Intelligent decision support for drought stress (IDSDS): An integrated remote sensing and artificial intelligence-based pipeline for quantifying drought stress in plants

Abstract

Drought is a major abiotic stress that adversely affects plant growth, physiology, and crop yield. Conventional methods for assessing drought stress tend to be fragmented, targeting either leaves, canopies, or roots, and are often expensive, low-throughput, and lack the ability to provide real-time, whole-plant insights. Addressing these limitations, this study presents a novel, integrated pipeline titled Intelligent Decision Support for Drought Stress (IDSDS) that leverages remote sensing and artificial intelligence (AI) for accurate, real-time monitoring of drought stress across entire plants. The IDSDS pipeline employs low-cost RGB images collected at various growth stages and uses a deep learning-based model to reconstruct hyperspectral data, which is typically costly and complex to obtain. This reconstructed data enables the extraction of key physiological traits, including greenness, saturation, and pigment content. A novel phenotyping metric—Greenness Coefficient (GC), was also proposed, offering precise spatial analysis of drought impact within the plant. The hyperspectral reconstruction model was validated using standard performance metrics such as the correlation coefficient, mean squared error, standard deviation of squared error, and spectral angle mapper (SAM). IDSDS further calculates a comprehensive set of spectral indices (e.g., greenness, leaf pigment, water content) that are closely linked to drought-induced changes. Finally, by integrating these indices with machine learning-based classification models, IDSDS accurately stratifies plant drought stress into seven distinct categories. The results showed that the proposed hyperspectral reconstruction model effectively converts RGB plant images into accurate hyperspectral data, achieving a SAM value between 0.14 and 0.30. This indicates strong spectral similarity, meaning the reconstructed pixel spectra closely align with the reference spectra. The GC, along with other reconstructed spectral indices, supports visual interpretation and enhances the traceability of the system’s outputs, thereby increasing transparency. Additionally, the findings demonstrate statistically significant results (p < 0.001) for these indices in detecting plant drought stress, with a high classification accuracy of 99 % and an average area under the curve (AUC) of 1.00, reflecting precise differentiation of stress across the entire plant. Overall, the study introduces a breakthrough in drought stress monitoring, combining high-throughput and cost-effective RGB imaging with AI to support both scientific research and practical crop management. The IDSDS pipeline lays the groundwork for informed, drought-adaptive decision-making of agricultural crops.

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