Harnessing Convolutional Neural Networks for Precise Classification of Paddy Leaf Diseases

Paddy is essential for promoting rice growth and supporting aquatic life. Wetland ecosystems and the supply of rice are both maintained by sustainable paddy management. Significant losses in rice yield are caused by paddy leaf diseases, a global threat. Infections from bacteria, viruses, and fungi prevent photosynthesis and growth. For the sustainability of rice production and food security, early disease detection is essential. Deep learning is a subset of machine learning that uses multiple-layered neural networks to understand complex data patterns. Image recognition, language processing, and decisionmaking are being revolutionized through the use of data-driven insights. Deep learning teaches neural networks to analyze leaf images for paddy leaf disease classification, correctly classifying disease types for efficient agriculture management. To detect paddy disease, Convolution Neural Networks (CNNs) are used