Plant Diseases Detection for Sustainable Agriculture

Abstract

Agriculture plays a crucial role in the economy of any country and agriculture, providing food, raw materials, and employment opportunities. However, plant diseases continue to be a significant threat to crop yield and food security. Early and reliable identification of such diseases is vital to mitigate their impact. Conventional identification methods are often dependent on expert analysis, which may be inaccessible in rural or remote regions and are prone to subjectivity and delays. To address these challenges, this work presents an automated system for detecting plant diseases using Convolutional Neural Networks (CNNs) based on the ResNet-34 model. ResNet-34 is selected due to its efficient handling of deep network training through residual learning, which enable better accuracy. The system is trained on the publicly available Plant Village dataset, comparing over 54303 leaf images categorized into 38 different plant disease classes. The approach includes preprocessing the images, applying transfer learning using ImageNet pretrained weights, and fine-tuning the model for better generalization. The result shows high performance on validation and testing datasets, showing the model works effectively in real-time plant disease detection. This solution is a practical and scalable tool aimed to helps the farmers with technological support, promoting healthy crops and sustainable agricultural practices and reduces the crops and financial losses due to disease in plants.