Intelligent Leaf Disease Classification using Deep Convolutional Neural Networks for Sustainable Agriculture

Abstract

The general health condition of the crop is very essential for increasing the agricultural production and global food security. Fungal diseases in leaves may spread rapidly and result in yield reduction if not detected timely. To this end, an intelligent system is constructed based on deep convolutional neural network to classify the leaf diseases of plants using images. The CNN model can be further qualified by a diverse dataset coupled with preprocess and data augmentation for higher generalization capability. The CNN architecture drawn learns highly packed visual feature from the plant leaf image and can classify several types of disease with very high accuracy. Our experiment results verified the efficacy of the package with 92.23% classification accuracy which outperformed traditional image processing combined with classical machine learning approaches in the past. It can effectively be used for actual farming in the field with positive contribution to sustainable farming practices.