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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Vydané v:	Remote Sensing Ročník 13; číslo 13; s. 2548
Hlavní autori:	Habibi, Luthfan Nur, Watanabe, Tomoya, Matsui, Tsutomu, Tanaka, Takashi S. T.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 29.06.2021
Predmet:	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
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Abstract	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 sets with different spatial resolutions, including unmanned aerial vehicle (UAV) imagery, PlanetScope satellite imagery, and climate data. The model establishment process includes (1) performing the high-throughput measurement of actual plant density from UAV imagery with the You Only Look Once version 3 (YOLOv3) object detection algorithm, which was further treated as a response variable of the estimation models in the next step, and (2) developing regression models to estimate plant density in the extended areas using various combinations of predictors derived from PlanetScope imagery and climate data. Our results showed that the YOLOv3 model can accurately measure actual soybean plant density from UAV imagery data with a root mean square error (RMSE) value of 0.96 plants m−2. Furthermore, the two regression models, partial least squares and random forest (RF), successfully expanded the plant density prediction areas with RMSE values ranging from 1.78 to 3.67 plant m−2. Model improvement was conducted using the variable importance feature in RF, which improved prediction accuracy with an RMSE value of 1.72 plant m−2. These results demonstrated that the established model had an acceptable prediction accuracy for estimating plant density. Although the model could not often evaluate the within-field spatial variability of soybean plant density, the predicted values were sufficient for informing the field-specific status.
AbstractList	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 sets with different spatial resolutions, including unmanned aerial vehicle (UAV) imagery, PlanetScope satellite imagery, and climate data. The model establishment process includes (1) performing the high-throughput measurement of actual plant density from UAV imagery with the You Only Look Once version 3 (YOLOv3) object detection algorithm, which was further treated as a response variable of the estimation models in the next step, and (2) developing regression models to estimate plant density in the extended areas using various combinations of predictors derived from PlanetScope imagery and climate data. Our results showed that the YOLOv3 model can accurately measure actual soybean plant density from UAV imagery data with a root mean square error (RMSE) value of 0.96 plants m−2. Furthermore, the two regression models, partial least squares and random forest (RF), successfully expanded the plant density prediction areas with RMSE values ranging from 1.78 to 3.67 plant m−2. Model improvement was conducted using the variable importance feature in RF, which improved prediction accuracy with an RMSE value of 1.72 plant m−2. These results demonstrated that the established model had an acceptable prediction accuracy for estimating plant density. Although the model could not often evaluate the within-field spatial variability of soybean plant density, the predicted values were sufficient for informing the field-specific status. 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 sets with different spatial resolutions, including unmanned aerial vehicle (UAV) imagery, PlanetScope satellite imagery, and climate data. The model establishment process includes (1) performing the high-throughput measurement of actual plant density from UAV imagery with the You Only Look Once version 3 (YOLOv3) object detection algorithm, which was further treated as a response variable of the estimation models in the next step, and (2) developing regression models to estimate plant density in the extended areas using various combinations of predictors derived from PlanetScope imagery and climate data. Our results showed that the YOLOv3 model can accurately measure actual soybean plant density from UAV imagery data with a root mean square error (RMSE) value of 0.96 plants m⁻². Furthermore, the two regression models, partial least squares and random forest (RF), successfully expanded the plant density prediction areas with RMSE values ranging from 1.78 to 3.67 plant m⁻². Model improvement was conducted using the variable importance feature in RF, which improved prediction accuracy with an RMSE value of 1.72 plant m⁻². These results demonstrated that the established model had an acceptable prediction accuracy for estimating plant density. Although the model could not often evaluate the within-field spatial variability of soybean plant density, the predicted values were sufficient for informing the field-specific status.
Author	Takashi S. T. Tanaka Tomoya Watanabe Tsutomu Matsui Luthfan Nur Habibi
Author_xml	– sequence: 1 givenname: Luthfan Nur surname: Habibi fullname: Habibi, Luthfan Nur – sequence: 2 givenname: Tomoya surname: Watanabe fullname: Watanabe, Tomoya – sequence: 3 givenname: Tsutomu surname: Matsui fullname: Matsui, Tsutomu – sequence: 4 givenname: Takashi S. T. orcidid: 0000-0001-7116-6962 surname: Tanaka fullname: Tanaka, Takashi S. T.
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Snippet	The plant density of soybean is a critical factor affecting plant canopy structure and yield. Predicting the spatial variability of plant density would be...
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SubjectTerms	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
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Title	Machine Learning Techniques to Predict Soybean Plant Density Using UAV and Satellite-Based Remote Sensing
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