Recognition of Plant Leaf Diseases Based on Shallow Convolutional Neural Network

Abstract

Existing popular methods for the recognition of plant leaf diseases with deep convolutional neural networks (DCNNs) improve the learning ability of traditional models by automatically learning the features of leaf images. However, these deep networks suffer from the concerns in terms of many parameters and high time complexity. To solve the limits, we propose a novel identification model (SCNN) of the plant leaf diseases based on shallow CNN. In SCNN, we reduce the number of parameters and the complexity by designing a new shallow network based on the deep learning technologies (BN and Dropout). Comprehensive evaluations on PlantVillage dataset demonstrate that our SCNN achieves state-of-the-art results.