Artificial Intelligence (AI)

Applications of Artificial Intelligence for Precision Agriculture

Yiannis Ampatzidis

Introduction to Artificial Intelligence in Agriculture

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.

Deep Learning Classification of High-Resolution Drone Images Using the ArcGIS Pro Software

Amr Abd-Elrahman, Katie Britt, and Tao Liu

Deep learning classification of invasive species using widely used ArcGIS Pro software and increasingly common drone imagery can aid in identification and management of natural areas. A step-by-step implementation, with associated data for users to access, is presented to make this technology more widely accessible to GIS analysts, researchers, and graduate students working with remotely sensed data in the natural resource field.

Understanding Artificial Intelligence: What It Is and How It Is Used in Agriculture

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.

Types of Unmanned Aerial Vehicles (UAVs), Sensing Technologies, and Software for Agricultural Applications

Sri Charan Kakarla and Yiannis Ampatzidis

This publication provides detailed information about the most used UAVs, sensing technologies, and software for agricultural applications. Written by Sri Charan Kakarla and Yiannis Ampatzidis, and published by the UF/IFAS Department of Agricultural and Biological Engineering, October 2021.

How to Identify If Your Time Series Inputs Are Adequate for AI Applications: Assessing Minimum Data Requirements in Environmental Analyses

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.

Artificial Intelligence (AI) For Crop Yield Forecasting

Clyde Fraisse, Yiannis Ampatzidis, Sandra Guzmán, Wonsuk Lee, Christopher Martinez, Sanjay Shukla, Aditya Singh, and Ziwen 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.