Kiwifruit Leaf Disease Identification Using Improved Deep Convolutional Neural Networks

Abstract

Brown spot, Mosaic and Anthracnose are three common kiwifruit leaf diseases, which causes serious economic losses in the kiwifruit industry. The timely and precise identification approach of kiwifruit leaf diseases is significant for controlling the spread of disease and ensuring the healthy growth of the kiwifruit industry. In this paper, a novel identification approach based on improved convolutional neural networks is proposed for kiwifruit leaf diseases. A dataset consisting of 11322 kiwifruit leaf images is firstly generated using image augmentation. And then, a novel CNNs-based model named Kiwi-ConvNet is built with Kiwi-Inception structures and dense connectivity strategy, which can enhance the capability of multi-scale feature extraction and ensure multi-dimensional feature fusion. Under the hold-out test set, the experimental results show that the proposed model realizes an accuracy of 98.54%, gaining a better accuracy of 2.29% and 9.51% than GoogLeNet and ResNet-20 respectively. This research indicates that the proposed model achieves accurate diagnosis of kiwifruit leaf diseases automatically, and provides a viable solution in the field of crop leaf disease identification with high recognition accuracy.