Identification of wheat seedling varieties based on MssiapNet

文献类型： 外文期刊

第一作者： Feng, Yongqiang

作者： Feng, Yongqiang;Liu, Chengzhong;Han, Junying;Xing, Xue;Lu, Qinglin

作者机构：

关键词： wheat; seedlings; variety identification; scse attention; visualized; feature fusion

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：5.6； 五年影响因子：6.8 ）

ISSN： 1664-462X

年卷期： 2024 年 14 卷

页码：

收录情况： SCI

摘要： Introduction In the actual planting of wheat, there are often shortages of seedlings and broken seedlings on long ridges in the field, thus affecting grain yield and indirectly causing economic losses. Variety identification of wheat seedlings using physical methods timeliness and is unsuitable for universal dissemination. Recognition of wheat seedling varieties using deep learning models has high timeliness and accuracy, but fewer researchers exist. Therefore, in this paper, a lightweight wheat seedling variety recognition model, MssiapNet, is proposed.Methods The model is based on the MobileVit-XS and increases the model's sensitivity to subtle differences between different varieties by introducing the scSE attention mechanism in the MV2 module, so the recognition accuracy is improved. In addition, this paper proposes the IAP module to fuse the identified feature information. Subsequently, training was performed on a self-constructed real dataset, which included 29,020 photographs of wheat seedlings of 29 varieties.Results The recognition accuracy of this model is 96.85%, which is higher than the other nine mainstream classification models. Although it is only 0.06 higher than the Resnet34 model, the number of parameters is only 1/3 of that. The number of parameters required for MssiapNet is 29.70MB, and the single image Execution time and the single image Delay time are 0.16s and 0.05s. The MssiapNet was visualized, and the heat map showed that the model was superior for wheat seedling variety identification compared with MobileVit-XS.Discussion The proposed model has a good recognition effect on wheat seedling varieties and uses a few parameters with fast inference speed, which makes it easy to be subsequently deployed on mobile terminals for practical performance testing.

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