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<p>In the advent of rapid climate change, the growing complexity of abiotic stress combinations poses a significant threat to global agricultural production and food security. Traditional approaches, such as univariate statistical models or isolated omics studies that focus on single stressors, are often inadequate for predicting crop performance in the contemporary multivariate stress conditions present in agriculture. Recent technological breakthroughs in high-throughput multi-omics, phenomics, and environmental monitoring have generated enormous datasets that clarify the intricate interplay between genetic and environmental factors affecting plant stress responses. Concurrently, machine learning (ML) and artificial intelligence (AI) methodologies have emerged as powerful tools for modeling these complex interactions; yet, their conventional “black box” nature limits biological interpretability and practical use in crop improvement. This review highlights recent developments in interpretable machine learning algorithms that anticipate multifactorial abiotic stress responses in climate-resilient crops. We analyze the integration of multi-omics data with high-throughput phenotyping and environmental factors using interpretable models, such as attention-based neural networks, SHAP value analysis, and decision tree ensembles. These techniques aim to enhance the predictive accuracy and clarify essential regulatory pathways and biological drivers influencing stress resilience. Additionally, we also shed light on the challenges in data integration, model transparency, and the translational properties of computational discoveries into practical breeding methodologies. Finally, we propose future research directions aimed at refining these AI-driven frameworks to expedite the creation of crop varieties with enhanced tolerance to multiple stressors. This review emphasizes the game-changing capability of interpretable machine learning to close the gap between computational predictions and operational, field-level applicability in precision agriculture in a changing climate.</p>

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Precision under pressure: leveraging interpretable AI and machine learning to predict multifactorial abiotic stress in climate-smart crops

  • 期刊论文

  • 发表日期：

    2026年5月11日

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  • L. Abhishek,
  • B. U. Shravya,
  • Neelakanta Sarvashiva Kiran,
  • Agnik Haldar,
  • Tikam Chand Dakal,
  • Senthilkumar Rajagopal

摘要

In the advent of rapid climate change, the growing complexity of abiotic stress combinations poses a significant threat to global agricultural production and food security. Traditional approaches, such as univariate statistical models or isolated omics studies that focus on single stressors, are often inadequate for predicting crop performance in the contemporary multivariate stress conditions present in agriculture. Recent technological breakthroughs in high-throughput multi-omics, phenomics, and environmental monitoring have generated enormous datasets that clarify the intricate interplay between genetic and environmental factors affecting plant stress responses. Concurrently, machine learning (ML) and artificial intelligence (AI) methodologies have emerged as powerful tools for modeling these complex interactions; yet, their conventional “black box” nature limits biological interpretability and practical use in crop improvement. This review highlights recent developments in interpretable machine learning algorithms that anticipate multifactorial abiotic stress responses in climate-resilient crops. We analyze the integration of multi-omics data with high-throughput phenotyping and environmental factors using interpretable models, such as attention-based neural networks, SHAP value analysis, and decision tree ensembles. These techniques aim to enhance the predictive accuracy and clarify essential regulatory pathways and biological drivers influencing stress resilience. Additionally, we also shed light on the challenges in data integration, model transparency, and the translational properties of computational discoveries into practical breeding methodologies. Finally, we propose future research directions aimed at refining these AI-driven frameworks to expedite the creation of crop varieties with enhanced tolerance to multiple stressors. This review emphasizes the game-changing capability of interpretable machine learning to close the gap between computational predictions and operational, field-level applicability in precision agriculture in a changing climate.