Crop Yield

Adjusting Crop Yield to a Standard Moisture Content

Michael J. Mulvaney and Pratap Devkota

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

Rebecca Barocco, Won Suk Lee, and Garret Hortman

Methods to Evaluate Peanut Maturity for Optimal Seed Quality and Yield

Ethan Carter, Patrick Troy, Diane Rowland, Barry Tillman, Keith Wynn, Krystel Pierre, and Michael Mulvaney

A UF/IFAS numbered Fact Sheet. in support of UF/IFAS Extension program: Plant Systems

Unlocking the Potential of Technology: Estimating Nitrogen Requirement in Corn Using Optical Sensors

Lakesh K. Sharma, Diego A. H. de S. Leitão, and Hardeep Singh

In the domain of agriculture, the application of optical sensing technology has become increasingly prevalent in the assessment of corn grain production. However, it is notable that comprehensive research endeavors addressing the efficacy of employing the Normalized Difference Vegetation Index (NDVI) as a predictive tool for corn grain yield in the specific context of Florida remain limited. To address this gap in knowledge, a dedicated study focusing on nitrogen (N) rates was meticulously designed in 2022. The primary objective of this study was to provide critical insights and empirical evidence to both our Extension agents and the community of corn farmers. Its overarching aim is to elucidate the practical utility of NDVI sensors in forecasting crop output and facilitating precise estimations of N requirements.

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.