Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US

Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US

Efficient and reliable corn (<i>Zea mays</i> L.) yield prediction is important for varietal selection by plant breeders and management decision-making by growers. Unlike prior studies that focus mainly on county-level or controlled laboratory-scale areas, this study targets a production-...

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Bibliographic Details
Main Authors: Sayantan Sarkar, Javier M. Osorio Leyton, Efrain Noa-Yarasca, Kabindra Adhikari, Chad B. Hajda, Douglas R. Smith
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Sensors
Subjects:
corn
maize
yield prediction
machine learning
vegetation indices
ensemble methods
Online Access:https://www.mdpi.com/1424-8220/25/2/543
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