Deep Structured Convolutional Neural Network for Tomato Diseases Detection

Abstract

Plant diseases outbreaks can cause significant threat to food security. Early detection of the diseases using machine learning could avoid such disaster. Currently, deep learning, which is a recent technology in machine learning, gained much popularity for object recognition tasks. Convolutional neural network (CNN) is one major techniques for object identification in deep learning. In this paper, we evaluate the effect of different depth of CNN architectures on the detection accuracies of the plant diseases detection. Various CNN architectures with different depth are investigated. They are simple CNN baseline (with two layer of convolutional layers), AlexNet (with five convolutional layers), and VGGNet (with 13 convolutional layers). We also evaluate GoogleNet architectures. Unlike previously mentioned architectures, GoogleNet use convolutional layers with various resolutions to be concantenated with each other, emphasizing the effect on not only the deep architecture but also a wide one. The experimental results suggest that CNN with deeper architecture, i.e. VGGNet, outperforms others, indicating that having deeper architectures may be more benefit for this task.