Song Shaozhong, Chen Zhenyang, Yu Helong, Xue Mingxuan, Liu Junling
School of Data Science and Artificial Intelligence, Jilin Engineering Normal University, Changchun, China.
Smart Agriculture Research Institute, Jilin Agricultural University, Changchun, China.
Front Plant Sci. 2025 Feb 13;15:1474906. doi: 10.3389/fpls.2024.1474906. eCollection 2024.
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.
绿豆种子在农业生产和食品加工中非常重要，但由于其品种繁多且外观相似，传统分类方法具有挑战性。为解决这一问题，本研究提出了一种基于深度学习的方法。在本研究中，基于深度学习模型MobileNetV2，针对绿豆种子提出了DMS模块，并通过引入ECA模块和Mish激活函数，提出了一种高精度网络模型，即HPMobileNet，旨在探索将其应用于图像识别领域，以快速准确地分类不同品种的绿豆种子。本研究收集了八个不同品种的绿豆种子，并通过阈值分割和图像增强技术共获得了34890张图像。以HPMobileNet作为主要网络模型，通过在大规模绿豆种子图像数据集上进行训练和微调，实现了高效的特征提取分类和识别能力。实验结果表明，HPMobileNet在绿豆种子粒分类任务中表现出优异的性能，测试集上的准确率从87.40%提高到了94.01%，与其他经典网络模型相比，结果表明HPMobileNet取得了最佳效果。此外，本研究分析了学习率动态调整策略对模型的影响，并探索了未来进一步优化和应用的潜力。因此，本研究为绿豆种子分类和智慧农业技术的发展提供了有益的参考和实证依据。
