From spectrum to yield: advances in crop photosynthesis with hyperspectral imaging

Abstract

Ensuring global food security requires noninvasive techniques for optimizing resource use and monitoring crop health. Hyperspectral imaging (HSI) enables the precise analysis of plant physiology by capturing spectral data across narrow bands. This review explores HSI's role in agriculture, particularly its integration with unmanned aerial vehicles, AI-driven analytics, and machine learning. These advancements allow real-time monitoring of photosynthesis, chlorophyll fluorescence, and carbon assimilation, linking spectral data to plant health and agronomic decisions. Key indicators such as solar-induced fluorescence and vegetation indices enhance crop stress detection. This work compares HSI-derived metrics in differentiating nutrient deficiencies, drought, and disease. Despite its potential, challenges remain in data standardization and spectral interpretation. This review discusses solutions such as molecular phenotyping and predictive modeling, for AI-driven precision agriculture. Addressing these gaps, HSI is poised to revolutionize farming, improve climate resilience, and ensure food security.

Plant phenotyping relevance

植物の光合成・クロロフィル蛍光・ストレスなどを hyperspectral imaging で推定する手法を中心に扱うレビューであり、植物フェノタイピング手法の方法論的レビューに該当する。

Code and data availability

This is a review article on hyperspectral imaging in crop photosynthesis. It contains no original phenotyping measurements, datasets, images, code, or models of its own. The only URLs present (ResearchGate, handle.net, Flinders, Colorado State) appear in the reference list as citations to prior works (e.g., Barnes etal

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