Classification and Comparison Study of Rice Plant Diseases using Pre-Trained CNN Models

Abstract

Convolution Neural Network plays an important role in the field of agriculture for the identification and classification of diseases in the different types of crops. Most of the families in India belong to the farming background. So, in farming the paddy crop plays the crucial role because most of the famers are interested in growing the paddy crop. There are many diseases which damages the paddy crop. and it effects the production of rice. Due to this, farmers can’t meet the required production of food. In order to reduce the damage from different types of diseases on paddy crop, the CNN model is chosen. There are other sources like machine learning technology for identification of diseases but in recent times due to increase in technology and accuracy metrics the deep learning is widely used. In paddy crop, there are more than 30 varieties of diseases which damage the crop. Among these, four types of diseases like Leaf Blast, Brown Spot, Hispa and Bacterial Leaf Blight are more commonly observed in the crop. And these diseases are similar to each other, so that they can’t even be identified by the naked eye. In order to reduce the damage from diseases they should be identified in early stage and the necessary precautions must be taken for the increase in the crop yield. The main objective of this project is to make a deep learning model for the identification and classification of paddy crop diseases through transfer learning and image processing and all the process is done in deep learning. This project is mainly focuses on four disease classes and the classification process, the accuracy metrics is analyzed. Among the different pre trained models such as InceptionV3, VGG 16, Resnet 50, the better accuracy is achieved from Inceptionv3 with an accuracy of 91.23%.