Detection of Plant Leaf Diseases through RNN and Transfer Learning Techniques

Plant diseases threaten the lives of farmers and the delivery of essential nutrients for the world's growing population, posing a serious threat to agricultural productivity and global food security. Plant diseases may be accurately and promptly identified. critical for mitigating their detrimental impact on agricultural productivity, as prompt intervention can help contain the spread of infections and minimize losses in crop yields. This paper proposes a novel approach that seamlessly integrates Recurrent Neural Networks and transfer learning techniques to effectively identify and classify a wide range of plant leaf diseases. The innovative methodology presented in this research aims to offer a strong, flexible, and all-inclusive solution for the prompt identification and precise categorization of diverse plant pathologies. By leveraging the powerful feature extraction and sequence modeling capabilities of Recurrent Neural Networks, combined with the rich visual representations and learned knowledge acquired from Convolutional models of neural networks that have already been trained, the suggested system aspires to make a significant contribution towards supporting sustainable farming methods and guaranteeing world food security by accurately Early detection and diagnosis of plant leaf diseases.