Comprehensive Multilayer Convolutional Neural Network for Plant Disease Detection

Agriculture has a dominant role in the world's economy. However, losses due to crop diseases and pests significantly affect the contribution made by the agricultural sector. Plant diseases and pests recognized at an early stage can help limit the economic losses in agriculture production around the world. In this paper, a comprehensive multilayer convolutional neural network (CMCNN) is developed for plant disease detection that can analyze the visible symptoms on a variety of leaf images like, laboratory images with a plain background, complex images with real field conditions and images of individual disease symptoms or spots. The model performance is evaluated on three public datasets -Plant Village repository having images of the whole leaf with plain background, Plant Village repository with complex background and Digipathos repository with images of lone lesions and spots. Hyperparameters like learning rate, dropout probability, and optimizer are fine-tuned such that the model is capable of classifying various types of input leaf images. The overall classification accuracy of the model in handling laboratory images is 99.85%, real field condition images is 98.16% and for images with individual disease symptoms is 99.6% The proposed design is also compared with the popular CNN architectures like GoogleNet, VGG16, VGG19 and ResNet50. The experimental results indicate that the suggested generic model has higher robustness in handling various types of leaf images and has better classification capability for plant disease detection. The obtained results suggest the favorable use of the proposed model in a decision support system to identify diseases in several plant species for a large range of leaf images.