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
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Zusammenfassung: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.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs13132548