Leaf Disease Identification Using DenseNet

To maintain a promising status of global food security, it is imperative to strike a congruous balance between the estimated alarming growth in the global population and the expected agricultural yield to cater to their needs appropriately. An agreeable balance has not been acquired in this respect which could be the cause of the origin of food crisis across the world. Therefore it is crucial to prevent any direct or indirect factors causing this. Proper growth of plants and protection against diseases is a very instrumental factor towards meeting the quality and quantity of food requirements globally. Deep learning Methods have gained successful results in the spheres of image processing and pattern recognition. We have made an effort in implementing the methods of deep learning for analyzing leaves of plants for prediction and detection of any diseases. Here, we have considered two majorly grown crops in Himachal Pradesh i.e. tomato and potato, for performing various experiments. In our result analysis, we have achieved an accuracy of 96.24% while identifying the diseases in the leaves.