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

文献类型： 外文期刊

第一作者： Liu, Chen

作者： Liu, Chen;Zhao, Chunjiang;Li, Shuqin;Liu, Chen;Zhao, Chunjiang;Wu, Huarui;Han, Xiao;Liu, Chen;Zhao, Chunjiang;Wu, Huarui;Han, Xiao

作者机构：

关键词： cucumber disease diagnosis; image classification; low calculation energy consumption; additive feature extraction method; lightweight convolution model

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.408； 五年影响因子：3.459 ）

ISSN：

年卷期： 2022 年 12 卷 4 期

页码：

收录情况： SCI

摘要： 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.

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