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ADDLight: An Energy-Saving Adder Neural Network for Cucumber Disease Classification

Author

Listed:
  • Chen Liu

    (College of Information Engineering, Northwest A&F University, Yangling, Xianyang 712100, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Chunjiang Zhao

    (College of Information Engineering, Northwest A&F University, Yangling, Xianyang 712100, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Huarui Wu

    (National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Xiao Han

    (National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Shuqin Li

    (College of Information Engineering, Northwest A&F University, Yangling, Xianyang 712100, China)

Abstract

It is an urgent task to improve the applicability of the cucumber disease classification model in greenhouse edge-intelligent devices. The energy consumption of disease diagnosis models designed based on deep learning methods is a key factor affecting its applicability. Based on this motivation, two methods of reducing the model’s calculation amount and changing the calculation method of feature extraction were used in this study to reduce the model’s calculation energy consumption, thereby prolonging the working time of greenhouse edge devices deployed with disease models. First, a cucumber disease dataset with complex backgrounds is constructed in this study. Second, the random data enhancement method is used to enhance data during model training. Third, the conventional feature extraction module, depthwise separable feature extraction module, and the squeeze-and-excitation module are the main modules for constructing the classification model. In addition, the strategies of channel expansion and = shortcut connection are used to further improve the model’s classification accuracy. Finally, the additive feature extraction method is used to reconstruct the proposed model. The experimental results show that the computational energy consumption of the adder cucumber disease classification model is reduced by 96.1% compared with the convolutional neural network of the same structure. In addition, the model size is only 0.479 MB, the calculation amount is 0.03 GFLOPs, and the classification accuracy of cucumber disease images with complex backgrounds is 89.1%. All results prove that our model has high applicability in cucumber greenhouse intelligent equipment.

Suggested Citation

  • Chen Liu & Chunjiang Zhao & Huarui Wu & Xiao Han & Shuqin Li, 2022. "ADDLight: An Energy-Saving Adder Neural Network for Cucumber Disease Classification," Agriculture, MDPI, vol. 12(4), pages 1-17, March.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:4:p:452-:d:777980
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    Cited by:

    1. Junchi Zhou & Wenwu Hu & Airu Zou & Shike Zhai & Tianyu Liu & Wenhan Yang & Ping Jiang, 2022. "Lightweight Detection Algorithm of Kiwifruit Based on Improved YOLOX-S," Agriculture, MDPI, vol. 12(7), pages 1-14, July.
    2. Chunyu Yan & Zhonghui Chen & Zhilin Li & Ruixin Liu & Yuxin Li & Hui Xiao & Ping Lu & Benliang Xie, 2022. "Tea Sprout Picking Point Identification Based on Improved DeepLabV3+," Agriculture, MDPI, vol. 12(10), pages 1-15, October.

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