Deep Learning Solution For Crop Health Monitoring using CNN in Smart Agriculture

The agricultural Sector has witnessed a transformative shift towards smart agriculture, with a significant emphasis on leveraging advanced technologies for efficient crop disease detection with context to this the paper explores the potential of modern technologies, particularly computer vision and machine learning, for the early detection and management of crop diseases .This report explores the integration of Convolutional Neural Networks (CNNs) in smart agriculture to address the escalating challenges of crop protection. Focusing on the significant threat of crop disease faced during cultivation of different crops, the study emphasizes early detection using advanced technologies. A literature survey showcases successful disease identification models, and the proposed methodology outlines data collection, preprocessing, and CNN implementation. Results demonstrate CNN’s effectiveness in Greencare with an accuracy of up to 95.71%, offering a robust framework for further advancements in agricultural image analysis. This research contributes to sustainable practices, promoting global food security through innovative crop disease detection systems.