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This study explored the use of high-throughput phenotyping with UAVs to assess stink bug resistance in soybean lines.

May 17, 2026Open Access

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

Key Points

This study explored the use of high-throughput phenotyping with UAVs to assess stink bug resistance in soybean lines.
Evaluated 304 soybean lines in alpha-lattice design trials across two seasons.
Scored five resistance-related traits and correlated them with UAV-derived indices.
Tested three machine learning models to predict traits from aerial features.
Visible Atmospherically Resistant Index (VARI_P25) strongly linked to resistance traits, high accuracy for grain yield and healthy seed weight predicted near flowering.
Texture indices at specific angles provided complementary structural information.
Cross-season predictions were unreliable, but VARI_P25 showed promise for identifying less promising lines.

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 (VARIP25), 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 VARIP25 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.

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