On Using Transfer Learning For Plant Disease Detection
share
Deep neural networks have been highly successful in image classification problems. In this paper, we show how deep neural networks can be used for plant disease recognition in the context of image classification. We have used a publicly available Plant Village dataset which has 38 classes of diseases. Hence the problem that we have addressed is a multi class classification problem. We have compared five different architectures including VGG16, ResNet50, InceptionV3, InceptionResNet and DenseNet169 as the backbones for our work. We found that ResNet50 which uses skip connections using a residual layer archives the best result on the test set. For evaluating the results, we have used metrics like accuracy, precision, recall, F1 score and class wise confusion metric. Our model achieves the best of results using ResNet50 with accuracy of 0.982, precision of 0.94, recall of 0.94 and F1 score of 0.94.
READ FULL TEXT