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刘斌副教授并行与视觉处理研究小组在深度学习葡萄叶部病害识别方面取得新进展

论文题目:Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks

作       者:Bin Liu , Zefeng Ding , Liangliang Tian , Dongjian He, Shuqin Li and Hongyan Wang

期刊名称:Frontiers in Plant Science(中科院2区)

发表时间:2020年7月

论文摘要:

  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.


论文链接https://www.frontiersin.org/articles/10.3389/fpls.2020.01082/full