Precision Agriculture

How to Avoid Common Problems with Leaf Wetness Sensor Installation and Maintenance

T. B. Onofre, C. W. Fraisse, N. A. Peres, and J. McNair

Postflight Data Processing Instructions on the Use of Unmanned Aerial Vehicles (UAVs) for Agricultural Applications

Sri Charan Kakarla and Yiannis Ampatzidis

Parameters for Site-Specific Soil Phosphorus Loss Modeling from Soil Test Data

Biswanath Dari, Vimala D. Nair, and Willie G. Harris

Applications of Unmanned Aerial Systems in Agricultural Operation Management: Part III: Best Practices for Efficient Aerial Surveying

Aditya Singh and James Fletcher

UF/IFAS Nutrient Management Series: Soil Sampling Strategies for Precision Agriculture

Rao S. Mylavarapu and Won Suk Daniel Lee

Yield Mapping Hardware Components for Grains and Cotton Using On-the-Go Monitoring Systems

Rebecca Barocco, Won Suk Lee, and Garret Hortman

Preflight and Flight Instructions on the Use of Unmanned Aerial Vehicles (UAVs) for Agricultural Applications

Sri Charan Kakarla, Leon De Morais Nunes, and Yiannis Ampatzidis

Applications of Unmanned Aerial Systems in Agricultural Operation Management: Part I: Overview

James Fletcher and Aditya Singh

Precision Farming Adoption by Florida Citrus Producers: Probit Model Analysis

Brian J. Sevier and Won Suk Lee

Instructions on the Use of Unmanned Aerial Vehicles (UAVs)

Sri Charan Kakarla and Yiannis Ampatzidis

Applications of Artificial Intelligence for Precision Agriculture

Yiannis Ampatzidis

Nutrition of Florida Citrus Trees, 3 Edition: Chapter 5. Precision Agriculture for Citrus Nutrient Management

Arnold W. Schumann and Edward A. Hanlon

Applications of Unmanned Aerial Systems in Agricultural Operation Management: Part II: Platforms and Payloads

Aditya Singh and James Fletcher

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.

Who Owns and Controls Farming Data?

Ziwen Yu, Albert De Vries, Yiannis Ampatzidis, and D. Daniel Sokol

This publication aims to clarify the concerns regarding data ownership and explain the responsibilities of that ownership, sharing, and benefits in a collaborative smart farming application. Written by Ziwen Yu, Albert De Vries, Yiannis Ampatzidis, and D. Daniel Sokol, and published by the UF/IFAS Department of Agricultural and Biological Engineering, October 2021.

Approaches to Consider for Site-Specific Field Mapping

Lakesh K. Sharma and Yiannis Ampatzidis

This publication discusses the general concept of site-specific farming, i.e., dividing a farm into several smaller management parcels instead of considering the whole farm as a single unit. This publication provides information on methods available that could be incorporated into the farming system for smart decision-making, for example, varying nutrient applications. The publication also discusses Normalized Difference Vegetative Index (NDVI) maps, Global Positioning System (GPS) units, yield monitors, and electrical conductivity maps.

Impact of In-Row Planting Patterns on Reniform Nematodes Using Precision Agriculture in Cotton

D. L. Wright, S. George, I. Small, T. Stansly, and Z. Grabau

This article is intended to inform cotton producers and associated agricultural professionals of a practical method to help manage plant-parasitic nematodes in cotton. Written by D. L. Wright, S. George, I. Small, T. Stansly, and Z. Grabau, and published by the UF/IFAS Agronomy Department, revised September 2022.

A Step-by-Step Guide for Automated Plant Canopy Delineation Using Deep Learning: An Example in Strawberry Using ArcGIS Pro Software

Amr Abd-Elrahman, Katie Britt, and Vance Whitaker

This publication presents a guide to image analysis for researchers and farm managers who use ArcGIS software. Anyone with basic geographic information system analysis skills may follow along with the demonstration and learn to implement the Mask Region Convolutional Neural Networks model, a widely used model for object detection, to delineate strawberry canopies using ArcGIS Pro Image Analyst Extension in a simple workflow. This process is useful for precision agriculture management.

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.