Plant Leaf Disease Identification Based on Lightweight Residual Network

被引：0
|
作者：
Li S. [1 ]
Chen C. [1 ]
Zhu T. [1 ]
Liu B. [1 ]
机构：
[1] College of Information Engineering, Northwest A&F University, Yangling
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2022年 / 53卷 / 03期
关键词：
Disease identification; Lightweight network; Plant leaf; ResNet; Squeeze-and-Excitation network;
D O I：
10.6041/j.issn.1000-1298.2022.03.025
中图分类号：
学科分类号：
摘要：
The plant leaf disease recognition method based on convolutional neural network has the problem of numerous network parameters, large amount of calculation and complexity.To solve this problem, combined with the characteristics of plant leaf diseases, a plant leaf disease recognition method based on lightweight residual network (Scale-Down ResNet) was proposed.The network was based on Residual Network (ResNet), by reducing the number of convolution kernels and the network module of SD-BLOCK, the network parameters and computational complexity were greatly reduced, while the recognition error rate was kept low.Then the Squeeze-and-Excitation module was added to further reduce the recognition error rate.Experiments on the PlantVillage data set showed that when parameters were 8×104 and calculation amout MFLOPs was 55, the recognition error rate of model was 0.55%.When parameters reached 2.8×105 and calculation amount MFLOPs was 176, the recognition error rate of model was 0.32%, which was lower than that of ResNet-18, and the parameter was about 1/39 of ResNet-18 and the amount of calculation was about 1/10 of ResNet-18. Compared with MobileNet V3 and ShuffleNet V2, the proposed network model was lighter and had lower recognition error rate.At the same time, the low recognition error rate of 1.52% was obtained on self built apple leaf disease data set. © 2022, Chinese Society of Agricultural Machinery. All right reserved.
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页码：243 / 250
页数：7
相关论文
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