Comparison of RSNET model with existing models for potato leaf disease detection

Since crop diseases have a substantial impact on production, it is necessary to detect and identify them. Using deep learning and intelligent firming, diseased crops may be identified automatically. We present exceptionally efficient convolution neural network (CNN) designs for the detection of leaf diseases as part of this research plan. For the training and testing stages of this project, a potato leaf database is created. CNN was used to extract the illness's features from the input images of the provided training dataset so that the disease could be classified. 1700 photographs of potato leaf were utilised for model training, followed by 600 photographs for testing. To recognise citrus illnesses, Convolutional Neural Networks, Deep Learning, base learning, and transfer learning were used. The proposed architecture outperforms other existing ResNet models in terms of accuracy, reaching a score of 99.62%, according to training, testing, and experimentation results.