Development of Detector for Wheat Powdery Mildew Based on Lightweight Improved Deep Learning Model

被引：0

作者：

Li Z. ^{[1
]}

Li J. ^{[2
]}

Wang N. ^{[1
]}

Zhang Y. ^{[3
]}

Sun H. ^{[1
,2
]}

Li M. ^{[1
,3
]}

机构：

[1] Key Laboratory oj Smart Agriculture Systems Integration, Ministry of Education, China Agricultural University, Beijing

[2] Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing

[3] Yantai Institute of China Agricultural University, Yantai

来源：

Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2023年 / 54卷

关键词：

disease characters; lightweight model; performance testing; target detection; wheat powdery mildew;

D O I：

10.6041/j.issn.1000-1298.2023.S2.037

中图分类号：

学科分类号：

摘要：

Wheat diseases have frequently threatened the yield and quality of wheat production. In order to quickly and comprehensively monitor wheat diseases in the field and identify diseases in different growth parts of wheat based on the characteristics of wheat disease, a dual camera wheat disease detection device based on a lightweight model was designed. The device was composed of a dual camera acquisition module and a main control module, and it can collect and detect wheat powdery mildew at multiple locations in cooperation with the disease detection software system. In order to ensure the feasibility of the model deployment in the detection device, a lightweight improved powdery mildew target detection model based on YOLO v7 tiny model (YOLO v7tiny ShuffleNet vl, YT SFNet) was proposed. To verify the accuracy and detection speed of the lightweight model, it was trained and compared with the YOLO v7 tiny model. The results showed that the YT SFNet model improved the average accuracy by 0. 57 percentage points compared with YOLO v7 tiny model. The detection time and model size were decreased by 2. 4 ms and 3. 2 MB, respectively. Finally, the lightweight model and software system were transplanted to the main control module of the device, and a test set was created to test the performance of the device's detection accuracy and detection speed. Its recognition accuracy for the test set was 86. 2%, with good stability in detection speed, and the average time spent on the entire process of processing, detecting, and displaying and saving a single disease image was 0. 507 9 s. © 2023 Chinese Society of Agricultural Machinery. All rights reserved.

引用

页码：314 / 322

页数：8

共 29 条

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