Published Paper Details:

The recognition and early prevention of leaf diseases in time is very essential for improving the crop production. Convolutional Neural Networks are widely used in autonomous driving, computer vision, and medical imaging. Different deep learning models are proposed by the researchers for detection of diseases in plants. A novel CNN model is implemented in this paper to identify and diagnose plant diseases.The standard CNN model requires a huge number of parameters, computation cost is high and also the training time is more. Standard convolution is replaced with the depth wise separable convolution in order to minimize the number of parameters and computation cost. The implemented model is tested on the leaves of tomato crop in plant village dataset. The implemented CNN model attained classification accuracy rates of 98.45%,98.52%,99.05%,99.34%,99.46%,99.60% using standard inception V3, ResNet101V2, modified inception V3, EfficientNetB0, ResNet 50V2 and DenseNet121 respectively. The implemented DWCNNmodel has taken less number of parameters and achieved better performance when compared with other deep learning models with reference to accuracy.