Published Paper Details:

Plant diseases pose formidable challenges to global food production, impacting crop yields, quality, and overall agricultural sustainability. Plant cultivation faces persistent threats from various pathogens. Early diagnosis and appropriate remedy of plant diseases is crucial for preventing widespread crop damage, ensuring food security and enhance the quality of the production. This project focuses on the development of a comprehensive framework for combatting plant pathogens through the integration of Convolutional Neural Networks (CNNs) for detection and remedial strategies. By employing sophisticated image processing techniques, the proposed system aims to analyze visual symptoms captured through imaging technology, facilitating early disease identification. The implementation of CNNs promises to revolutionize the traditional methods of disease diagnosis, enabling farmers to take proactive measures to prevent and control outbreaks. The main objective is to offer efficient and timely solutions for safeguarding plants against various diseases by providing specific remedies and preventive measures.