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Wheat-Seed Variety Recognition Based on the GC_DRNet Model

文献类型： 外文期刊

作者： Xing, Xue 1 ; Liu, Chengzhong 1 ; Han, Junying 1 ; Feng, Quan 2 ; Lu, Qinglin 3 ; Feng, Yongqiang 1 ;

作者机构： 1.Gansu Agr Univ, Coll Informat Sci & Technol, Lanzhou 730070, Peoples R China

2.Gansu Agr Univ, Coll Mech & Elect Engn, Lanzhou 730070, Peoples R China

3.Gansu Acad Agr Sci, Wheat Res Inst, Lanzhou 730070, Peoples R China

关键词： convolutional neural network; wheat seeds; image recognition; ResNet18; wheat-seed recognition model

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.6 ）

ISSN：

年卷期： 2023 年 13 卷 11 期

页码：

收录情况： SCI

摘要： Wheat is a significant cereal for humans, with diverse varieties. The growth of the wheat industry and the protection of breeding rights can be promoted through the accurate identification of wheat varieties. To recognize wheat seeds quickly and accurately, this paper proposes a convolutional neural network-based image-recognition method for wheat seeds, namely GC_DRNet. The model is based on the ResNet18 network and incorporates the dense network idea by changing its residual module to a dense residual module and introducing a global contextual module, reducing the network model's parameters and improving the network's recognition accuracy. Experiments were conducted on the self-constructed wheat-seed dataset and the publicly available dataset CIFAR-100 by combining GC_DRNet with network models such as ResNet18, ResNet34, ResNet50, and DenseNet121. The GC_DRNet model achieved a recognition accuracy of 96.98% on the wheat-seed dataset, which was improved by 2.34%, 1.43%, 2.05%, and 1.77% compared to ResNet18, ResNet34, ResNet50, and DenseNet121, respectively. On the CIFAR-100 dataset, the recognition accuracy of the GC_DRNet model was 80.77%, which improved the accuracy of ResNet18, ResNet34, ResNet50, and DenseNet121 by 8.19%, 1.6%, 9.59%, and 16.29%, respectively. Analyzing the confusion-matrix results of the wheat-seed dataset, the average recognition precision of the test set was 97.02%, the recall rate was 96.99%, and the F1 value was 96.98%. The parameter size of the GC_DRNet model was smaller than that of the other three models, only 11.65MB. The experimental results show that the GC_DRNet has a high level of recognition accuracy and detection capability for images of wheat seeds and provides technical support for wheat-seed identification.

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