Published Paper Details:

Plant disease diagnosis is an art as well as science. The diagnosis process (i.e. recognition of symptoms and signs), is inherently visual and requires intuitive judgment as well as the use of scientific methods to classify them. In this era convolutional neural networks (CNNs) has achieved impressive results in the area of image classification. Hence, this paper is concerned with a new approach to the development of pomegranate Leaf disease recognition model, based on leaf image classification, by the use of convolutional neural networks, more over deep convolutional networks, by exploring small kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against over fitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for Leaf disease recognition.