The UF/IFAS Faculty AI Working Group (FAWG) is presenting a series of seminars from faculty across UF/IFAS as well as external speakers to build a robust scholarly community on artificial intelligence (AI) and data science. It will provide a platform for experts to share their latest research advancements in fundamental and applied AI and data science technologies for agriculture, natural resources, and human systems.
Bookmark this blog for reference as links will be added after each event.
Nov. 17, 2023 AI seminar series presentations:
Applications of Generative Artificial Intelligence Models to Agriculture: Preliminary Studies on Heat Stress Estimation Using Aerial Images
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
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.
October 27. 2023
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.
September 29, 2023
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 Phenomcs 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.
August 16, 2023
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.
June 27, 2023
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.
ABOUT UF/IFAS
The mission of the University of Florida Institute of Food and Agricultural Sciences (UF/IFAS) is to develop knowledge relevant to agricultural, human and natural resources and to make that knowledge available to sustain and enhance the quality of human life. With more than a dozen research facilities, 67 county Extension offices, and award-winning students and faculty in the UF College of Agricultural and Life Sciences, UF/IFAS brings science-based solutions to the state’s agricultural and natural resources industries, and all Florida residents.
About AI at UF
The University of Florida is making artificial intelligence the centerpiece of a major, long-term initiative that combines world-class research infrastructure, cutting-edge research and a transformational approach to curriculum. UF is committed to building the nation’s first AI University by offering artificial intelligence courses to all students in its 16 colleges through our AI Across the Curriculum program. Our faculty and students can analyze vast amounts of data that can impact our country’s biggest challenges with HiPerGator, the most powerful, university-owned supercomputer in the nation.
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