Zhao Panzhen, Wang Songfeng, Duan Shijiang, Wang Aihua, Meng Lingfeng, Hu Yichong
Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, China.
Graduate School of Chinese Academy of Agricultural Sciences, Beijing, China.
Front Plant Sci. 2024 Dec 18;15:1474731. doi: 10.3389/fpls.2024.1474731. eCollection 2024.
Due to the constraints of the tobacco leaf curing environment and computational resources, current image classification models struggle to balance recognition accuracy and computational efficiency, making practical deployment challenging. To address this issue, this study proposes the development of a lightweight classification network model for recognizing tobacco leaf curing stages (TCSRNet). Firstly, the model utilizes an Inception structure with parallel convolutional branches to capture features at different receptive fields, thereby better adapting to the appearance variations of tobacco leaves at different curing stages. Secondly, the incorporation of Ghost modules significantly reduces the model's computational complexity and parameter count through parameter sharing, enabling efficient recognition of tobacco leaf curing stages. Lastly, the design of the Multi-scale Adaptive Attention Module (MAAM) enhances the model's perception of key visual information in images, emphasizing distinctive features such as leaf texture and color, which further improves the model's accuracy and robustness. On the constructed tobacco leaf curing stage dataset (with color images sized 224×224 pixels), TCSRNet achieves a classification accuracy of 90.35% with 158.136 MFLOPs and 1.749M parameters. Compared to models such as ResNet34, GhostNet, ShuffleNetV2×1.5, EfficientNet-b0, MobileViT-xs, MobileNetV2, MobileNetV3-large, and MobileNetV3-small, TCSRNet demonstrates superior performance in terms of accuracy, FLOPs, and parameter count. Furthermore, when evaluated on the public V2 Plant Seedlings dataset, TCSRNet maintains an impressive accuracy of 97.15% compared to other advanced network models. This research advances the development of lightweight models for recognizing tobacco leaf curing stages, providing theoretical support for smart tobacco curing technologies and injecting new momentum into the digital transformation of the tobacco industry.
由于烟叶烘烤环境和计算资源的限制，当前的图像分类模型难以在识别准确率和计算效率之间取得平衡，这使得实际部署具有挑战性。为了解决这个问题，本研究提出开发一种用于识别烟叶烘烤阶段的轻量级分类网络模型（TCSRNet）。首先，该模型利用具有并行卷积分支的Inception结构来捕捉不同感受野的特征，从而更好地适应不同烘烤阶段烟叶的外观变化。其次，Ghost模块的引入通过参数共享显著降低了模型的计算复杂度和参数数量，实现了对烟叶烘烤阶段的高效识别。最后，多尺度自适应注意力模块（MAAM）的设计增强了模型对图像中关键视觉信息的感知，突出了叶片纹理和颜色等显著特征，进一步提高了模型的准确性和鲁棒性。在构建的烟叶烘烤阶段数据集（彩色图像大小为224×224像素）上，TCSRNet在158.136 MFLOPs和1.749M参数的情况下实现了90.35%的分类准确率。与ResNet34、GhostNet、ShuffleNetV2×1.5、EfficientNet-b0、MobileViT-xs、MobileNetV2、MobileNetV3-large和MobileNetV3-small等模型相比，TCSRNet在准确率、FLOPs和参数数量方面表现出卓越的性能。此外，在公共V2植物幼苗数据集上进行评估时，与其他先进网络模型相比，TCSRNet保持了令人印象深刻的97.15%的准确率。本研究推动了用于识别烟叶烘烤阶段的轻量级模型的发展，为智能烟叶烘烤技术提供了理论支持，并为烟草行业的数字化转型注入了新动力。
