Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease

Abstract Experts help farmers to diagnose the citrus diseases by using agriculture laboratories or viewing the visual symptoms. These methods may not be accessible to all the farmers due to the expert’s cost and non-availability of laboratories. The proposed work presents the comparison of two different Convolutional Neural Network (CNN) architectures to classify diseases of the citrus leaf. In this paper, two types of CNN architectures, such as MobileNet and Self-Structured (SSCNN) classifiers were used to detect and classify citrus leaf diseases at the vegetative stage. The proposed work prepared a smartphone image based citrus disease dataset. Both the models were trained and tested on the same citrus dataset. The performances of the models were evaluated using the accuracy and loss of the training and validation sets, respectively. The best training accuracy of the MobileNet CNN was 98% with 92% validation accuracy at the epoch 10. But the best training accuracy of the SSCNN was 98% with 99% validation accuracy at the epoch 12. The proposed system indicates that the SSCNN is more helpful and accurate for smartphone image based citrus leaf disease classification. In addition, the SSCNN algorithm takes less computation time as compared to MobileNet and it can be considered a cost-effective method for citrus disease detection.