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<p>As facility agriculture advances towards high precision and energy efficiency, plant supplemental lighting strategies are shifting from static, preset methods to dynamic, perception-driven approaches. Traditional lighting recipes or empirical supplemental lighting methods often result in plant disease issues, energy waste, and photoinhibition. In recent years, hyperspectral imaging technology has emerged as a powerful, non-destructive monitoring tool, capable of capturing subtle real-time changes in plant photosynthetic pigments, water content, nitrogen levels, and early stress responses. When combined with hyperspectral imaging, machine learning enables the extraction of features and the construction of predictive models from vast spectral datasets, serving as a core driver for the early detection of plant diseases and informed decision-making. This paper systematically reviews recent advances in the integration of hyperspectral technology and machine learning for plant supplemental lighting. Furthermore, it emphasizes the critical role of machine learning models in predicting light demand, diagnosing stress, and addressing plant diseases.</p>

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From light supplementation to spectral analysis: machine learning and hyperspectral reveals plant health status

  • 期刊论文

  • 发表日期：

    2026年6月14日

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  • Jiajie Wang,
  • Zihan Wang,
  • Yilin Wang,
  • Jiang Wang,
  • Yue Li,
  • Defan Chen,
  • Mingming Shi,
  • Yanqiang Shi,
  • Hongliu Xu,
  • Jun Zou

摘要

As facility agriculture advances towards high precision and energy efficiency, plant supplemental lighting strategies are shifting from static, preset methods to dynamic, perception-driven approaches. Traditional lighting recipes or empirical supplemental lighting methods often result in plant disease issues, energy waste, and photoinhibition. In recent years, hyperspectral imaging technology has emerged as a powerful, non-destructive monitoring tool, capable of capturing subtle real-time changes in plant photosynthetic pigments, water content, nitrogen levels, and early stress responses. When combined with hyperspectral imaging, machine learning enables the extraction of features and the construction of predictive models from vast spectral datasets, serving as a core driver for the early detection of plant diseases and informed decision-making. This paper systematically reviews recent advances in the integration of hyperspectral technology and machine learning for plant supplemental lighting. Furthermore, it emphasizes the critical role of machine learning models in predicting light demand, diagnosing stress, and addressing plant diseases.