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.
Highlights
In soybean (Glycine max (L.) Merr.) production, ensuring a high plant density is one of the main ways to maximize crop yield [1,2,3]
In order to establish models to predict the plant density of soybean, we divided the workflow of the study into three steps: (1) performing high-throughput measurement of actual plant density of the observation fields from unmanned aerial vehicle (UAV) imagery using an object detection model based on the You Only Look Once version 3 (YOLOv3) algorithm, which was further treated as a response variable in the regression model development; (2) performing preprocessing of the PlanetScope imagery to fill in the gaps of missing daily spectral reflectance data using spline smoothing, which served as predictor variables from satellite imagery data; (3) establishing satellite-based plant density estimation models using two regression analysis methods, partial least squares (PLS) and random forest (RF) regression, to expand the measurement of plant density into wider area coverage
This study demonstrates that the satellite-based RF regression model successfully identified field-specific plant density
Summary
In soybean (Glycine max (L.) Merr.) production, ensuring a high plant density is one of the main ways to maximize crop yield [1,2,3]. Plant density is a product of seedling rates and stands establishment ratios. There is an optimal plant density for maximizing yield according to the environment [6] and genotype [7], because an excessively high soybean population affects plant structure, mainly by reducing the number of pods per plant. The quantification of plant density is an essential step to identifying the optimal plant population, row spacing, and seed density during sowing, which could contribute to developing better crop management practices. Given the recent diffusion of precision agricultural technologies, such as variable-rate seeding, field-specific and within-field variability of plant density should be quantified
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
