Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks

Bin Liu; Zefeng Ding; Liangliang Tian; Dongjian He; Shuqin Li; Hongyan Wang

doi:10.3389/fpls.2020.01082

Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks

Front Plant Sci. 2020 Jul 15:11:1082. doi: 10.3389/fpls.2020.01082. eCollection 2020.

Authors

Bin Liu^{1

2

3}, Zefeng Ding¹, Liangliang Tian¹, Dongjian He^{2

3

4}, Shuqin Li^{1

2

5}, Hongyan Wang^{5

6}

Affiliations

¹ College of Information Engineering, Northwest A&F University, Yangling, China.
² Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Northwest A&F University, Yangling, China.
³ Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Northwest A&F University, Yangling, China.
⁴ College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China.
⁵ Ningxia Smart Agricultural Industry Technology Collaborative Innovation Center, Yinchuan, China.
⁶ West Electronic Business, Co., Ltd., Yinchuan, China.

Abstract

Anthracnose, brown spot, mites, black rot, downy mildew, and leaf blight are six common grape leaf pests and diseases, which cause severe economic losses to the grape industry. Timely diagnosis and accurate identification of grape leaf diseases are decisive for controlling the spread of disease and ensuring the healthy development of the grape industry. This paper proposes a novel recognition approach that is based on improved convolutional neural networks for the diagnoses of grape leaf diseases. First, based on 4,023 images collected in the field and 3,646 images collected from public data sets, a data set of 107,366 grape leaf images is generated via image enhancement techniques. Afterward, Inception structure is applied for strengthening the performance of multi-dimensional feature extraction. In addition, a dense connectivity strategy is introduced to encourage feature reuse and strengthen feature propagation. Ultimately, a novel CNN-based model, namely, DICNN, is built and trained from scratch. It realizes an overall accuracy of 97.22% under the hold-out test set. Compared to GoogLeNet and ResNet-34, the recognition accuracy increases by 2.97% and 2.55%, respectively. The experimental results demonstrate that the proposed model can efficiently recognize grape leaf diseases. Meanwhile, this study explores a new approach for the rapid and accurate diagnosis of plant diseases that establishes a theoretical foundation for the application of deep learning in the field of agricultural information.

Keywords: convolutional neural networks; deep learning; disease identification; grape leaf diseases; image augmentation.