Optimizing Soybean Breeding: High-Throughput Phenotyping Technologies for Stink Bug Resistance and High Yields

Abstract

Abstract The stink bug complex is a major agricultural pest for soybean crops, significantly reducing productivity. Genetic resistance is the most effective control strategy, but its quantitative nature and labor-intensive phenotyping make its implementation in breeding programs challenging. This study explored high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with RGB cameras to evaluate a soybean population and identify stink bug resistance by correlating image-derived features and machine learning (ML) models. Using an alpha-lattice design with three replications, we phenotyped 304 soybean lines over two seasons under natural stink bug infestations. We manually evaluated five traits associated with stink bug resistance and correlated them with color, texture, and histogram features from aerial images. Three ML models—AdaBoost, SVM, and MLP— were tested to predict these traits. VIs, especially the Visible Atmospherically Resistant Index at the first percentile (VARI_P25) and texture-based indices at 45° and 135°, effectively predicted traits in stressed environments, particularly during flights near maturation. While ML models showed good predictive ability for yield, healthy seed weight, and maturity, they were less effective for stink bug resistance. Increasing the number of UAV flights modestly improved predictive accuracy, though predicting traits across different seasons remained challenging. Despite this, indices like VARI_25P were valuable for screening and excluding less promising genotypes, optimizing breeding program resources. This pioneering work offers valuable insights and highlights the need for further research to optimize resistance selection, promising significant advances in soybean breeding for stink bug resistance.

Plant phenotyping relevance

UAV RGB画像と機械学習による形質推定を、ソイビーン育種集団の高スループット表現型解析として評価しており、画像特徴量・モデル性能・季節間予測を検討する方法中心の研究である。

Code and data availability

The supplied blocks describe UAV image collection, image-derived trait extraction, and ML prediction for soybean stink bug resistance, but contain no data availability statement, public dataset link, image deposit, or author code repository. Only generic software (Agisoft MetaShape, QGIS, FieldImageR, OpenCV, scikit-AS

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