Plant Disease Detection using Feature Fusion

Plant diseases are one of the biggest threats in today’s world, it impacts yield of crop in a big way. Early detection is vital for effective disease management, prompting the need for advanced methodologies. This work addresses the challenge of plant disease detection through an innovative approach using spectral data and image data. The focus is on improving accuracy and efficiency in identifying diseases at their early stages. Our motivation to improve upon existing work stems from the limitations of standalone techniques that exist currently, emphasizing the potential of spectral data and image data. By performing the detection models of diseases in plants, with the help of spectral data and image data, we aim to increase the accuracy of the models with the help of ensemble learning, and feature fusion.