Sensor-Based Bermudagrass Yield Prediction Models Using Random Forest Algorithm in Oklahoma

The current available direct and indirect forage biomass estimation methods are prohibitive for producers because they are labor-intensive and time-consuming. Current literature states that (i) machine learning algorithms are promising in agriculture, and (ii) proximity and multispectral sensors can...

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書誌詳細

出版年:	Agronomy
主要な著者:	Gabriel Camargo de Campos Jezus, Lucas Freires Abreu, Daryl Brian Arnall, Lucas Martins Stolerman, Alexandre Caldeira Rocateli
フォーマット:	論文
言語:	英語
出版事項:	MDPI AG 2025-04-01
主題:	bermudagrass yield prediction random forest sensors
オンライン･アクセス:	https://www.mdpi.com/2073-4395/15/5/1004