基于田间采集的大量茶树春梢生育图片,借助计算机视觉技术,利用目标检测算法YOLOv5构建茶树新梢不同生育阶段的识别模型,测试结果表明该模型具有较高的检测精度。进一步探究了Image-J软件处理以及基于Gray值、RGB值、HSV值的阈值切割图像处理方法在茶叶面积处理方面的应用,比较了不同方法的准确度和运行效率。结果表明,基于HSV阈值切割法的茶树叶片面积算法准确率在94%以上,表现优于RGB阈值切割法。研究结果为开发茶树新梢生育进度智能识别模型和叶片性状信息提取算法提供了技术支撑,为采茶机械的茶芽自动识别模块的研发提供了理论基础。
In this study, based on the picture collection of tea shoot growth in the field, we used deep learning target detection algorithm YOLOv5 to construct a model for identifying different growth stages of tea shoots, and the testing results indicate that the model had high accuracy. Furthermore, the Image-J software and the image processing methods of threshold cutting based on Gray, RGB and HSV values were applied to process tea leaf area, and the accuracy and efficiency of different methods were compared. The results show that the accuracy of HSV-based algorithm system of cutting tea leaves and automatically calculating tea leaf area was over 94%, which had better performance than RGB-based algorithm system. The research results provide technical support for the intelligent recognition model of tea growth state and information extraction algorithm of leaf traits, and also build a theoretical basis for the development of tea bud automatic recognition module of tea plucking machinery.
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