050 in the Southeast: AI-driven innovations in agriculture. Auburn, AL, March 9-11 (poster).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Ampatzidis Y., 2021. AI applications in specialty crops. 2021 Virtual ASABE Annual International Meeting, Special Session - Processing Systems AI and Data Science Application in Food and Biological Material Processing, July 11-14, 2021.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Vijayakumar V., Partel V., Ampatzidis Y., Silwal A., Kantor G., 2021. Autonomous smart sprayer for precision weed management using machine vision and AI. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Adosoglou G., Park S., Ampatzidis Y., Pardalos P., 2021. A high-level task planning of autonomous robots with multi-dimensional loading constraints for precision weed management under field variability. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021, ASABE Paper No. 2100426, doi: 10.13031/aim.202100426.
  • Type: Theses/Dissertations Status: Published Year Published: 2024 Citation: Vijayakumar V., 2024. DEVELOPMENT OF A MACHINE VISION AND SPRAYING SYSTEM OF A ROBOTIC SMART SPRAYER FOR PRECISION WEED MANAGEMENT IN SPECIALTY CROPS. UNIVERSITY OF FLORIDA.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Zhu Z., Ampatzidis Y., Silwal A., Pardalos P., 2022. Robust weed sprayers route planning using unmanned aerial vehicles. FASABE Annual Conference and Trade Show, Clearwater Beach, FL, May 19-22.


Progress 04/01/23 to 03/31/24

Outputs
Target Audience:Vegetable growers and allied industry; students and faculty, extension agents. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?One PhD student and one postdoc attended the ASABE (American Society of Agricultural and Biological Engineers) international ASABE conference in Omaha, Nebraska, USA, on July 8-12, 2023. They also improved their skills in image processing through a paid subscription to online courses. We also offered the Summer Undergraduate Research Experience (SURE) program (to provide undergraduate students with an extended experience through an 8-week program) to four students. The students work on several hands-on projects in collaboration with the PhD student and postdoc. PI Ampatzidis was able to attend several extension and outreach venues and presented the findings of this project (please see next section). How have the results been disseminated to communities of interest?Dr. Ampatzidis presented findings of this project in several extension, educational, and outreach venues including: Robotics, automation, and AI for specialty crops. Florida AgTech and AI Expo, Punta Gorda, FL, December 14, 2023. Steam weeding in sugarcane production - An alternative tool for weed management. Florida Crystals webinar, December 7, 2023. Potential technologies to maximize drone use. South Florida Veg Growers Meeting: Whitefly Management, Immokalee, FL, November 29, 2023. AI-enabled technologies for precision management of specialty crops. USDA ARS-Northeast Area retreat, November 14, 2023. Drones and AI for Precision Crop Management. Florida Ag Expo, Gulf Coast Research and Education Center, Wimauma, FL, November 9, 2023. Artificial intelligenceenhanced technology for precision citrus production. Cold Hardy Citrus Field Day, Quincy, FL, October 26, 2023. Commercializing your AI: Lessons and challenges from spinning off AI companies. UF AI Days, Gainesville, FL, October 18, 2023. Collaborative research: NRI: High throughput multi-robot weed management for specialty crops. Foundational Research in Robotics (FRR) -National Robotics Initiative (NRI) Principal Investigators' (PI) Meeting, Arlington, Virginia, May 1-3, 2023. Advancing Florida's Specialty Crop Production and Harvesting using Mechanization, Precision Agriculture, Drones, Robotics, and Artificial Intelligence. UF ABE Centennial Seminar Series, Gainesville, FL, April 20, 2023. Artificial Intelligence and precision agriculture in vegetable production. Citrus Show, Vegetable Session, Fort Pierce, April 13, 2023. UAVs, Sensors, & Other Applied Research Technology. Research Center Administrators Society (RCAS) Meeting, Gulf Coast REC, February 6, 2023. AI-enhanced smart machinery for precision scouting and spraying. National Alliance of Independent Crop Consultants (NAICC) 2023 Annual Meeting and AG PRO EXPO, Nashville, TN, January 23-27, 2023. AI-enhanced pest and disease management in vegetable production. South Florida Vegetable Growers Meeting: Research Updates on Weed and Disease Management. Immokalee, January 12, 2023. What do you plan to do during the next reporting period to accomplish the goals?In the next year, we will implement the updated machine vision system with the new camera and evaluate it for achieving high-speed detection at a high detection accuracy. We will also integrate the IMU with the GPS to improve the velocity readings for the robotic platform. We will evaluate the upgraded autonomous smart spraying system on vegetable beds and compare the spraying efficiency with conventional methods. The goal is to improve the spraying accuracy and achieve a low percentage of missed targets (aka., weeds) and off-target sprays using fine-tuning of the system.

Impacts
What was accomplished under these goals? For the machine vision and AI-based weed detection system (Obj. 1), we tested and installed a new industrial camera with software control and manual control of image capture parameters. We analyzed the image dataset (several weed categories and three crops) and performed class balance by collecting more images and by using image augmentation techniques. The new dataset was added to the old dataset and overrepresented and non-relevant classes were removed. The AI models were retrained using the latest YOLO v8 models (nano, small, and medium) and their performance was evaluated in all the classes. For the smart spraying system, experiments were conducted to evaluate the system's pressure fluctuation with pressure and flow rate variations. Three more pumps were added to the system based on the tests to supply a higher flow rate. An updated control system was designed and implemented to maintain a stable application pressure in the spraying system under nozzle activation. We also designed and performed experiments, in a low-cost setup, to analyze the spray area, spray length, and width variation with application pressure, spray height, wind, and vehicle speed, in order to select the best nozzles and configuration for the smart sprayer. The optimum range of application pressure, spray height, and vehicle speed was determined for a narrow cone nozzle. We developed a newer robotic platform with a larger wheelbase and four independent motorized wheels (Obj. 2). The electrical and electronic component layout was redesigned, and a new larger capacity battery and multi-step redundancies were added to provide multiple layers of electrical safety. The robotic spraying system was evaluated in vegetable fields. For thevehicle routing problem (Obj. 3), we further improved and evaluated the developedcapacitated arc routing model to treat various types of weeds using a fleet of autonomous vehicles in large fields. The uncertainty of weed distributions in the field is considered in this optimization. The weed distribution in the field is given by UAV collected data (from the weed detection model developed in the previous year). This model provides better results compared to the optimization models developed in previous years.

Publications

  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Vijayakumar V., Ampatzidis Y., Schueller J.K., Burks T., 2023. Smart spraying technologies for precision weed management: a review. Smart Agricultural Technology, 6, 100337, https://doi.org/10.1016/j.atech.2023.100337.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Ampatzidis Y., 2023. Emerging and advanced technologies in agriculture. Link (Linking Industry Networks through Certifications; High School Teachers Training) Conference, Daytona Beach, FL, October 10-12, 2023.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Ampatzidis Y., 2023. AI and Extension. Possibilities and Challenges. 2023 SR-PLN Middle Managers Conference, Next Generation: Evolving the Extension Enterprise, Orlando, FL, August 22-24.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Vijayakumar V., Ampatzidis Y., Silwal A., Kantor G., 2023. 2023. Development of a machine vision and spraying system of an autonomous robotic sprayer for specialty crops. ASABE Annual International Meeting, Omaha, Nebraska, USA, July 8-12, 2023.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Ampatzidis Y., 2023. Solutions to critical issues facing field and specialty crop production. Integrative Precision Agriculture  Local Solutions Through Global Advances International Conference, Athens, Georgia, May 18-19, 2023.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Ampatzidis Y., 2023. Agrifood systems in a Circular Economy Framework: Unlocking the Future. 11th Agrotechnology Conference, American Hellenic Chamber of Commerce, Thessaloniki, Greece, February 17, 2023.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Ampatzidis Y., 2023. AI-enhanced smart machinery for precision scouting and spraying. Annual Meeting and Ag Expo of the National Alliance of Independent Crop Consultants (NAICC), Nashville, TN, January 23-27, 2023.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Vijayakumar, V., Ampatzidis, Y., Silwal, A., Kantor, G., 2023. Specialty crop-specific robotic precision smart sprayer based on machine vision and PWM-controlled spraying system. 2nd AI in Agriculture Conference, April 17-19, 2023, Orlando, FL.


Progress 04/01/22 to 03/31/23

Outputs
Target Audience:Vegetable growers and allied industry; students and faculty, extension agents. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?One PhD student and one postdoc attended the Florida ASABE (American Society of Agricultural and Biological Engineers) and the international ASABE conference in Houston, TX. We also offered the Summer Undergraduate Research Experience (SURE) program (to provide undergraduate students with an extended experience through an 8-week program) to four students. The students work on several hands-on projects in collaboration with the PhD student and postdoc. PI Ampatzidis was able to attend several extension and outreach venues and presented the findings of this project (please see next section). How have the results been disseminated to communities of interest?Dr. Ampatzidis presented findings of this project in several extension, educational, and outreach venues including: AI in Health, Engineering, and the Sciences. Panel discussion at the UF AI Days event. Gainesville, FL, October 28, 2022. AI-enhanced smart farming. The 6th IEEE UV2022, International Conference on Universal Village, Virtual and main venue in Boston, USA, October 22-25, 2022 (Keynote Speaker). Robotics and artificial intelligence in Agriculture. ABE2012C: Intro to Biological Engineering (Guest Lecture), UF. October 12, 2022 AI-enhanced precision management in specialty crops. USDA National Agricultural Statistics Service, Research and Development Division (virtual seminar). September 29, 2022. Robotics and AI in specialty crop production. 4-H robotics team, Exploding Bacon (virtual seminar). September 26, 2022. Artificial intelligence in Agriculture. BSC2930 Frontiers in AI course (virtual lecture), UF, September 20, 2022. Introduction to Agricultural and Biological Engineering. Freshman Leadership Engineering Group (virtual event), UF, September 19, 2022. Remote sensing, AI, and automation in digital agriculture. International Smart Ag Meeting (virtual), China, July 15, 2022 How artificial intelligence can enhance precision agriculture and address climate change issues. UF/IFAS TREC Seminar Series, Homestead, FL, April 14, 2022. How artificial Intelligence can enhance agriculture production and address climate change issues. Volo Climate Correction Conference, Orlando, FL, March 17, 2022. Putting research and technological advances in the hands of stakeholders what we should do to convert research to practice (panel discussion). Envisioning 2050 in the Southeast: AI-driven innovations in agriculture. Auburn, AL, March 9-11. Emerging technologies and AI in precision agriculture. UF/IFAS In-Service training 32072, ANR-AIEET IST Module 1 (virtual lecture), February 28, 2022. Artificial intelligence for precision agriculture. UF/IFAS In-Service training 32032, New Technology for Commercial Crop Production (virtual lecture), February 23, 2022. AI applications in horticulture. Guest Lecture, Hort 3213 Fruit and Nut Production, Oklahoma State University, February 16, 2022. Artificial intelligence and smart farming. UF ABE Biocomplexity Engineering group seminars (webinar), January 18, 2022. What do you plan to do during the next reporting period to accomplish the goals?In the next year, we plan to train the weed classification models with different networks to improve detection accuracy. The goal is to identify the best possible AI model(s) after comparing more algorithms and networks. We will also re-design the PWM-controlled spraying system to improve efficiency. We will test new nozzles and conduct spray characteristic tests in a controlled environment and in the field. We have already developed an experimental design to evaluate the integrated system on vegetable fields. Finally, we will Integrate the smart spraying system with the autonomous robot developed by our Co-PI (Carnegie Mellon University) and evaluate it in vegetable fields.

Impacts
What was accomplished under these goals? For the machine vision and AI-based weed detection system (Obj. 1), we trained the classification model with new data and compared two state-of-the-art object detection algorithms (YOLOv7 and Faster RCNN) and determined the performance of each for each class under consideration. We have also built a new remote-controlled prototype robot for initial testing (Obj. 2). This prototype is equipped with the spraying system, control box, pumps, camera, and GPS to perform field trials. For thevehicle routing problem (Obj. 3), we evaluated the developedcapacitated arc routing model to treat various types of weeds using a fleet of autonomous vehicles in large fields. The uncertainty of weed distributions in the field is considered in this optimization. The weed distribution in the field is given by UAV collected data (from the weed detection model developed in the previous year). This model provides better results compared to the optimization models developed in previous years.

Publications

  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Vijayakumar V., Ampatzidis Y., Silwal A., Kantor G., Burks T., 2022. Machine vision and AI-equipped multi-crop robotic smart sprayer for precision weed management of three weed types in vegetable crops. FASABE Annual Conference and Trade Show, Clearwater Beach, FL, May 19-22.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Vijayakumar V., Partel V., Ampatzidis Y., Silwal A., Kantor G., 2022. Precision weed management based on machine vision and AI integrated to an autonomous robotic smart sprayer for specialty crops. ASABE Annual International Meeting, Houston, Texas, July 17-20.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Vijayakumar V., Costa L., Ampatzidis Y., 2022. AI applications in Smart Agriculture (poster). UF/IFAS AI Summit, Gainesville, FL, June 21, 2022.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Vijayakumar V., Ampatzidis Y., Silwal A., Kantor G., 2022. Machine vision and AI precision weed management using an integrated autonomous robotic smart sprayer for specialty crops. Envisioning 2050 in the Southeast: AI-driven innovations in agriculture. Auburn, AL, March 9-11 (poster).
  • Type: Journal Articles Status: Under Review Year Published: 2023 Citation: Vijayakumar V., Ampatzidis Y., Schueller J.K., Burks T., 2023. Smart spraying technologies for precision weed management: a review. Computers and Electronics in Agriculture.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Vijayakumar V., Ampatzidis Y., Silwal A., Kantor G., 2023. Specialty crop-specific robotic precision smart sprayer based on machine vision and PWM-controlled spraying system. AI in Agriculture Conference: Innovation and Discovery to Equitably meet Producer Needs and Perceptions, Orlando, FL, April 17-19, 2023.


Progress 04/01/21 to 03/31/22

Outputs
Target Audience:Vegetable growers and allied industry; students and faculty, extension agents. Changes/Problems:Because of Covid-19 and supply chain issues, there were several delays on material and equipment delivery. What opportunities for training and professional development has the project provided?Two PhD students and one postdoc attended seminars offered by NVIDIA on the use of embedded programming for real time AI applications. They also submitted three papers and attended the ASABE (American Society of Agricultural and Biological Engineers) virtual conference in July 12-16, 2021. We also offered the Summer Undergraduate Research Experience (SURE) program (to provide undergraduate students with an extended experience through an 8-week program) to four students. The students work on several hands-on projects in collaboration with the PhD students and postdoc. PI Ampatzidis was able to attend several extension and outreach venues and presented findings of this project (please see next section). How have the results been disseminated to communities of interest?Dr. Ampatzidis presented findings of this project in several extension, educational, and outreach venues including: Emerging technologies for specialty crops. LaBelle Rotary Club, LaBelle Fl, November 16, 2021. Smart and data-driven farming. ABE2012C: Intro to Biological Engineering (virtual lecture), October 6, 2021. How artificial intelligence can enhance precision agriculture. Institute for Learning in Retirement, Life-Long Learning programs at Oak Hammock (virtual lecture), October 5, 2021. Artificial intelligence in agriculture. UF ABE Florida Farm Bureau Board, UF ABE, Gainesville, September 22, 2021. Smart and data-driven agriculture. UF Frontiers of AI (virtual lecture), September 21, 2021. Artificial intelligence in crop production. UF Foundation National Board Assembly (virtual event), September 17, 2021. Emerging technologies for precision management in vegetables. SWFREC Vegetable seminar, Immokalee, May 28, 2021. AI applications for Florida crop production. AFIS AI Forum, Virtual Seminar, May 26, 2021. Emerging Technologies and AI for BMP. UF/IFAS BMP Virtual Summit. May 18, 2021. Artificial Intelligence in Agriculture. UF AI Town Hall, May 13, 2021. Artificial intelligence for precision agriculture applications. UF/IFAS Extension Symposium, May 3-7, 2021. Smart sprayer utilizing sensor fusion and AI. Ag Tech Expo, Immokalee, April 9, 2021. Artificial Intelligence in Agriculture. Florida Senators, UF President, and Board of Trustees. February 25, 2021. Emerging Technologies and AI for BMP. UF/IFAS Water Wednesdays. February 24, 2021. Artificial Intelligence in Agriculture. UF EGN1935-FOAI (31730) - Freshman Engineering: Frontiers of AI (virtual lecture). February 9, 2021. Applications of Artificial Intelligence in Precision Agriculture. Central District Ag BMP virtual meeting. February 3, 2021. Drones, artificial intelligence, and the future of pest management in vegetable crops. Annual vegetable growers virtual meeting. The Oregon processed vegetable commission. January 25, 2021. AI in agriculture. UF/IFAS Extension Associate Deans AI Retreat, January 12, 2021. What do you plan to do during the next reporting period to accomplish the goals?In the next year, we plan to collect more images of weeds and plants during the initial growing phases to balance the dataset and build a customized weed detection model. We plan to improve the weed detection model's precision and recall to >90%. For the smart sprayer, activities will include the improvement of the spraying resolution/accuracy. Several nozzles are tested for this purpose. We plan to evaluate the integrated system during Fall 2022. For Objective 3, we plan to continue working on Tasks 1-4. For Task 3, we plan to incorporate the more recent weed distribution information estimated from the operating vehicles into the model. We will explore appropriate modeling approach such as online optimization and design efficient solution methodologies.

Impacts
What was accomplished under these goals? For the machine vision and AI-based weed detection system (Obj. 1), we have collected 3,018 images of three different weed types and target plants (dataset of 5,662 high resolution images total). An initial AI-based weed detection model was created on these images. To develop a unmanned aerial vehicle (UAV) and AI-based weed detection, classification, and distribution system, we collected data at 35, 60, and 100 ft from three different locations. Weed distribution estimation using image processing techniques applied to these images. This information will be used for the fleet optimization model (Obj. 3) Furthermore, we improved the sprayer's functionality and debugged the CAN BUS communication issues. For thevehicle routing problem (Obj. 3), we previously developeda capacitated arc routing model to treat various types of weeds using a fleet of autonomous vehicles in large fields. In this year, we further consider the uncertainty of weed distributions in this problem.We first provide a linear vehicle routing model to formulate the problem with deterministic weed distributions. Based on this model, we propose a robust optimization model to incorporate the uncertainty of weed distributions by using a set, call uncertainty set, that includes all possible realizations of weed distributions. The objective of the robust model becomes seeking a route feasible to all weed distribution realizations in the uncertainty set with the minimum cost. We utilizethe estimation of weed distributions fromUAVsin the construction of uncertainty sets. Numerical results demonstrate the need of using the robust approach and show that the proposed model leads to a better out-of-sample performance.

Publications

  • Type: Book Chapters Status: Published Year Published: 2022 Citation: Kakarla S.C., Ampatzidis Y., Park S., Adosoglou G., and Pardalos P., 2022. Emerging sensing technologies for precision agriculture. Book: Information and Communication Technologies for AgricultureTheme I: Sensors. Springer Nature, ISBN: 978-3-030-84143-0.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2022 Citation: Zhu Z., Ampatzidis Y., Silwal A., Pardalos P., 2022. Robust route planning for autonomous vehicles. 2022 ASABE Annual International Meeting, July 17-20, 2022
  • Type: Other Status: Submitted Year Published: 2022 Citation: Vijayakumar V., Ampatzidis Y., Silwal A., Kantor G., 2022. Machine vision and AI precision weed management using an integrated autonomous robotic smart sprayer for specialty crops. Envisioning 2050 in the Southeast: AI-driven innovations in agriculture. Auburn, AL, March 9-11 (poster).
  • Type: Conference Papers and Presentations Status: Submitted Year Published: 2021 Citation: Adosoglou G., Park S., Ampatzidis Y., Pardalos P., 2021. A high-level task planning of autonomous robots with multi-dimensional loading constraints for precision weed management under field variability. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021, ASABE Paper No. 2100426, doi: 10.13031/aim.202100426.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Vijayakumar V., Partel V., Ampatzidis Y., Silwal A., Kantor G., 2021. Autonomous smart sprayer for precision weed management using machine vision and AI. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Partel V., Costa L., Ampatzidis Y., 2021. Smart citrus tree sprayer using sensor fusion and artificial intelligence. 22021 Virtual ASABE Annual International Meeting, July 11-14, 2021, Paper Number: 2100525, doi:10.13031/aim.202100525.


Progress 04/01/20 to 03/31/21

Outputs
Target Audience:Vegetable growers and allied industry; students and faculty, extension agents. Changes/Problems:Because of Covid-19, the University of Florida's Southwest Florida Research and Education Center was closed for several months, and we were not able to offer the Summer Undergraduate Research Experience (SURE) program (to provide undergraduate students with an extended experience through an 8-week program). We plan to offer this project in Years 2-4. What opportunities for training and professional development has the project provided?In the first year of this project, two PhD students were hired at University of Florida to work on this project. The students were able to attend seminars offered by NVIDIA on the use of embedded programming for real time AI applications. They have also submitted two papers and plan to attend the ASABE (American Society of Agricultural and Biological Engineers) virtual conference in July 12-16. PI Ampatzidis was able to attend several extension and outreach venues and presented findings of this project (please see next section). How have the results been disseminated to communities of interest?Dr. Ampatzidis presented findings of this project in several extension, educational, and outreach venues including: Artificial Intelligence in Agriculture. Florida Senators, UF President, and Board of Trustees. February 25, 2021. Emerging Technologies and AI for BMP. UF/IFAS Water Wednesdays. February 24, 2021. Artificial Intelligence in Agriculture. UF EGN1935-FOAI (31730) - Freshman Engineering: Frontiers of AI (virtual lecture). February 9, 2021. Applications of Artificial Intelligence in Precision Agriculture. Central District Ag BMP virtual meeting. February 3, 2021. Drones, artificial intelligence, and the future of pest management in vegetable crops. Annual vegetable growers virtual meeting. The Oregon processed vegetable commission. January 25, 2021. AI in agriculture. UF/IFAS Extension Associate Deans AI Retreat, January 12, 2021. Smart technologies and AI for best management practices. IFAS Certified Crop Advisory (CCA) CEU training program. Theme: Crop Management and Soil & Water. Recorded presentation via the UF CANVAS, November 2020. Moderator and in the Roundtable at the Florida-Israel Agriculture Innovation Summit (Ag Summit for robotics, drones, and AI research), Webinar, November 18, 2020. UAVs and AI in Agriculture. Lecture at Precision Agriculture Class, University of Missouri, Webinar, November 3,2020. AI and emerging technology in horticulture. IST at the FSHS Conference, Webinar, October 20, 2020. AI applications in agriculture. UFInformaticsInstitute, Webinar, October 14, 2020. AI in Agriculture. ABE2012c-Introduction to Biological Engineering. Guest Lecture, September 30, 2020. AI applications in agriculture. UPL North America R&D Innovation Webinar. September 29, 2020. UAV-based plant nutrient analysis and a novel smart spraying technology. Virtual Citrus Meeting, September 15, 2020. AI in Agriculture. UF ABE Webinar "HWCOE Artificial Intelligence Initiative Vision", Round Table, September 11, 2020. Precision and smart spraying technology for vegetables. Vegetable Growers Meeting, SWFREC, Immokalee, August 20, 2020. Emerging technologies for vegetables. Vegetable and Specialty Crop Virtual Meeting, August 13, 2020. Precision and variable rate spraying and fertilizer applications. Virtual Ag BMP summit (Webinar), June 16, 2020. Emerging technologies for precision management in vegetables. SWFREC Vegetable Webinar, May 28, 2020. Partel V., and Ampatzidis Y., 2020. Machine vision and deep learning techniques for precision weed management. Precision Agriculture Webinar Series, Alabama Extension, May 21, 2020. What do you plan to do during the next reporting period to accomplish the goals?In Year-2, we plan to develop and in-field evaluate the first prototype of the autonomous and smart spraying vehicle. The first prototype of the smart spraying system (attached on an ATV) has already been developed. The machine vision and AI-based system will be developed further to increase weed detection and spraying accuracies. We also plan to develop further the intelligence multi-robot coordination system and optimize spraying applications. Finally, we will start collecting data to develop a feasibility study for the proposed autonomous spraying system.

Impacts
What was accomplished under these goals? Most available conventional sprayers apply agrochemicals uniformly, even though distribution of weeds is typically patchy, resulting in wastage of valuable compounds, increased costs, crop damage risk, pest resistance to chemicals, environmental pollution, and contamination of produce. Minimizing the negative impacts of agrochemicals (and spraying technologies) is a major global societal challenge. This project tackles the above problem by developing an autonomous multi-crop robotic platform equipped with a machine vision and artificial intelligence (AI) based precision sprayer to distinguish target weeds from non-target objects (e.g., vegetable crops) and precisely spray on the desired target. The precision sprayer is targeted to simultaneously treat 3 different types of weeds -vine weeds, broadleaf, and grass weeds- for specialty crops such as tomato and pepper. The first prototype was developed and utilizes 22 spray nozzles controlled by 12V solenoid valves, with a response time of less than 50 ms, covering a total work length of 1.3 m. A combination of a LUCID Triton 5.0 MP RGB camera with an intel RealSense depth camera (D435) was used for capturing RGB images and depth information. The images were processed by an NVIDIA Xavier GPU running a YOLO V4 object detector, which is a state-of-the-art open source neural network. This smart spraying system achieved an overall precision of 71% and a recall of 78% (for plant detection and target spraying accuracy) when tested with real plants, and 91% accuracy and recall with artificial plants. The next prototype is expected to achieve a significantly higher overall precision and recall and would be integrated to the autonomous robotic platform, making it a novel technology in precision weed management. The autonomous robotic platform is under development and it will use RTK-GPS and LIDAR for navigation in the field. We are also currently investigating a combination of end-to-end deep learning based steering regressor with Generative Adversarial Network (GAN) for autonomous navigation. Furthermore, a task planning algorithm was developed to optimize spraying application for the fleet of weeding robots. This optimization algorithm considers multiple vehicles carrying multiple types of herbicides to treat various types of weeds in a field by taking into account the need to replenish, as well as the time cost associated with it and the variability of the field. We formulated this problem as an extension of the capacitated arc routing problem (CARP), where we considered multiple trips to and from the refilling unit, the time required for replenishment, as well as the multiple commodities being transferred. We developed two mathematical models, one in which optimizes only for the total distance traveled among all vehicles, while the second model also optimizes the maximum time spent for completing the task. We demonstrated solutions to vehicle routing problems for a weed treatment operation using three types of agrochemicals and a fleet of autonomous precision sprayers.

Publications

  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2021 Citation: Vijayakumar V., Partel V., Ampatzidis Y., Silwal A., Kantor G., 2021. Autonomous smart sprayer for precision weed management using machine vision and AI. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021.
  • Type: Book Chapters Status: Accepted Year Published: 2021 Citation: Kakarla S., Ampatzidis Y., Park S., Adosoglou G., Pardalos P., 2021. Emerging technologies for precision agriculture. Information and Communication Technologies for Agriculture  Theme I: Sensors, EFITA. Springer Nature, Switzerland.
  • Type: Other Status: Other Year Published: 2020 Citation: Partel V., and Ampatzidis Y., 2020. Machine vision and deep learning techniques for precision weed management. Precision Agriculture Webinar Series, Alabama Extension, May 21, 2020 (invited talk).
  • Type: Other Status: Other Year Published: 2020 Citation: Ampatzidis Y., 2020. Emerging technologies for precision management in vegetables. SWFREC Vegetable Webinar, May 28, 2020 (invited talk).
  • Type: Other Status: Other Year Published: 2020 Citation: Ampatzidis Y., 2020. Precision and variable rate spraying and fertilizer applications. Virtual Ag BMP summit (Webinar), June 16, 2020 (invited talk).
  • Type: Other Status: Other Year Published: 2020 Citation: Ampatzidis Y., 2020. Emerging technologies for vegetables. Vegetable and Specialty Crop Virtual Meeting, August 13, 2020 (invited talk).
  • Type: Other Status: Other Year Published: 2020 Citation: Ampatzidis Y., 2020. Precision and smart spraying technology for vegetables. Vegetable Growers Meeting, SWFREC, Immokalee, August 20, 2020 (invited talk).
  • Type: Other Status: Other Year Published: 2020 Citation: Ampatzidis Y., 2020. AI applications in agriculture. UPL North America R&D Innovation Webinar. September 29, 2020 (invited talk).
  • Type: Other Status: Other Year Published: 2020 Citation: Ampatzidis Y., 2020. AI applications in agriculture. UF Informatics Institute, Webinar, October 14, 2020 (invited talk).
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2021 Citation: Adosoglou G., Park S., Ampatzidis Y., Pardalos P., 2021. A high-level task planning of autonomous robots with multi-dimensional loading constraints for precision weed management under field variability. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021.
  • Type: Other Status: Other Year Published: 2020 Citation: Ampatzidis Y., 2020. AI and emerging technology in horticulture. IST (In Service training) at the FSHS Conference, Webinar, October 20, 2020 (invited talk).
  • Type: Other Status: Other Year Published: 2020 Citation: Ampatzidis Y., 2020. Smart technologies and AI for best management practices. IFAS Certified Crop Advisory (CCA) CEU training program. Theme: Crop Management and Soil & Water. Recorded presentation via the UF CANVAS, November 2020 (invited talk).
  • Type: Other Status: Other Year Published: 2021 Citation: Ampatzidis Y., 2021. Drones, artificial intelligence, and the future of pest management in vegetable crops. Annual vegetable growers virtual meeting. The Oregon processed vegetable commission. January 25, 2021 (invited talk).
  • Type: Other Status: Other Year Published: 2021 Citation: Ampatzidis Y., 2021. Applications of Artificial Intelligence in Precision Agriculture. Central District Ag BMP virtual meeting. February 3, 2021 (invited talk).
  • Type: Other Status: Other Year Published: 2021 Citation: Ampatzidis Y., 2021. Emerging Technologies and AI for BMP. UF/IFAS Water Wednesdays. February 24, 2021 (invited talk).