Wang Chun, Wang Zejun, Chen Lijiao, Liu Weihao, Wang Xinghua, Cao Zhiyong, Zhao Jinyan, Zou Man, Li Hongxu, Yuan Wenxia, Wang Baijuan
College of Mechanical and Electrical Engineering, Yunnan Agricultural University, Kunming 650201, China.
Yunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, China.
Plants (Basel). 2025 Jun 27;14(13):1965. doi: 10.3390/plants14131965.
To achieve an efficient, non-destructive, and intelligent identification of tea plant seedlings under high-temperature stress, this study proposes an improved YOLOv11 model based on chlorophyll fluorescence imaging technology for intelligent identification. Using tea plant seedlings under varying degrees of high temperature as the research objects, raw fluorescence images were acquired through a chlorophyll fluorescence image acquisition device. The fluorescence parameters obtained by Spearman correlation analysis were found to be the maximum photochemical efficiency (Fv/Fm), and the fluorescence image of this parameter is used to construct the dataset. The YOLOv11 model was improved in the following ways. First, to reduce the number of network parameters and maintain a low computational cost, the lightweight MobileNetV4 network was introduced into the YOLOv11 model as a new backbone network. Second, to achieve efficient feature upsampling, enhance the efficiency and accuracy of feature extraction, and reduce computational redundancy and memory access volume, the EUCB (Efficient Up Convolution Block), iRMB (Inverted Residual Mobile Block), and PConv (Partial Convolution) modules were introduced into the YOLOv11 model. The research results show that the improved YOLOv11-MEIP model has the best performance, with precision, recall, and mAP50 reaching 99.25%, 99.19%, and 99.46%, respectively. Compared with the YOLOv11 model, the improved YOLOv11-MEIP model achieved increases of 4.05%, 7.86%, and 3.42% in precision, recall, and mAP50, respectively. Additionally, the number of model parameters was reduced by 29.45%. This study provides a new intelligent method for the classification of high-temperature stress levels of tea seedlings, as well as state detection and identification, and provides new theoretical support and technical reference for the monitoring and prevention of tea plants and other crops in tea gardens under high temperatures.
为实现高温胁迫下茶树幼苗的高效、无损、智能识别，本研究提出一种基于叶绿素荧光成像技术的改进YOLOv11模型用于智能识别。以不同程度高温下的茶树幼苗为研究对象，通过叶绿素荧光图像采集装置获取原始荧光图像。经Spearman相关性分析得到的荧光参数为最大光化学效率（Fv/Fm），并利用该参数的荧光图像构建数据集。对YOLOv11模型进行了如下改进。首先，为减少网络参数数量并保持较低计算成本，将轻量级MobileNetV4网络引入YOLOv11模型作为新的骨干网络。其次，为实现高效特征上采样，提高特征提取效率和准确性，减少计算冗余和内存访问量，将EUC（高效上卷积块）、iRMB（倒置残差移动块）和PConv（部分卷积）模块引入YOLOv11模型。研究结果表明，改进后的YOLOv11-MEIP模型性能最佳，精度、召回率和mAP50分别达到99.25%、99.19%和99.46%。与YOLOv11模型相比，改进后的YOLOv11-MEIP模型在精度、召回率和mAP50上分别提高了4.05%、7.86%和3.42%。此外，模型参数数量减少了29.45%。本研究为茶树幼苗高温胁迫水平分类以及状态检测与识别提供了一种新的智能方法，为茶园高温下茶树及其他作物的监测与防控提供了新的理论支持和技术参考。
