Automated Spectral Biomarker Identification for Early Disease Detection in Cover Crop-Integrated No-Till Systems

**Abstract:** This paper introduces a novel framework for automated spectral biomarker identification aimed at early disease detection within cover crop-integrated no-till (CCINT) agricultural systems. Leveraging hyperspectral imaging data coupled with advanced machine learning techniques, we propose a modular pipeline capable of identifying subtle spectral shifts indicative of early-stage plant stress, preceding visible symptoms. This system overcomes the limitations of traditional visual inspection and subjective assessments, enabling proactive disease management and minimizing yield losses in sustainable agricultural practices. The framework is designed for rapid deployment and real-time decision-making, offering a commercially viable solution for precision agriculture.