Leaf Disease Detection Using ResNet Deep Neural Network

Plant diseases have a detrimental impact on agricultural yield worldwide. Farmers that detect diseases early and accurately can reduce crop losses by taking appropriate action. The article uses deep convolutional neural networks to provide an automated online system for leaf disease diagnosis. The ResNet model is trained on a dataset of around 10000 images across 6 common leaf diseases. The evaluation of the performance of ResNet-50 models in multi-class disease classification across a held-out test set. The ResNet-50 model achieves an accuracy of 96.6%. The project demonstrates the efficacy of transfer learning with deep ResNet model for plant disease classification from leaf images. The high accuracy achieved makes the system useful for real-world agriculture applications. Accuracy metric will be used to assess the models. Additionally, important facets of model interpretability will be examined. The research will offer valuable insights from data on Images that can makes the system useful for real-world agriculture applications. The system aims to help identify potential plant diseases early for timely treatment.