• Bio

  Dr. Meha Jain is an Associate Professor at the University of Michigan’s School for Environment and Sustainability. She combines satellite remote sensing, machine learning, geospatial analysis, and socioeconomic data to examine how environmental change affects agricultural production and how farmers adapt to climate and resource constraints. Her research, published in leading journals including Science, Science Advances, Nature Sustainability, and Environmental Research Letters, has been supported by multiple NASA grants. Dr. Jain received the inaugural 2026 ASU–Science Prize for Transformational Impact for her research using satellite imagery and machine learning to support smallholder farmers facing climate stress. Her honors also include the 2024 American Geophysical Union Early Career Award in Global Environmental Change and the S&R Evermay Kuno Award for Applied Science for the Social Good. She earned her Ph.D. from Columbia University and her B.A. from Princeton University.

• Bio

  Dr. Song Li is a Professor of Plant Genomics and Bioinformatics at Virginia Tech and a faculty member of the Sanghani Center for Artificial Intelligence and Data Analytics. His research integrates artificial intelligence, machine learning, and genomics to advance translational plant science. His group develops computational approaches for cross-species single-cell analysis, gene regulatory modeling, transcription factor binding prediction, and image-based cellular phenotyping. A central goal of his research is to translate biological knowledge from well-studied model plants to diverse crop and non-model species. Dr. Li has led interdisciplinary projects supported by NSF, DOE, and USDA and received Virginia Tech’s Research Excellence Award and the School of Plant and Environmental Sciences Outstanding Basic Research Award.

• Bio

  Dr. Steven Mirsky is Director of Digital Agriculture for USDA-ARS, leading national efforts to advance artificial intelligence, computer vision, and digital agriculture technologies. He develops scalable solutions that translate agricultural research into practical tools for farmers, ranchers, breeders, and conservation professionals. His work has helped establish national programs and partnerships focused on phenotyping, precision agriculture, and agricultural innovation. Through DASH and related initiatives, he is helping build the digital infrastructure needed to unlock precision agriculture. 

• Abstract

  The Digital Agriculture Systems Hub (DASH) is a USDA-ARS initiative focused on accelerating the development and deployment of computer vision and artificial intelligence across plant science and breeding, and for farmers and ranchers. This presentation will provide an overview of DASH, its mission, and the digital infrastructure being developed to support scalable phenotyping, precision agriculture, and decision-support tools. Open image repositories, sensing technologies, data pipelines, and machine learning workflows to transform agricultural data into deployable solutions will be introduced. Examples will highlight collaborations across USDA and partner organizations that are advancing crop, soil, pest, and conservation applications. The presentation will conclude with a vision for national-scale deployment of computer vision technologies to support researchers, producers, and conservation professionals.

• Bio

  Dr. Song Gao is an H.I. Romnes Distinguished Professor in Geographic Information Science at the University of Wisconsin-Madison, where he leads the Geospatial Data Science Lab. He is an affiliated faculty member of the Data Science Institute and the Department of Computer Science. He received his PhD degree from University of California, Santa Barbara. He was elected as a Fellow of the American Association of Geographers (AAG) in 2024. His main research interests include Geospatial Data Science and GeoAI. His research was published in top journals and conferences such as Science, PNAS, Nature Communications, and NeurIPS. He is the PI of multiple research grants from the NSF, Microsoft AI for Earth, Google DeepMind, and other industry partners. He currently serves as the Associate Editor of IJGIS.  Dr. Gao was the recipient of the UCGIS Early/Mid-Career Research Award and among the Web of Science Top 1% highly cited researchers list.

• Bio

  Rattan Lal, Ph.D., is a Distinguished University Professor and Director of the Lal Carbon Center at The Ohio State University. He was President of the Soil Science Society of America (2006-2008) and the International Union of Soil Sciences (2017-2018). He researches soil carbon sequestration, regenerative agriculture, soil physical processes and properties. With an h-index of 209 and 180,000 citations, Dr. Lal has authored about 1200 journal articles, authored/edited 109 books, authored 590 book chapters, and mentored 430 researchers from about 60 countries. He is laureate of the 2018 GCHERA World Agricultural Prize, 2018 Glinka World Soil Prize 2019 Japan Prize, 2020 World Food Prize,2020 Arell Innovation prize, 2021 Padma Shri Award of India, and 2024 Gulbenkian Prize for Humanity. He donated all the Honoria to the Ohio State University for which the Dr. Rattan Lal Endowed Professorship has been created and implemented.

• Abstract

  Atmospheric concentration of CO2, CH4 and N2O has increased since the onset of agriculture which involved conversion of natural to agro-ecosystems. Whereas agriculture -related activities started about 10 millennia ago, the magnitude of emission from agro-ecosystems increased with the onset of Industrial Revolution since circa 1750. In addition to land conversion for arable farming, domestication of livestock and cultivation of rice paddies aggravated the emission of CH4. Recycling  of organics to enhance soil fertility and input of chemical fertilizers since the World War 2 aggravated emission of N2O. Growing demands of the ever-increasing and progressively affluent world population necessitate that agriculture be transformed, from being a problem in relation to its adverse effects on environment, to a part of the solution. Soil of agro-ecosystems (covering about 38% of the Earth’s land area and using 70% of all freshwater withdraw) is depleted of its carbon stock. The latter consists of soil organic C (SOC) in humid environments and mostly soil inorganic C (SIC) in arid environments. Depletion of soil C stock has degraded soil quality, polluted  water, dwindled biodiversity, accelerated anthropogenic climate change, and aggravated under nutrition and malnutrition. Thus, re-carbonization of the terrestrial biosphere, through use of artificial intelligence (AI) for using soil-specific best management practices (BMPs), is a win-win option. A judicious use of AI for adopting BMPs and returning agriculturally marginal land to nature is also critical to advancing Sustainable Development Goals of the Agenda 2030 of the United Nations. Rewarding farmers for sequestering C in land-based sinks through payments for ecosystem services ($50 per  credit or one metric ton or Mg of CO2eq  may be facilitated by implementation of Soil Health Act at local, state, national and international level. There is also a strong need to update educational curricula from the kinder garden to the secondary school and beyond. Promoting home gardening is another useful option with the aim of “one home one garden” to reduce fuel cost of long distance food transport.

  • View/Download Flyer (PDF)

• Bio

  Dharmendra Saraswat is a Professor of Agricultural and Biological Engineering at Purdue University and serves as the Research Mission Leader of the Institute for Digital and Advanced Agricultural Systems (IDAAS). Prior to joining Purdue, he held academic and research positions at the University of Arkansas Cooperative Extension Services, the Indian Council of Agricultural Research (ICAR)–Central Institute of Agricultural Engineering in Bhopal, and Chandra Sekhar Azad University of Agriculture and Technology in Kanpur, India. He completed his graduate training at The Ohio State University and ICAR–Indian Agricultural Research Institute, New Delhi, India, and earned his undergraduate degree from the University of Allahabad. His research, education, and outreach focus on advancing digital agriculture by addressing challenges in data usability, actionable analytics, and farmer adoption. Through technical innovation, tool development, scholarly leadership, and global engagement, he integrates engineering, plant science, and computing to develop practical, data-driven agricultural solutions and train the next generation of agricultural data scientists.

• Bio

  Dr. Lirong Xiang is an Assistant Professor in the Department of Biological and Environmental Engineering at Cornell University. She was previously an Assistant Professor in the Department of Biological and Agricultural Engineering at NC State University, with affiliations in the Department of Electrical and Computer Engineering and the NC Plant Sciences Initiative. Dr. Xiang received her Ph.D. in Agricultural and Biosystems Engineering from Iowa State University and her B.S. in Biosystems Engineering from Zhejiang University. She currently serves as a guest editor for Computers and Electronics in Agriculture, as an associate editor for the Journal of the ASABE, and as an associate editor for The Plant Phenome Journal. Dr. Xiang’s research interests include robotics, computer vision, and machine learning, with a focus on developing innovative and sustainable solutions for crop production, plant health monitoring, and agricultural system optimization.

• Abstract

  Advances in artificial intelligence and robotics are creating new opportunities to transform precision agriculture by enabling more timely, accurate and scalable crop monitoring and intervention. In this talk, Lirong Xiang, an assistant professor in the Department of Biological and Environmental Engineering at Cornell University, presents research from the Automation and Robotics Lab. Her work integrates AI-driven perception with field-deployable robotic systems to address key challenges in crop production and plant health management. By enabling early and accurate detection of diseases and weeds, as well as targeted, data-driven interventions, Xiang’s research supports improved crop yields, reduced labor and input costs and increased resilience of agricultural production systems.

  • View/Download Flyer (PDF)

Archive

• 2025

  Date	Speaker Name	Title/Recording
  DEC 3	Dr. Glen Rains Professor of Entomology and Agricultural Engineering, University of Georgia	Smart Farming in Action: Turning Agricultural Data into Intelligent Decisions This presentation highlights the University of Georgia’s 4D (Data-Driven and Digital Demonstration) Farm Project, a 90-acre research and demonstration farm designed to show how digital technologies and artificial intelligence can transform agricultural management from reactive to predictive decision-making. The project integrates data collection from multiple sources—farm operations, IoT sensors, aerial imagery, and manual scouting—to create a continuous data pipeline that supports in-season forecasting and decision-support tools. These tools help farmers manage irrigation, plant growth regulation, and yield optimization with precision. The overall goal is to convert raw data into actionable insights, enabling farmers to make informed management decisions proactively rather than reactively. The project demonstrates the importance of integrated data systems and AI-based forecasting for creating Decision-Assistant Tools—digital agents that advise farmers on what actions to take and when for more sustainable and profitable operations.
  OCT 1	Dr. Nicholas Pfeifer Registered Patent Attorney, Smith & Hopen Patent and Trademark Attorneys	Artificial Intelligence and Intellectual Property: Navigating the New Frontier Artificial intelligence (AI) is reshaping research, business, and innovation at a pace that challenges traditional intellectual property (IP) frameworks. From AI-generated inventions to the use of AI as a tool in creative and technical fields, researchers, entrepreneurs, and institutions face new questions: Who owns AI-created output? How can innovations involving AI be protected? Where do copyright, patents, trademarks, and trade secrets apply—and where do they fall short? In this presentation, Artificial Intelligence and Intellectual Property: Navigating the New Frontier, Nicholas Pfeifer provides an accessible introduction to the evolving relationship between AI and IP. Recording
  SEPT 25	Dr. Yang Zhao Associate Professor of Animal Science, UT AgResearch Guthrie Endowed Professor of Precision Livestock Farming (PLF) - University of Tennessee	Precision Livestock Farming for Better Poultry Production and Welfare The global poultry industry is rapidly expanding to meet increasing demand for affordable animal protein, but it faces significant challenges including labor shortages, resource limitations, and resilience to disease outbreaks. At the same time, consumer expectations for higher animal welfare and sustainability standards are placing additional pressures on poultry producers. Addressing these challenges requires transformative solutions that enhance production efficiency, sustainability, and welfare. This presentation will introduce Precision Livestock Farming (PLF), with particular emphasis on its applications in the poultry industry. PLF integrates advanced sensing technologies, automation, and AI-driven analytics to enable real-time monitoring of birds, which is the core of the production. Such systems support timely management decisions that may improve animal welfare, production efficiency, and resource utilization. By integrating PLF into modern production systems, the poultry industry can move toward a more sustainable, resilient model capable of meeting future global food demands. Recording
  APR 25	Dr. Jaehyun Ahn Assistant Professor, Artificial Intelligence in Extension, UF/IFAS Agricultural Education and Communication Dr. Golmar Golmohammadi Assistant Professor, Hydrology and Water Quality, UF/IFAS Range Cattle Research & Education Center	Mapping AI Adoption in Florida Extension: Who’s Ready, Who’s Connected, and What Comes Next Speaker: Dr. Jaehyun Ahn This spring, 154 UF/IFAS Extension agents participated in a statewide survey examining their perceptions, use, and social influence toward artificial intelligence (AI). Jaehyun “Jae” Ahn, a UF/IFAS assistant professor of AI Extension in agricultural education and communication, discusses his new study. It will offer a robust snapshot of current AI readiness for Extension programs across Florida. Through a mixed-methods approach, this training will explore how AI use and confidence vary across UF/IFAS faculty, who are positioned to influence others at UF/IFAS, what tools Extension agents are already trying, and how we can move from isolated adoption to peer-led transformation. AI-Driven Approaches for Hydrological Forecasting and Flood Susceptibility Analysis in Florida Speaker: Dr. Golmar Golmohammadi As part of her research, Golmar Golmohammadi, a UF/IFAS assistant professor of soil, water, and ecosystem sciences explores the potential of artificial intelligence to enhance the forecasting of water resource variables at both field and regional scales. Accurate prediction of hydrological and water quality parameters is critical for effective water resource management, particularly in regions like Florida, where water systems are integral to the environment and economy. Her research demonstrates the promise of advanced AI methodologies in addressing complex water resource challenges and presents Florida-based case studies as illustrative examples. Recording
  MAR 26	Dr. Yiannis Ampatzidis Associate Professor, Department of Agricultural and Biological Engineering Dr. Xiaoya Zhang Assistant Professor, Family, Youth, and Community Sciences	Integrating AI and Robotics for Enhanced Crop Management Speaker: Dr. Yiannis Ampatzidis Advancements in robotics and AI are revolutionizing specialty crop management, enabling more sustainable and efficient agricultural practices. Yiannis Ampatzidis, a UF/IFAS associate professor of agricultural and biological engineering at the Southwest Florida Research and Education Center, talks about cutting-edge technologies such as Agroview, Agrosense, and robotic systems that drive precision agriculture. Agroview provides actionable insights into canopy size, plant health, nutrient deficiencies, and spatial variability. These insights enable targeted interventions, such as prescription maps for nutrient optimization in citrus production. Agrosense complements Agroview as an AI-enabled ground-based sensing system for orchard management, offering capabilities like tree crop counting, canopy density classification, tree height estimation, and fruit counting. Advancing Youth Mental Health Intervention Effectiveness with AI Methods Speaker: Dr. Xiaoya Zhang There is growing concern about the prevalence of depression and anxiety among adolescents. Mental health prevention programs tend to adopt a one-size-fits-all approach, resulting in limited effectiveness. Some individuals benefit significantly; others don’t. Therefore, there is a pressing need to enhance the precision of mental health prevention efforts. Xiaoya Zhang, a UF/IFAS assistant professor in family, youth, and community sciences, addresses this by identifying factors that moderate the effects of a family-centered, web-based intervention program on adolescents’ emotional problems -- using machine-learning. This project will advance basic science by providing a more nuanced understanding of how intervention programs could promote youth mental health.
  MAR 14	Dr. Yunjun Xu Professor, Mechanical and Aerospace Engineering - University of Central Florida	Robotic Technologies and Control Algorithms for Agricultural Robots Field operations through robotic and AI technologies have become more and more important in the modern agricultural industry, particularly in specialty crops, which have the potential to address the pressing issues of labor shortages and cost increasing. In this talk, Dr. Yunjun Xu will focus on hardware platforms, mechanisms, and algorithms that have been developed in his group for field robot motion and control, multiple robots’ row allocation, harvesting, anomalies detection, etc. Meanwhile, he will introduce several AI technologies currently being studied in the UCF Expanding AI program. Recording
  FEB 19	Dr. Ziwen Yu Assistant Professor, Department of Agricultural and Biological Engineering Dr. Diana Taft Assistant Professor, UF/IFAS Food Science and Human Nutrition Department	Harnessing Generative AI for Surveying Farmers' Perceptions of Data Rights in Agriculture Speaker: Dr. Ziwen Yu Generative AI (GenAI) is increasingly used in computational social studies as a substitute for traditional human survey participants, addressing challenges like declining response rates, questionnaire complexity, and participant unpredictability while reducing bias and operational costs. In his presentation, Ziwen Yu, discusses a novel framework to survey farmers' perceptions of data rights in agriculture using GenAI models as "silicon subjects." His methodology incorporates a previous farmer survey on data ownership as a ground truth, ensuring demographic alignment between human respondents and GenAI-generated profiles. Logical reasoning is examined by analyzing key demographic influences, such as how farmers with different education levels perceive data technology adoption. Quantitative results are used for statistical alignment and to assess GenAI's capability in replicating human-like decision-making patterns. Leveraging Bioinformatic Tools to Identify the Relationship Between Breastfeeding and Infant Carriage of Resistant ESKAPE Pathogens Speaker: Dr. Diana Taft Antibiotic resistance is a major and growing public health challenge. Diana Taft, an assistant professor of food science and human nutrition, talks about ESKAPE pathogens, six organisms that can ‘escape’ antibiotics and lack effective treatment once antimicrobial resistance develops. Infants can carry higher levels of antimicrobial resistance genes than adults, which makes identifying the presence of ESKAPE pathogens in the infant gut microbiome of particular importance. New computational tools such as MGS2AMR have enabled scientists to study resistant organisms using metagenomic sequencing methods. Recording

   

• 2024

  Date	Speaker Name	Abstract/Recording
  DEC 4	Dr. Alina Zare Professor, Electrical and Computer Engineering Department - University of Florida	The Strength to Say “I Don't Know”: expanding neural networks to address out of distribution samples and ambiguous inputs Machine learning methods, and specifically neural networks, have shown impressive capabilities on a wide range of areas. However, these models are often brittle when seeing unexpected or novel data that differs in significant or meaningful ways from the data that was used to train them. Often, this is overcome with ever-increasing training data volumes. Alina Zare, professor of electrical and computer engineering and director of the Artificial Intelligence and Informatics Institute, discusses methods that her group has developed to flag out-of-distribution and address ambiguous inputs in test data. Recording
  NOV 22	Dr. Stefano Carpin Professor and Associate Dean for Research and Graduate Programs - University of California, Merced	Scaling Data Collection for Precision Agriculture: Challenges and Innovations The increasing demand for sustainable and resilient agricultural practices has fueled expectations that robotics and AI will play a pivotal role in revolutionizing the industry. While significant advancements have been achieved in both academic research and the corporate sector, numerous critical challenges remain unresolved. In this talk, Stefano Carpin, a professor in computer science and engineering and associate dean for research and graduate programs at the School of Engineering at the University of California, Merced, will present his team’s recent work on addressing one of these challenges -- developing efficient, scalable data collection methods for detecting and managing water stress in California high-value perennial crops. Carpin will discuss the computational and technical hurdles faced in this context and present some solutions that developed and deployed in the field. Finally, he will highlight promising new research directions aimed at reducing barriers to entry for end users, making these technologies more accessible and cost-effective for farmers and agricultural stakeholders.
  NOV 6	Dr. Manoj Karkee Professor and Director, Center for Precision and Automated Agricultural Systems, Biological Systems Engineering - Washington State University	AI and Robotics for Specialty Crops In this presentation, the author will first discuss the importance of AI-empowered precision and automated/robotic systems for the future of farming (Smart Farming, Ag 4.0). He will then summarize past efforts and current status of agricultural automation and robotics in fruit crops. For example, his work on apple harvesting robots achieved a picking rate of ~80% of apples in modern orchards taking about ~5.0 sec per fruit. His effort on robotic pollination of apple flowers has achieve a pollination success rate of 84% with a cycle time of 4.2 s. The presentation will conclude with an introduction of the novel robotic systems being developed in his program, and discussion on major challenges and opportunities in AI and robotics in agriculture and related areas including future directions in research and development.
  OCT 23	Dr. Shashi Shekhar McKnight Distinguished University Professor, Distinguished University Teaching Professor, ADC Chair - University of Minnesota.	AI-CLIMATE: National AI Research Institute for Climate-Land Interactions, Mitigation, Adaptation, Tradeoffs and Economy Shashi Shekhar, McKnight Distinguished University Professor in the Department of Computer Science and Engineering at the University of Minnesota and Director of AI-CLIMATE, the National AI Research Institute for Climate Smart Agriculture and Forestry, shared a presentation on AI-CLIMATE, which is aimed at advancing artificial intelligence by incorporating laws of nature and leveraging new nature-informed AI methods to curb climate effects while lifting rural economies. Recording
  AUG 28	Dr. Joel Harley Associate Professor - Department of Electrical and Computer Engineering, University of Florida	Integrating Data-Driven Machine Learning and Knowledge-Driven Analyses for Quantifying Ecosystem Services Ecosystem services provide the foundation for human well-being and economic development by supporting food production, clean water availability, climate regulation, and other vital functions. Quantifying ecosystem services helps us understand complex relationships between nature and human well-being. This presentation by Joel Harley demonstrates two avenues for creating hybrid machine learning systems. One approach utilizes the Decision Support System for Agrotechnology Transfer (DSSAT) to improve our ability to predict nitrogen levels in soil. The second utilizes ecological relationships between land use and other ecosystem services to improve broader ecosystem service quantification with remote sensing. We discuss how these frameworks can be best utilized as well as the challenges and opportunities associated with them. Recording
  MAY 10	Dr. Nikolay Bliznyuk Associate Professor of Statistics - Department of Agricultural and Biological Engineering, University of Florida	Statistical machine learning for improved detection and uncertainty quantification of pathogenic E. Coli in hydroponic irrigation water using impedimetric systems Reuse of alternative water sources for irrigation is a sustainable approach that can reduce water gaps, while increasing food production. However, when growing fresh produce, this practice increases the risk of bacterial contamination. Thus, rapid and accurate identification of pathogenic organisms such as E. coli is crucial for resource management. Dr. Nikolay Bliznyuk, a University of Florida associate professor of statistics, presented a statistical machine learning framework his team developed to predict and quantify uncertainty about E. coli concentration, which lets scientists quantify uncertainty about the bacterial concentration in a solution sample. Recording
  APR 22	Dr. Rohan L Fernando Professor Emeritus - Department of Animal Science, Iowa State University	How would Fisher use genomic information to determine the genetic contribution to continuous traits? In 1918, Ronald Fisher developed theory to demonstrate that under the assumption of Mendelian inheritance, one could determine the genetic contribution to continuous traits. In the absence of genomic information, he used pedigree relationships between individuals to determine the covariance between their genotypic values. In most of the current genetic analyses that utilize genomic information, the pedigree relationships are replaced by genomic relationships. In this talk, Rohan Fernando presented an alternative approach to study the genomic contribution to continuous traits that does not require making many of the usual assumptions such as random mating and linkage equilibrium. Recording
  APR 5	Dr. Ibrahim Volkan Isler Professor - Computer Science and Engineering, University of Minnesota	Top-down vs. Bottom-up Intelligence: A Robotics Perspective Over the past couple of decades, robotics research has mainly focused on transforming classical automation-style robots into intelligent machines that can operate in complex and unstructured environments with minimal human intervention. With recent advances in machine learning, the development of such intelligent robot systems is closer than ever to reality. In this talk, Dr. Volkan Isler gives an overview of robot systems that have been built by his group over the years. He also presents their recent work to combine top-down and bottom-up approaches to intelligence toward building general purpose robots that can operate robustly and safely in everyday environments.
  MAR 22	Dr. Xu "Kevin" Wang University of Florida, Gulf Coast Research and Education Center - Agricultural and Biological Engineering Dr. Sabine Grunwald University of Florida - Soil, Water and Ecosystem Sciences	AI-Powered Phenomics for Plant Breeding Presented by Xu "Kevin" Wang AI Modeling of Soil Carbon from Local to Global Scales Presented by Sabine Grunwald Recording
  FEB 23	Dr. Raquel Dias University of Florida - Microbiology and Cell Science	Integrative and Explainable AI for Early Detection of Multifactorial Autoimmune Diseases Recording

   

• 2023

  Date	Speaker Name	Abstract/Recording
  NOV 17	Dr. Henry Medeiros University of Florida - Agricultural and Biological Engineering Dr. Arnold Schumann University of Florida - Soil, Water, and Ecosystem Sciences	Applications of Generative Artificial Intelligence Models to Agriculture: Preliminary Studies on Heat Stress Estimation Using Aerial Images Presented by Dr. Henry Medeiros In this presentation, Henry Medeiros, an associate professor of agricultural and biological engineering explored the potential of generative artificial intelligence models to design soft sensors for agricultural applications. In practical terms crop heat stress depends on several environmental factors and has been estimated by using costly thermal aerial imaging. Medeiros presented preliminary results that demonstrate the capability of generative models to estimate heat stress using RGB (color) images. Novel Uses of Generative AI in Agricultural Machine Vision Presented by Dr. Arnold Schumann Generative AI heralds a novel approach for augmenting sparse datasets with synthetic images for mission-critical deep learning machine vision applications in agriculture. Convolutional artificial neural network (CNN) models used in deep learning image classification require large collections of labeled images for training. In this presentation, Arnold Schumann, a professor of soil, water and ecosystem sciences talked about how smartphone apps using CNNs to detect exotic plant diseases could help mobilize early responses and quarantines. Recording
  OCT 27	Dr. Rafael Munoz-Carpena University of Florida - Agricultural and Biological Engineering Dr. Ziwen Yu University of Florida - Agricultural and Biological Engineering	Convergence of Mechanistic Modeling and Artificial Intelligence in Hydrologic Science and Engineering (and Lessons for Other Fields) Our water system is complex, and studying it requires analyses of increasingly large data available from conventional and remote sensing and IoT sensor technologies. Some artificial intelligence applications lack the ability to address explicitly important hydrological questions. Rafael Munoz-Carpena, a professor of agricultural and biological engineering, presented four main types of hydrological problems based on their dominant space and time scales. He also identified important opportunities for AI and machine learning in hydrology. Data Ethics in AI Development and Implementation in Agriculture Given legal limitations, defining data as a property that can be owned is a tricky and difficult process, especially for farmers. Big companies, with professional legal teams, have already created aggressive contractual terms to protect their rights and obligations. However, farmers, lacking related knowledge and skills, may not realize the problem or, even if they do, may not know how to react. Recent efforts indicate that interpreting data rights from contractual terms is challenging. In his presentation, Dr. Ziwen Yu, a UF/IFAS assistant professor of agricultural and biological engineering, explained key elements regarding the protection of data rights, leveraging data value, and recognizing emerging data-derivative markets. Recording
  SEPT 29	Dr. Haipeng Yu University of Florida - Animal Science Dr. Charlie Li University of Florida - Agricultural and Biological Engineering	Development of User-Friendly, Open-Source Computer Vision Tools for Precision Livestock Farming Haipeng Yu, a UF/IFAS assistant professor of animal sciences and quantitative geneticist, uses computer vision (CV) to help livestock producers. Yu introduced ShinyAnimalCV, an open-source application that provides a user-friendly interface for performing CV tasks. Yu says CV technology optimizes decision-making through timely and individualized animal care. Affordable two- and three-dimensional camera sensors, combined with various algorithms, have provided opportunities to improve livestock production systems. Yu anticipates ShinyAnimalCV can contribute to CV research and teaching in the animal science community. Automation and Deep Learning to Advance Phenomics and Postharvest Handling Charlie Li, a UF/IFAS professor of agricultural and biological engineering, talked about sustainably intensifying agricultural production and food supply while preserving the environment. Li will go over multiple research projects that leverage agricultural robotics and deep learning to address challenges spanning the food chain — from breeding to harvest and postharvest handling. He presented a novel modular agricultural robotic system (MARS) that is an autonomous, multi-purpose, and affordable robotic platform for in-field automated phenotyping and precision farming. Recording
  AUG 16	Dr. Tie Liu University of Florida - Horticultural Sciences Dr. Nikos Tziolas University of Florida - Soil, Water, and Ecosystem Sciences	AI and machine learning to reduce postharvest losses Tie Liu, an assistant professor of horticultural sciences, says the quality of fresh fruits and vegetables deteriorates before reaching consumers due to biochemical processes and compositional changes. In his presentation Liu talked about how he and his team are addressing food waste and loss problems. They’re leveraging imaging-based machine learning technologies to understand postharvest deterioration and microbial spoilage of fresh produce to evaluate the quality. Liu proposes a research program to identify proteins and compounds as “freshness indicators” and to help develop easy-to-use tools to accurately estimate the freshness of produce and or contamination of produce. Click here for more about Liu’s research. From soil mapping to informed decision making for ecosystem health: An envisioned target and the role of AI After Liu spoke, Nikos Tziolas, an assistant professor of soil, water, and ecosystem sciences at the UF/IFAS Southwest Research and Education Center, talked about how monitoring soil health means we must improve evidence-based conservation strategies. This can be achieved through multidimensional approaches, the use of AI and cost-effective digital tools. Tziolas will present advanced data-handling techniques. These innovations boost surveys and provide essential soil-testing advisory services, showcasing their potential in Florida. His future strategy involves integrating spectra from over 100 countries and developing a user-friendly online prediction tool with localized multi-channel AI models to enhance global predictions. Recording
  JUNE 27	Dr. Dana Choi University of Florida - Agricultural and Biological Engineering Dr. Chang Zhao University of Florida - Agronomy	A new approach to training agricultural robotics through synthetic data and digital twin Dana Choi, an assistant professor of agricultural and biological engineering at the Gulf Coast Research and Education Center, talked about how AI advancements offer groundbreaking solutions across numerous fields, including agriculture. However, training machine-learning models for agricultural robotics presents significant challenges. She stressed the scarcity of high-quality training data, complex real-world agricultural environments and the time-consuming, costly nature of physical testing. Choi presented her current research in which she leverages synthetic data and digital twins to train machine-learning algorithms. Learn more about this research here. Geospatial Artificial Intelligence for Ecosystem Service Quantification At the same seminar, Chang Zhao, an assistant professor of agronomy, presented how her research can advance quantification and mapping ecosystem services, which include ways that human health and well-being are closely tied to the environment. Those include benefits such as food production, carbon storage and sequestration, habitat conservation, and non-material benefits such as recreation and landscape appreciation. She described several research projects she’s leading or working on, including remote sensing and AI-based methods to quantify and map land-cover change, vegetation dynamics and biodiversity. Zhao’s ultimate goal is to help us understand ecosystem services and develop easy-to-use geographic information tools that inform evidence-based decision-making for sustainable land use planning and management. Recording