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.

Keywords: Calvin cycle; chlorophyll fluorescence; crop productivity; hyperspectral imaging; photosynthesis.

Fig. 1. Interaction of light with the leaf lamina. (A) Sunlight, also known as incoming light, reaches the plant leaf. (B) As the light encounters the leaf surface, some of it is reflected; (C) a portion of the light is diffusely reflected off the leaf surface, scattering in various directions; (D) a portion is absorbed by the leaf's lamina, which is crucial for photosynthesis; and (E) transmitted light, which is the remaining light that is not absorbed or reflected, passes through the leaf and may reach lower leaves or the ground.

Fig. 2. Flow chart of hyperspectral imaging system components and processes for plant spectral data acquisition and analysis.

Fig. 3. Illustration of the process of measuring leaf reflectance using a spectroradiometer (ASD Field Spec 3). (A) Incoming solar radiation is directed at a plant leaf. (B) The sensor is positioned at a 25-degree angle from the leaf to capture the reflected radiation. (C) A white surface is used as a reference to calibrate the sensor for accurate measurement. (D) The sensor collects data on the leaf's reflectance, which is then plotted on a graph, showing how different wavelengths of light are absorbed or reflected by the leaf, indicating various plant physiological properties and atmospheric interactions.

Fig. 4. The diagram presents a multi-step scientific process for measuring the fluorescence line height (FLH) index and analyzing photosynthetic activity using a fluorescence-based approach.

Fig. 5. The flowchart outlines a structured process for analyzing hyperspectral image data, starting with image acquisition, preprocessing, data extraction, modelling, and validation. It includes normalization, calibration, filters, segmentation, and computing indices. The final output is a classified image with an interpreted analysis of the hyperspectral data.

Fig. 6. Workflow for Calvin Cycle assessment using hyperspectral imaging and analysis.