Plant diseases classification with Spectral Signature Taxonomy & Analysis Software (SSTAS)

This paper investigates a novel approach to plant disease classification, addressing cases where symptoms are not visually apparent. Traditional machine learning methods, reliant on observable symptoms, face challenges such as limited training data, high costs, and low interpretability. To overcome these limitations, a spectroscopy-based classification technique was developed. Experimental data, collected over 15 months at Anand Agriculture University, Gujarat, and Charotar University Space Research Centre, utilized spectral signatures (400–1000 nm) to detect mango diseases. The SSTAS Software, developed with a fine-tuned deep learning model, Deep-Spectro, demonstrated superior accuracy using an 80:20 training-to-testing ratio, surpassing existing models reported in prior research. • Real-time spectral data detects asymptomatic plant diseases for early detection. • SSTAS achieves 98.11% accuracy , surpassing previous models in plant disease classification. • Deep learning framework optimizes multiclass classification for precise disease identification. • Non-invasive, cost-effective solution for continuous plant disease monitoring and management. • Sustainability impact through reduced pesticide use and improved crop yield prediction.