Leveraging deep learning for strategic decision-making in sustainable agriculture: enhancing plant disease detection for optimised supply chain management and ecosystem health

Agriculture, an essential component of our society, suffers the continuous threat of plant diseases that can potentially destroy crops and threaten the country's food security. Traditional methods of disease detection that rely on human visual inspection are often unreliable, particularly when it comes to recognising early signs of diseases. This research proposes an innovative method that integrates image processing and deep learning techniques to transform the field of crop plant disease detection in agriculture. Our process utilises computer vision and Convolutional Neural Network (CNN) to detect various crop plant diseases, including viral outbreaks and fungal infections, in different plant species and environmental situations. Our solution combines advanced technologies, and an exhaustive evaluation procedure to provide farmers with accurate and fast information. This enables them to reduce crop losses and improve agricultural sustainability.