Crop disease recognition and diagnosis using Residual Neural Network

Crop disease is a serious problem in the agricultural sector. To prevent crop disease we have to detect the disease at an early stage. Various technologies are emerging these days to determine specific diseases in crops. Deep Learning is one of the best approaches to detecting crop disease. This research paper includes a deep learning framework to classify healthy and diseased crops. For image recognition, ResNet was built using Keras applications. It is a deep residual learning approach that was used, as its framework is easy for training networks. Our used dataset consists of 87,354 images of 14 different sets of crops including both healthy and diseased images. The dataset was collected using a cloud-based architecture with AR. The model architecture that was trained gives us an accuracy of 99.53% in finding the diseased crop images successfully. The high success rate of this model makes it very useful and most effective in real-life applications. The further expansion of this idea “Crop disease diagnosis using deep learning” will help contribute to the operation in real cultivation conditions.