Predicting within-field variability in grain yield and protein content of winter wheat using UAV-based multispectral imagery and machine learning approaches

Xinbin Zhoua College of Resources and Environment, Southwest University, Chongqing, ChinaView further author information

Yusuke Konob Faculty of Applied Biological Sciences, Gifu University, Gifu, JapanView further author information

Aung Winc The United Graduate School of Agricultural Science, Gifu University, Gifu, JapanView further author information

Tsutomu Matsuib Faculty of Applied Biological Sciences, Gifu University, Gifu, JapanView further author information

Takashi S. T. Tanakab Faculty of Applied Biological Sciences, Gifu University, Gifu, Japan;d Artificial Intelligence Advanced Research Center, Gifu University, Gifu, JapanCorrespondence[email protected]

https://orcid.org/0000-0001-7116-6962 View further author information

Predicting within-field variability in grain yield and protein content of winter wheat using UAV-based multispectral imagery and machine learning approaches

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Predicting within-field variability in grain yield and protein content of winter wheat using UAV-based multispectral imagery and machine learning approaches

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