Machine Learning Techniques to Predict Soybean Plant Density Using UAV and Satellite-Based Remote Sensing

The plant density of soybean is a critical factor affecting plant canopy structure and yield. Predicting the spatial variability of plant density would be valuable for improving agronomic practices. The objective of this study was to develop a model for plant density measurement using several data s...

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Veröffentlicht in:	Remote Sensing Jg. 13; H. 13; S. 2548
Hauptverfasser:	Habibi, Luthfan Nur, Watanabe, Tomoya, Matsui, Tsutomu, Tanaka, Takashi S. T.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 29.06.2021
Schlagworte:	Accuracy Agricultural practices Aircraft Algorithms Biomass canopy Climatic data Density measurement Learning algorithms Machine learning meteorological data Model accuracy Object recognition partial least squares regression Photogrammetry PlanetScope plant density Plant populations prediction Predictions Q random forest Regression analysis Regression models Remote sensing Root-mean-square errors Satellite imagery satellites Science Sensors Soybeans spatial variation spectral reflectance Unmanned aerial vehicles Variability Vegetation YOLOv3
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
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