Paddy Crop Disease Detection using Deep Learning Techniques

Agriculture productivity plays a key role in the economy development. The availability of food is being exposed to crop diseases. Due to the spread of technology worldwide, it is now technically possible to use image processing techniques to identify the kind of plant disease from a straightforward approach. The use of an automatic method for crop disease detection is advantageous due to the less effort and in identifying the disease signs at an early stage. In the presented work a deep convolutional network and semi-supervised techniques are trained to distinguish crop species and the condition of illness in leaf. The technique for identifying paddy illness contains two key phases: the first is training the model, and the second is spotting the disease from the provided picture. The proposed work has used the CNN, VGG19 and DenseNet models to classify the Paddy Crop Disease. This work shows that the DenseNet model achieves the highest accuracy of 99% compared to other models.