Wang Mengjie, Shi Yali, Li Yaping, Meng Hewei, Ding Zezhong, Tian Zhengrui, Dong Chunwang, Chen Zhiwei
Tea Research Institute of Shandong Academy of Agricultural Sciences, Jinan 250100, China.
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
Foods. 2025 Mar 25;14(7):1125. doi: 10.3390/foods14071125.
Rapid and non-destructive detection methods for the withering degree of fresh tea leaves are crucial for ensuring high-quality tea production. Therefore, this study proposes a fresh tea withering degree detection model based on image classification confidence. The moisture percentage of fresh tea leaves is calculated by developing a weighted method that combines confidence levels and moisture labels, and the degree of withering is ultimately determined by incorporating the standard for wilted moisture content. To enhance the feature extraction ability and classification accuracy of the model, we introduce the Receptive-Field Attention Convolution (RFAConv) and Cross-Stage Feature Fusion Coordinate Attention (C2f_CA) modules. The experimental results demonstrate that the proposed model achieves a classification accuracy of 92.7%. Compared with the initial model, the detection accuracy was improved by 0.156. In evaluating the predictive performance of the model for moisture content, the correlation coefficients (Rp), root mean square error (RMSEP), and relative standard deviation (RPD) of category 1 in the test set were 0.9983, 0.006278, and 39.2513, respectively, and all performance were significantly better than PLS and CNN methods. This method enables accurate and rapid detection of tea leaf withering, providing crucial technical support for online determination during processing.
快速无损的鲜叶萎凋程度检测方法对于确保高品质茶叶生产至关重要。因此，本研究提出了一种基于图像分类置信度的鲜叶萎凋程度检测模型。通过开发一种结合置信度水平和水分标签的加权方法来计算鲜叶的水分百分比，并最终通过纳入萎凋水分含量标准来确定萎凋程度。为了提高模型的特征提取能力和分类准确率，我们引入了感受野注意力卷积（RFAConv）和跨阶段特征融合坐标注意力（C2f_CA）模块。实验结果表明，所提出的模型实现了92.7%的分类准确率。与初始模型相比，检测准确率提高了0.156。在评估模型对水分含量的预测性能时，测试集中类别1的相关系数（Rp）、均方根误差（RMSEP）和相对标准偏差（RPD）分别为0.9983、0.006278和39.2513，所有性能均显著优于PLS和CNN方法。该方法能够准确快速地检测茶叶萎凋情况，为加工过程中的在线测定提供关键技术支持。
