Abstract: The prevalence of Disease recognition in rice leaves poses a significant challenge to agricultural productivity and food security globally. Traditional methods of disease recognition in rice leaves, which depend heavily on manual inspection and expert knowledge, are increasingly inadequate for modern agricultural needs. These methods are labor-intensive, time-consuming, and often prone to human error. This research aims to leverage the advancements in deep learning to develop an automated system for rice leaf disease identification. By employing convolutional neural networks (CNNs), DenseNet, and ResNet architectures, this study seeks to classify images of rice leaves into various disease categories accurately. The high performance of these models underscores their capability to capture intricate patterns and features essential for disease identification. In addition to accuracy. The system is designed to run on a local server, ensuring accessibility and reliability for farmers and agricultural experts. Key components include user authentication, image upload, preprocessing, disease classification, and result visualization. The results demonstrate the system's effectiveness in early disease detection, which can significantly improve crop management and yield. Future enhancements include integrating IoT devices, expanding to multiple crops, and developing a mobile application for greater accessibility.

Keywords: Rice leaves disease recognition, Deep Learning, Convolutional Neural Networks, Automated Disease Classification, Agricultural Technology, Image Processing, Local Server

| DOI: 10.17148/IARJSET.2024.11734