Artificial Intelligence (AI)

Daeun Choi, Omeed Mirbod, Uchechukwu Ilodibe, and Steven Kinsey

The primary goal of this article is to provide background knowledge and terms that frequently come up in other articles about AI as well as in general use. The target audiences of this series include the general public, Extension educators, and farmers who want to know more about AI systems and their applications. This series will help readers understand the opportunities that AI brings to agriculture as smart technologies continue to grow in the market. Written by Daeun Choi, Omeed Mirbod, Uchechukwu Ilodibe, and Steven Kinsey, and published by the UF/IFAS Department of Agricultural and Biological Engineering, October 2023.

Arnold Schumann, Laura Waldo, Chris Oswalt, andChris Oswalt

The Citrus under Protective Screen (CUPS) project is a novel approach to citrus production in protected agriculture environments. To support the CUPS community, including growers, researchers, and Extension agents, a specialized chatbot named CUPSchat has been developed. This article provides an overview of CUPSchat, its features, and ways it differs from mainstream chatbots. Written by Arnold Schumann, Laura Waldo, and Chris Oswalt, and published by the UF/IFAS Department of Soil, Water, and Ecosystem Sciences, January 2026.

Eduart Murcia and Sandra M. Guzmán

This publication is intended for scientists, technicians, and decision-makers who want to start using machine learning (ML) in their projects. It provides an overview of the factors that should be considered when employing ML applications with time series (TS) data as input. Written by Eduart Murcia and Sandra M. Guzmán, and published by the UF/IFAS Department of Agricultural and Biological Engineering, January 2024.

Christopher J. Martinez, Clyde Fraisse, Yiannis Ampatzidis, Sandra M. Guzmán, Won Suk Lee, Christopher J. Martinez, Sanjay Shukla, Aditya Singh, andZiwen Yu

This publication aims to introduce readers to recent crop yield forecasting approaches based on artificial intelligence (AI) and to provide examples of how AI can potentially improve yield forecasting at the field and regional levels. Written by Clyde Fraisse, Yiannis Ampatzidis, Sandra Guzmán, Wonsuk Lee, Christopher Martinez, Sanjay Shukla, Aditya Singh, and Ziwen Yu, and published by the UF/IFAS Department of Agricultural and Biological Engineering, April 2022.

Golmar Golmohammadi, Seyed Mostafa Biazar, Rohith Reddy Nedhunuri, andNikolaos Tziolas

The advent of artificial intelligence (AI) and machine learning has permeated every aspect of our lives. Water resource management is becoming increasingly important due to the impacts of climate change and human activities. This publication provides examples of how AI can potentially improve the forecasting of water resource variables at both the field and regional levels. The main goal is to introduce new AI techniques for forecasting key environmental factors (climate, soil, water) related to water resources in Florida. The results demonstrate how deep learning models performed consistently well and established their superiority in capturing the temporal dynamics of river discharge and groundwater in the region. The results also show a dramatic groundwater level drawdown in certain areas, which could jeopardize the ability to meet water demands for agricultural purposes. The increase in river discharge and high groundwater levels might be related to flooding in those areas.

Young Gu Her, Nikolay Bliznyuk, Yiannis Ampatzidis, Ziwen Yu, and Haimanote Bayabil

This article is prepared to help Extension agents, farmers, farm managers, researchers, graduate students, and the general public better understand AI, associated terms, and technologies. Written by Young Gu Her, Nikolay Bliznyuk, Yiannis Ampatzidis, Ziwen Yu, and Haimanote Bayabil, and published by the UF/IFAS Department of Agricultural and Biological Engineering, September 2024.

Namrata Dutt andDaeun Choi

This article provides an introductory overview of how computers analyze images using artificial intelligence (AI) and computer vision. To illustrate the concepts, the publication uses mushrooms as an example case for object detection. Although mushroom detection is used for demonstration, the goal of this article is to help readers understand how AI-based image analysis supports broader agricultural and environmental applications. Written by Namrata Dutt and Daeun Choi, and published by the UF/IFAS Department of Agricultural and Biological Engineering, December 2025.

Vanessa Spero, Elaine Giles Simfukwe, andTara Dorn

There is a growing need for youth to learn how to use artificial intelligence (AI) in a way that will positively impact their careers and their everyday lives. To effectively use AI, familiarizing oneself with AI is crucial. This is known as AI literacy. This publication provides background for educators to increase their understanding of AI in youth educational settings and offers practical tools to better prepare themselves and their youth for responsible AI use.  

Santhi Daggubati, Xu Wang, Xue Zhou, Shubham Singh, andJessica Chitwood-Brown

Manual quantification of specialty crop traits, such as flowers and fruits, is often labor-intensive, time-consuming, and inconsistent, limiting scalability and precision. We present PhenoSnap, an artificial intelligence (AI)-powered web application that provides an intuitive and efficient interface for automated specialty crop trait extraction from images. PhenoSnap bridges the gap between advanced computer vision technologies and practical agricultural applications by eliminating the need for programming expertise. This ready-to-use solution can enable growers, breeders, and Extension faculty to accelerate field work and enhance decision-making related to strawberry and tomato yield estimation for breeding selections and strawberry runner management. Written by Santhi Daggubati, Xu Wang, Xue Zhou, Shubham Singh, and Jessica Chitwood-Brown, and published by the UF/IFAS Department of Agricultural and Biological Engineering, June 2026.