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Small sample and efficient crop pest recognition method based on transfer learning and data transformation

文献类型： 外文期刊

作者： Wei, Qingfeng 1 ; Li, Huan 3 ; Luo, Changshou 1 ; Yu, Jun 1 ; Zheng, Yaming 1 ; Wang, Furong 1 ; Zhang, Bao 4 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Inst Data Sci & Agr Econ, Beijing, Peoples R China

2.Beijing Res Ctr Engn Technol Rural Distance Infor, Beijing, Peoples R China

3.CRRC Grp Co Ltd, Changsha, Hunan, Peoples R China

4.Landscape Bur Xinzhou Dist, Wuhan, Hubei, Peoples R China

关键词： Transfer learning; data transformation; pest identification; crops

期刊名称：JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING （ 影响因子：0.5； 五年影响因子：0.5 ）

ISSN： 1472-7978

年卷期： 2022 年 22 卷 5 期

页码：

收录情况： SCI

摘要： In order to solve the problem of long training time and large samples required by traditional image recognition model, a method of crop pest recognition based on transfer learning and data conversion was proposed. It takes CNN models such as Inception V3, VGG16, ResNet as the backbone structure. And the transfer learning was used to improve the model effect. The original picture data was expanded through the transformation of flip, rotation, scale, crop, translation and shading. Based on the data of 11 common pests such as white grub, east asian locust and whitefly etc., the model training and recognition was carried out. The result shows that, the accuracy of transfer learning model is higher than that of non-transfer learning model. The Inception V3 model performs well of all, the recognition accuracy is more than 98.94%. Through the analysis of cross entropy and confusion matrix, data transformation is helpful to improve the accuracy of the model with small sample.

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