Efficient Plant Diseases Recognition based on Modified Residual Neural Network and Transfer Learning

Abstract

Efficient and accurate recognition of plant diseases based on leaf images is a hot research topic. The plant diseased leaf images are complex and diverse. It is generally difficult to extract reliable features. In this paper, a new plant disease recognition method is proposed, based on a Modified Residual Neural Network (MRNN) and transfer learning. Compared with the classical residual neural network ResNet-50, the residual block structure in MRNN is modified. The experiment results on the AI Challenger dataset show MRNN can achieve 91.4% recognition accuracy which is higher than other classic CNN models. Combined with the Kaggle Cassava dataset, the MRNN is trained with transfer learning, which improves the accuracy, robustness and generalization ability. The experiments results show that the proposed method not only has an advantage in accuracy, but also has a significant improvement in training speed, which validates the efficiency and effectiveness of the proposed approach.