Soybean Yield Prediction with High-Throughput Phenotyping Data and Machine Learning

Abstract

The non-destructive estimation of grain yield could increase the efficiency of soybean breeding through early genotype testing, allowing for more precise selection of superior varieties. High-throughput phenotyping (HTPP) data can be combined with machine learning (ML) to develop accurate prediction models. In this study, an unmanned aerial vehicle (UAV) equipped with a multispectral camera was utilized to collect data on plant density (PD), plant height (PH), canopy cover (CC), biomass (BM), and various vegetation indices (VIs) from different stages of soybean development. These traits were used within random forest (RF) and partial least squares regression (PLSR) algorithms to develop models for soybean yield estimation. The initial RF model produced more accurate results, as it had a smaller error between actual and predicted yield compared with the PLSR model. To increase the efficiency of the RF model and optimize the data collection process, the number of predictors was gradually decreased by eliminating highly correlated VIs and selecting the most important variables. The final prediction was based only on several VIs calculated from a few mid-soybean stages. Although the reduction in the number of predictors increased the yield estimation error to some extent, the R2 in the final model remained high (R2 = 0.79). Therefore, the proposed ML model based on specific HTPP variables represents an optimal balance between efficiency and prediction accuracy for in-season soybean yield estimation.

Plant phenotyping relevance

UAVマルチスペクトル画像から植物形質を取得し、機械学習でダイズ収量を推定するワークフローが研究の中心であり、形質抽出・予測モデルの開発と効率化を扱っている。

Code and data availability

The paper's HTPP dataset (UAV-derived VIs, PD, CC, PH, BM for 1113 soybean plots) and RF/PLSR analysis are not publicly deposited; the Data Availability Statement says data are available only on request from the corresponding author. No public code, model checkpoints, or image assets with URLs are mentioned.

No evidence-backed public reproduction asset is currently recorded.