Near real-time soybean phenology detection using proximally-sensed hyperspectral canopy reflectance and machine learning methods
Nicolás Rigalli,
Enrique Montero Bulacio,
Martín Romagnoli
et al.
Abstract:To date, most of the research data on the selection of plant types based on crop growth phenology is obtained by expert observations in the field. The extraction of soybean phenology stages/phases from canopy spectral data might provide timing information for optimal field management. In this work a simple high-resolution technique for collecting proximally-sensed hyperspectral data at canopy level was performed and machine learning phenology models based on canopy spectral data plus physical predictors were d… Show more
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