Rapid and accurate classification of mung bean seeds based on HPMobileNet

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Dades bibliogràfiques
Publicat a:	Frontiers in Plant Science vol. 15 (Feb 2025), p. 1474906-1474927
Autor principal:	Song, Shaozhong
Altres autors:	Chen, Zhenyang, Yu, Helong, Xue, Mingxuan, Liu, Junling
Publicat:	Frontiers Media SA
Matèries:	Accuracy Deep learning Datasets Classification Discriminant analysis Agricultural technology Digital agriculture Image processing Food processing Machine learning Seeds Beans Raw materials Food processing industry Spectrum analysis Image enhancement Image segmentation Computer vision Neural networks Support vector machines Impact analysis Agricultural production Algorithms Economic Vigna radiata
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Descripció
Resum:	Mung bean seeds are very important in agricultural production and food processing, but due to their variety and similar appearance, traditional classification methods are challenging, to address this problem this study proposes a deep learning-based approach. In this study, based on the deep learning model MobileNetV2, a DMS block is proposed for mung bean seeds, and by introducing the ECA block and Mish activation function, a high-precision network model, i.e., HPMobileNet, is proposed, which is explored to be applied in the field of image recognition for the fast and accurate classification of different varieties of mung bean seeds. In this study, eight different varieties of mung bean seeds were collected and a total of 34,890 images were obtained by threshold segmentation and image enhancement techniques. HPMobileNet was used as the main network model, and by training and fine-tuning on a large-scale mung bean seed image dataset, efficient feature extraction classification and recognition capabilities were achieved. The experimental results show that HPMobileNet exhibits excellent performance in the mung bean seed grain classification task, with the accuracy improving from 87.40% to 94.01% on the test set, and compared with other classical network models, the results show that HPMobileNet achieves the best results. In addition, this study analyzes the impact of the learning rate dynamic adjustment strategy on the model and explores the potential for further optimization and application in the future. Therefore, this study provides a useful reference and empirical basis for the development of mung bean seed classification and smart agriculture technology.
ISSN:	1664-462X
DOI:	10.3389/fpls.2024.1474906
Font:	Agriculture Science Database