HARPS: A Hybrid Algorithm for Robust Plant Stress Detection to Foster Sustainable Agriculture

For sustainable agriculture practices to be achieved as a result of changing climates and growing hazards to the environment, improving resilience in plants is crucial. Stress-Associated Proteins (SAPs) have an important role in helping plants react to abiotic stress conditions such as drought, salinity, and changes in temperature. This study underlines the ability of the SAP gene family to promote stress adaptation mechanisms by presenting a thorough analysis of the gene family across 86 distinct plant species and genera. We present an optimized Hybrid Algorithm for Robust Plant Stress (HARPS), a unique machine learning (ML)-based system designed to efficiently identify and classify plant stress responses. A comparison with conventional ML models shows that HARPS substantially reduces computational time while achieving higher accuracy. This efficiency makes HARPS ideal for real-time agricultural applications, where precise and quick stress detection is essential. With the help of an ablation study and conventional evaluation metrics, we further validated the effectiveness of the model. Overall, by strengthening crop monitoring, increasing resilience, lowering dependency on chemical inputs, and enabling data-driven decision-making, this research advances the objectives of sustainable agriculture production and crop protection. HARPS facilitates scalable, resource-efficient stress detection essential for adjusting to climatic uncertainty and mitigating environmental consequences.