基于边缘智能的茶叶病害识别

doi:10.13733/j.jcam.issn.20955553.2022.06.023

摘要/Abstract

摘要： 为实现在资源有限的边缘设备上自动识别茶叶病害，提出基于边缘智能的深度学习模型部署方法。首先使用自动化模型剪枝（AMC）算法在PlantVillage数据集上对MobileNetV2进行模型剪枝，然后使用剪枝率为90%时生成的模型AMC-MobileNetV2在自建茶叶病害数据集上进行迁移学习训练，最后将获得的茶叶病害识别模型部署在边缘设备上。试验结果表明，AMC-MobileNetV2与MobileNetV2相比，在模型参数量减少94.5%、存储体积减小93.4%的情况下，提高模型在资源有限边缘设备上的识别速度，对8种茶叶病害识别平均准确率高达97.42%。研究结果可应用于茶园病害防治机器人。

关键词: 茶叶病害, 自动识别, 迁移学习, 自动化模型剪枝, 边缘智能

Abstract: To achieve automatic identification of tea diseases on resourcelimited edge devices, a deep learning model deployment method based on edge intelligence is proposed. Firstly, the automated model pruning （AMC） algorithm was used to prune the model of MobileNetV2 on the PlantVillage dataset. Then the model AMC-MobileNetV2 generated at a pruning rate of 90% was used to perform migration learning training on the selfbuilt tea disease dataset. Finally, the obtained tea disease recognition model was deployed on the edge devices. The experimental results show that AMC-MobileNetV2 improves the recognition speed of the model on resourcelimited edge devices with a 94.5% reduction in the number of model parameters and 93.4% reduction in storage volume compared with MobileNetV2, and the average accuracy of the recognition of eight tea diseases is as high as 97.42%. The results of this study can be applied to tea garden disease control robots.

Key words: tea leaf diseases, automatic recognition, transfer learning, automatic pruning, edge intelligence

中图分类号:

S126

李博, 江朝晖, 洪石兰, 饶元, 张武, . 基于边缘智能的茶叶病害识别[J]. 中国农机化学报, 2022, 43(6): 175-180.

Li Bo, Jiang Zhaohui, Hong Shilan, Rao Yuan, Zhang Wu. . Tea leaf diseases recognition based on edge intelligence[J]. Journal of Chinese Agricultural Mechanization, 2022, 43(6): 175-180.

参考文献

［1］侯俊铭, 姚恩超, 朱红杰. 基于卷积神经网络的蓖麻种子损伤分类研究［J］. 农业机械学报, 2020, 51（S1）: 440-449.
Hou Junming, Yao Enchao, Zhu Hongjie. Classification of castor seed damage based on convolutional neural network ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51（S1）: 440-449.
［2］丁永军, 张晶晶, 李民赞. 基于卷积胶囊网络的百合病害识别研究［J］. 农业机械学报, 2020, 51（12）: 246-251-331.
Ding Yongjun, Zhang Jingjing, Li Minzan. Disease detection of lily based on convolutional capsule network ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51（12）: 246-251, 331.
［3］汪传建, 赵庆展, 马永建, 等. 基于卷积神经网络的无人机遥感农作物分类［J］. 农业机械学报, 2019, 50（11）: 161-168.
Wang Chuanjian, Zhao Qingzhan, Ma Yongjian, et al. Crop identification of drone remote sensing based on convolutional neural network ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50（11）: 161-168.
［4］王国伟, 刘嘉欣. 基于卷积神经网络的玉米病害识别方法研究［J］. 中国农机化学报, 2021, 42（2）: 139-145.
Wang Guowei, Liu Jiaxin. Research on corn disease recondition method based on convolutional neural network ［J］. Journal of Chinese Agricultural Mechanization, 2021, 42（2）: 139-145.
［5］赵立新, 侯发东, 吕正超, 等. 基于迁移学习的棉花叶部病虫害图像识别［J］. 农业工程学报, 2020, 36（7）: 184-191.
Zhao Lixin, Hou Fadong, Lu Zhengchao, et al. Image recognition of cotton leaf diseases and pests based on transfer learning ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36（7）: 184-191.
［6］余小东, 杨孟辑, 张海清, 等. 基于迁移学习的农作物病虫害检测方法研究与应用［J］. 农业机械学报, 2020, 51（10）: 252-258.
Yu Xiaodong, Yang Mengji, Zhang Haiqing, et al. Research and application of crop diseases detection method based on transfer learning ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51（10）: 252-258.
［7］ Gu R, Niu C, Wu F, et al. From serverbased to clientbased machine learning: A comprehensive survey ［J］. ACM Computing Surveys （CSUR）, 2021, 54（1）: 1-36.
［8］马富齐, 王波, 董旭柱, 等. 电力视觉边缘智能: 边缘计算驱动下的电力深度视觉加速技术［J］. 电网技术, 2020, 44（6）: 2020-2029.
Ma Fuqi, Wang Bo, Dong Xuzhu, et al. Power vision edge intelligence: Power depth vision acceleration technology driven by edge computing ［J］. Power System Technology, 2020, 44（6）: 2020-2029.
［9］ GonzalezHuitron V, LeónBorges José A, RodriguezMata A E, et al. Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4 ［J］. Computers and Electronics in Agriculture, 2021, 181: 105951.
［10］ Sandler M, Howard A, Zhu M, et al. Mobilenetv2: Inverted residuals and linear bottlenecks ［C］. Proceedings of the IEEE conference on computer vision and pattern recognition, 2018: 4510-4520.
［11］ Zoph B, Vasudevan V, Shlens J, et al. Learning transferable architectures for scalable image recognition ［C］. Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 8697-8710.
［12］ Franois Chollet. Xception: Deep learning with depthwise separable convolutions ［C］. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1251-1258.
［13］ Andrew Howard, Mark Sandler, Grace Chu, et al. Searching for mobilenetv3［C］. Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 1314-1324.
［14］ Durmus H, Günes E O, Kirci M. Disease detection on the leaves of the tomato plants by using deep learning ［C］. 2017 6th International Conference on AgroGeoinformatics. IEEE, 2017: 1-5.
［15］王健, 刘雪花. 基于深度可分离卷积的苹果叶病理识别［J］. 计算机系统应用, 2020, 29（11）: 190-195.
Wang Jian, Liu Xuehua. Pathological recognition of apple leaves based on deeply separable convolution ［J］. Computer Systems & Applications, 2020, 29（11）: 190-195.
［16］王冠, 王建新, 孙钰. 面向边缘计算的轻量级植物病害识别模型［J］. 浙江农林大学学报, 2020, 37（5）: 978-985.
Wang Guan, Wang Jianxin, Sun Yu. Lightweight plant disease recognition model for edge computing ［J］. Journal of Zhejiang A & F University, 2020, 37（5）: 978-985.
［17］刘阳, 高国琴. 采用改进的SqueezeNet模型识别多类叶片病害［J］. 农业工程学报, 2021, 37（2）: 187-195.
Liu Yang, Gao Guoqin. Identification of multiple leaf diseases using improved SqueezeNet model ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37（2）: 187-195.
［18］刘连忠, 李孟杰, 宁井铭. 基于时序巡航图像的茶树生长监测研究［J］. 浙江农业学报, 2020, 32（05）: 886-896.
Liu Lianzhong, Li Mengjie, Ning Jingming. Tea plant growth monitoring based on time series cruise images ［J］. Acta Agriculturae Zhejiangensis, 2020, 32（5）: 886-896.
［19］ Molchanov P, Mallya A, Tyree S, et al. Importance estimation for neural network pruning ［C］. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 11264-11272.
［20］ Liu Z, Sun M, Zhou T, et al. Rethinking the value of network pruning ［C］. International conference on learning representations. 2018.
［21］ He Y, Lin J, Liu Z, et al. AMC: AutoML for model compression and acceleration on mobile devices ［C］. Proceedings of the European Conference on Computer Vision （ECCV）, 2018: 784-800.
［22］谢军, 江朝晖, 李博, 等. 基于二次迁移模型的小样本茶树病害识别［J］. 江苏农业科学, 2021, 49（6）: 176-182.
［23］杨观赐, 杨静, 李少波, 等. 基于Dopout与ADAM优化器的改进CNN算法［J］. 华中科技大学学报: 自然科学版, 2018, 46（7）: 122-127.
Yang Guanci, Yang Jing, Li Shaobo, et al. Modified CNN algorithm based on Dropout and ADAM optimizer ［J］. Journal of Huazhong University of Science and Technology （Natural Science Edition）, 2018, 46（7）: 122-127.
［24］蒋文斌, 彭晶, 叶阁焰. 深度学习自适应学习率算法研究［J］. 华中科技大学学报（自然科学版）, 2019, 47（5）: 79-83.
Jiang Wenbin, Peng Jing, Ye Geyan. Research on adaptive learning rate algorithm in deep learning ［J］. Journal of Huazhong University of Science and Technology （Natural Science Edition）, 2019, 47（5）: 79-83.
［25］ Mittal S. A Survey on optimized implementation of deep learning models on the NVIDIA Jetson platform ［J］. Journal of systems architecture, 2019, 97: 428-442.

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