Leaf-image based analysis of crop diseases using a deep learning recognition model.

Diseases can have a detrimental effect on plants depreciating their market value. Disease diagnosis is important for improving plant yield. Over the years, farmers are relying on Soft Computing (SC) techniques for the purpose of early diagnosis of various plant diseases. The paper builds an application using a Deep Learning (DL) model for timely disease diagnosis. The research employs pre-trained Convolution Neural Networks (CNN) for identifying crop diseases and insect pests. CNN efficiently tackles the challenges related to image stability and the number of parameters. PlantVillage dataset is used for training and testing the model. The dataset comes with fifteen different classes of infected and healthy leaves. The work applies image pre-processing techniques and data augmentation for enhancing the image recognition rate. The paper implements a crop disease recognition model comprising of Inception-ResNet-v2, Visual Geometry Group 16, DenseNet201 and, Inception_V3 for training. The model and subsequently the application are validated and tested using standard methods. The convolution layer of the networks uses a non-linear activation function-ReLU which has gained popularity in the deep learning domain for its simple activation strategy. Later the name of the crop reporting the highest matching degree is displayed. Lastly, the accuracy of all the networks is compared against the traditional convolution network. It is inferred that the system thus aims to efficiently identify crop diseases and produces better recognition accuracy than the traditional convolution networks. [ABSTRACT FROM AUTHOR]