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Crop Disease Detection and Classification with Transfer learning and hyper-parameters optimized Convolutional neural network

To identify plant diseases in an early stage, image processing techniques are used in detection and classification of plant diseases. In this paper, CNN with transfer learning and optimized CNN are proposed for detection and classification of crop diseases. The method is used for training and testing of images of maize leaves belonging to four classes. In the proposed work, the hyper-parameters are optimized and then the network is trained. Experimental results prove that the hyper-parameters optimized CNN results in improved classification accuracy. Transfer learning based CNN is implemented with four pretrained models –Alexnet, VGG16, ResNet50, and DenseNet121. The database used is Plant Village dataset from Kaggle Platform. The hyper-parameter optimized CNN achieves an accuracy of 0.9623. The transfer learning with Densenet121 model yields maximum classification accuracy of 0.9727. Experimental results prove that attained results achieve an improvement of 2.95% over the state-of-the art methods.