Identification and selection of evaluation indices for soybean shade tolerance via high-throughput phenotyping platforms and machine learning

doi:10.1016/j.jia.2025.11.004

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Xiuni Li^1,^2,³, Menggen Chen¹, Shuyuan He¹, Jie Chen⁴, Xiangyao Xu¹, Panxia Shao¹, Yahan Su¹, Lingxiao He¹, Wenjing Zhang¹, Nanli Du¹, Mei Xu¹, Yao Zhao^1,^2,³, Wenyu Yang^1,^2,³, Wouter H. Maes^5#, Weiguo Liu^1,^2,^3# #br#

¹College of Agronomy, Sichuan Agricultural University, Chengdu 610000, China
²Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu 610000, China
³Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu 610000, China
⁴Guizhou Academy of Tobacco Science, Guiyang 550000, China

⁵ UAV Research Center, Department of Plants and Crops, Ghent University, Gent 9000, Belgium

Highlights

l High-throughput phenotyping of 202 soybean accessions across two growth stages identified key shade tolerance indicators.

l A two-stage framework assessed symbiotic shade tolerance, recovery ability, and overall performance.

l Machine learning–based trait mining was validated across multiple environments.

Abstract
References

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摘要

间作是一个有前景的耕作方式，不仅能够实现可持续的水资源和土地利用效率还能保障国家粮油安全。为提升间作体系下大豆的产量稳定性，亟需构建科学、高效的耐荫性筛选体系。本研究基于高通量表型平台，提出一种集成多环境试验与机器学习分析的大豆耐荫性综合评价方法。该方法通过多元分析将202份大豆种质资源对套作环境的综合耐荫能力划分为共生期的耐荫能力和独立生长期的恢复能力，并从弱到强分为5个等级。然后，通过相关性分析、广义遗传力分析对指标进行初步筛选，再结合6种机器学习模型在大豆不同生育时期筛选关键耐荫指标。最后选用已知耐荫性的大豆品种在大田盆栽、大田种植、温室三种环境中种植，采用显著性分析对关键耐荫指标的准确性及普适性进行验证。研究结果表明，侧面投影面积（SCA）、顶部投影面积（TCA3）和顶部混合熵（TME）在耐荫能力较强的大豆品种中表现出两个关键特征：一是在遮荫条件下数值显著较高，二是在恢复期的增长幅度更大。基于上述三项指标构建的预测模型在耐荫能力与恢复能力方面的决定系数分别为R²=0.776和R²=0.959，具有良好的预测性能。综上所述，本研究展示了高通量表型平台与机器学习技术结合在耐荫性指标筛选中的应用潜力。仅需测定SCA、TCA3与TME三项指标，即可快速准确评估大豆苗期耐荫能力、生长后期的恢复能力及全生育期综合耐荫性。该方法为大豆种质资源的耐荫性评价、耐荫相关基因挖掘及间作专用品种的定向培育提供了有效工具和理论支撑。

Abstract

Intercropping is a promising cultivation strategy that enhances the sustainable use of water and land resources while contributing to national food and oil security. To improve the yield stability of soybeans in intercropping systems, there is an urgent need to develop a scientific and efficient framework for evaluating shade tolerance. In this study, we propose an integrated shade tolerance assessment method based on high-throughput phenotyping, multienvironment trials, and machine learning (ML) approaches. Utilizing multivariate analysis, we evaluated 202 soybean accessions and partitioned their performance under intercropping into two distinct capacities, namely, shade tolerance during the cogrowth stage and recovery ability during the independent growth stage, each of which was classified into five levels from weak to strong. Preliminary trait selection was performed through correlation analysis and broad-sense heritability estimation, followed by the application of six ML models to identify the key shade tolerance traits across different growth stages. The robustness and generalizability of the selected traits were validated in three environments—a field pot, an open field, and a greenhouse—using soybean varieties with known shade tolerance levels. The results revealed that three traits—the side canopy area (SCA), top canopy area at stage 3 (TCA3), and top-view mixed entropy (TME)—were strongly associated with shade-tolerant varieties. These traits presented two distinguishing features: significantly higher values under shaded conditions and greater increases during the recovery phase. The prediction models constructed with these three traits achieved strong performance, with coefficients of determination of R²=0.776 for shade tolerance and R²=0.959 for recovery ability. In summary, this study demonstrates the potential for integrating high-throughput phenotyping with ML to efficiently identify the key indicators of shade tolerance. By measuring only three indicators—SCA, TCA3, and TME—soybean shade tolerance at the seedling stage, recovery ability during later growth, and overall shade tolerance across the full growth period can be rapidly and accurately evaluated. This method offers a powerful and practical tool for implementing shade tolerance evaluations, gene discovery, and targeted breeding of soybean cultivars that are suitable for intercropping systems.

Keywords: soybean high-throughput phenotyping machine learning comprehensive evaluation indicator of shade tolerance

Online: 07 November 2025

Fund:

This work was supported by the Major Project on Agricultural Biotechnology Breeding under the Technology Innovation 2030 Initiative (2023ZD0403405), the National Natural Science Foundation of China (32172122), the Key Research and Development Project of the Guizhou Branch of the China National Tobacco Corporation (2023XM18), and the National Modern Agricultural Industry Technology System, Sichuan Soybean Innovation Team, China (SC-CXTD-2024-21).

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Xiuni Li, Menggen Chen, Shuyuan He, Jie Chen, Xiangyao Xu, Panxia Shao, Yahan Su, Lingxiao He, Wenjing Zhang, Nanli Du, Mei Xu, Yao Zhao, Wenyu Yang, Wouter H. Maes, Weiguo Liu. 2025. Identification and selection of evaluation indices for soybean shade tolerance via high-throughput phenotyping platforms and machine learning. Journal of Integrative Agriculture, Doi:10.1016/j.jia.2025.11.004

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