Classification of Rice Leaf Diseases Using CNN-Based Pre-Trained Models and Transfer Learning

In the past, diagnosing pests has been a very important and challenging task for farmers, and ocular detection methods with the help of phytosanitary specialists, were time consuming, costly, and associated with human error. Today, in modern agriculture, diagnostic softwares by artificial intelligence can be used by farmers themselves with little time and cost. On the other hand, because diseases and pests of plants, especially rice leaves, are of different intensities and are similar to each other, automatic detection methods are more accurate and have less error. In this paper, two transfer learning methods for diagnosing rice leaf disease are investigated. The first method uses the CNN-based output of a pre-trained model and an appropriate classifier is added. In the second method, freezing the bottom layers, fine-tuning the weights in the last layers of the pre-trained network, and adding the appropriate classifier to the model are proposed. For this purpose, seven CNN models have been designed and evaluated. Simulation results show that four of these networks as: VGG16 network with fine tuning the last two layers, Inceptionv3 with fine tuning the last 12 layers, Resnet152v2 with fine tuning the last 5 and 6 layers reach 100% accuracy and an f1-score of 1. In addition, fewer number of layers in VGG16 network with 2-layers fine tuning consumes less memory and has faster response time. Also, our paper has a higher accuracy and less training time than similar papers.