改进Multi-scale ResNet的蔬菜叶部病害识别

王春山; 周冀; 吴华瑞; 滕桂法; 赵春江; 李久熙

doi:10.11975/j.issn.1002-6819.2020.20.025

改进Multi-scale ResNet的蔬菜叶部病害识别

王春山^1,2,3,4,
周冀¹,
吴华瑞^2,3,
滕桂法^1,4,
赵春江^2,3,
李久熙⁵

1.
河北农业大学信息科学与技术学院，保定 071001
2.
国家农业信息化工程技术研究中心，北京 100097
3.
北京农业信息技术研究中心，北京 100097
4.
河北省农业大数据重点实验室，保定 071001
5.
河北农业大学机电工程学院，保定 071001

基金项目: 国家大宗蔬菜产业技术体系（CARS-23-C06）；国家重点研发计划（2019YFD1101105）；国家自然科学基金项目（61771058）；河北省重点研发计划项目（20327402D）；河北省研究生创新资助项目（CXZZBS2020103）

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出版历程
- 收稿日期: 2020-07-06
- 修回日期: 2020-08-27
- 发布日期: 2020-10-14

Identification of vegetable leaf diseases based on improved Multi-scale ResNet

1.
College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China
2.
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
3.
Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
4.
Hebei Province Key Laboratory of Agricultural Big Data, Baoding 071001, China
5.
College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071001, China

摘要

摘要: 基于深度网络的蔬菜叶部病害图像识别模型虽然性能显著，但由于存在参数量巨大、训练时间长、存储成本与计算成本过高等问题，仍然难以部署到农业物联网的边缘计算设备、嵌入式设备、移动设备等硬件资源受限的领域。该研究在残差网络（ResNet18）的基础上，提出了改进型的多尺度残差（Multi-scale ResNet）轻量级病害识别模型，通过增加多尺度特征提取模块，改变残差层连接方式，将大卷积核分解，进行群卷积操作，显著减少了模型参数、降低了存储空间和运算开销。结果表明，在PlantVillage和AI Challenge2018中15种病害图像数据集中取得了95.95%的准确率，在自采集的7种真实环境病害图像数据中取得了93.05%的准确率，在准确率较ResNet18下降约3.72%的情况下，模型的训练参数减少93%左右，模型总体尺寸缩减约35%。该研究提出的改进型Multi-scale ResNet使蔬菜叶部病害识别模型具备了在硬件受限的场景下部署和运行的能力，平衡了模型的复杂度和识别精度，为基于深度网络模型的病害识别系统进行边缘部署提供了思路。
- 图像处理 /
- 病害 /
- 图像识别 /
- 多尺度 /
- 轻量化 /
- 残差层 /
- ResNet18
Abstract: Abstract: Although the performance of the deep network-based image recognition model of vegetable leaf disease is excellent, it is still difficult to deploy to the edge computing equipment, embedded equipment, mobile equipment, and other areas of agricultural Internet of Things (IoT) due to the problems of huge parameters, long training time, high storage cost, and calculation cost. Therefore, how to reduce the size of the model while ensuring the accuracy of model recognition has become a hot issue. Based on the study of the advantages and disadvantages of ResNet18, this study proposed an improved Multi-scale ResNet lightweight disease recognition model. The Multi-scale ResNet had made improvements in network structure design, multi-scale feature extraction, feature mapping dimensionality reduction, and complexity reduction. In order to test the effectiveness of the network, this study used two datasets. Dataset 1 used 15 vegetable diseases in the public dataset of the Plantvillage and the AI Challenge2018, and dataset 2 was self-collecting 7 disease images. Each image was resized to 224 × 224 using bilinear interpolation. In order to prevent overfitting due to too few data, the original dataset was enhanced through translation, scaling and rotation, random clipping and scaling, random brightness contrast enhancement, random gamma noise, and vertical flip. The original dataset was expanded to 134 232, and the training set, verification set, and test set were divided according to the ratio of 7∶2∶1. The experimental scheme included model structure design and comparison with other models. In the model structure design experiment, five options were included convolution kernel design, channel design, residual layer connection design, grouping design, and residua block number design. The effectiveness of network design mode was verified by changing one design method by fixing other design methods. Finally, the network structure of Multi-scale ResNet was determined, including the network structure of using large convolution kernel and group convolution, two channels, two-layer connection mode, and two residua blocks. The accuracy of Multi-scale ResNet on dataset 1 and dataset 2 reached 95.95% and 93.05% respectively. Compared with other models (AlexNet, VGG16, ResNet50, SqueezeNet), the improved Multi-scale ResNet has the least number of parameters and the smallest model volume. And the accuracy of Multi-scale ResNet was 3.72% lower than the original ResNet18, but the training parameters of the model were reduced about 93% and the overall size of the model was reduced about 35%. The minimum size of Multi-scale ResNet was 68.75 MB, and the accuracy was only about 1.5% lower than ResNet50. Moreover, the loss value of the model decreases smoothly, and there was no similar oscillation phenomenon of the original ResNet18 and ResNet50. Experiments showed that the Multi-scale ResNet has the characteristics of small size and high accuracy. It made the vegetable leaf disease identification model had the ability to deploy and run in the scene of limited hardware. It overcame the shortcomings of the traditional depth model which was not suitable for edge deployment because of its large parameters and calculation. It could meet the urgent need of realizing long-term, large-scale, and low-cost automatic identification of vegetable diseases under the condition of resource constraints.
- image processing /
- diseases /
- image recognition /
- multi-scale /
- lightweight /
- residual layer /
- ResNet18

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参考文献(22)

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改进Multi-scale ResNet的蔬菜叶部病害识别

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Identification of vegetable leaf diseases based on improved Multi-scale ResNet

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