Optimizing soybean breeding: High-throughput phenotyping for stink bug resistance and high yields

Abstract

Abstract Background: The stink bug complex is one of the most damaging pests of soybean, reducing yield and seed quality. Genetic resistance remains the most sustainable and effective management strategy, but its quantitative inheritance and labor-intensive field phenotyping make its implementation in breeding programs challenging. Objective: This study explored high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with RGB cameras to evaluate a soybean population and the potential of phenotyping to stink bug resistance by correlating image-derived features and machine learning (ML) models. Methods: A population of 304 soybean lines was evaluated in alpha-lattice design trials across two seasons under natural infestations. Five resistance-related traits, grain yield (GY), healthy seed weight (HSW), number of days to maturity (NDM), tolerance (TOL), and leaf retention (LR), were manually scored and linked to UAV-derived vegetation indices (VIs) and texture indices (TIs). Three ML models (AdaBoost, SVM, MLP) were tested to predict these traits from aerial features. Results: Results showed that VIs, particularly Visible Atmospherically Resistant Index at the 25th percentile (VARI_P25), were consistently associated with resistance-related traits, while decision tree analysis highlighted TIs at 45° and 135° as complementary sources of structural information. Prediction ability was highest for GY, HSW, and NDM, especially in flights near flowering and maturity, but remained low for TOL and LR. Integrating multiple flights modestly improved accuracy, whereas cross-season predictions were unreliable. Nonetheless, indices such as VARI_P25 provided useful cross-season correlations for HSW and TOL, enabling early screening of less promising lines. Conclusion: This pioneering study demonstrates that UAV–ML pipelines can capture genetic signals of stink bug resistance in soybean, despite environmental complexity. These findings open new avenues for resistance phenotyping, supporting more efficient breeding strategies and accelerating genetic gains in soybean improvement.

Plant phenotyping relevance

UAV画像と機械学習を用いて、ダイズの抵抗性関連形質や収量を推定するHTPパイプラインを技術的に評価しており、表現型取得・推定法が研究の中心である。

Code and data availability

The paper's UAV-derived vegetation/texture index data, manually scored trait data, and ML analysis are not publicly deposited; the authors state data are available only upon request. No author code or dataset URL is provided, and the supplementary material link is not an allowed URL.

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