[1]叶荣,何云,高泉,等.基于OpenCV和模糊数学的茶叶病害分析方法研究[J].北方园艺,2024,(4):145-153.[doi:10.11937/bfyy.20232536]
　YE Rong,HE Yun,GAO Quan,et al.Research on Tea Disease Analysis Methods Based on OpenCV and Fuzzy Mathematics[J].Northern Horticulture,2024,(4):145-153.[doi:10.11937/bfyy.20232536]

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基于OpenCV和模糊数学的茶叶病害分析方法研究

《北方园艺》[ISSN:1001-0009/CN:23-1247/S] 卷: 期数: 2024年4 页码: 145-153 栏目: 出版日期: 2024-02-29

Title:: Research on Tea Disease Analysis Methods Based on OpenCV and Fuzzy Mathematics

文章编号:: 1001-0009(2024)04-0145-09

作者:: 叶荣1; 何云2; 高泉3; 章广传3; 邵郭奇3; 李彤3; （1．云南农业大学食品科学技术学院，云南昆明 650051；2.云南农业大学大数据学院，云南昆明 650051；3.云南省作物生产与智慧农业重点实验室，云南昆明 650051）

Author(s):: YE Rong1; HE Yun2; GAO Quan3; ZHANG Guangchuan3; SHAO Guoqi3; LI Tong3; (1.College of Food Science and Technology，Yunnan Agricultural University，Kunming,Yunnan 650051；2.Big Data College,Yunnan Agricultural University，Kunming,Yunnan 650051；3.Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province，Kunming，Yunnan 650051)

关键词:: 图像处理; 算法; 茶叶; 模糊数学

Keywords:: image processing; algorithm; tea; fuzzy mathematics

分类号:: S 571.1

DOI:: 10.11937/bfyy.20232536

文献标志码:: A

摘要:: 茶叶病害的图像特征具有复杂性、多样性和模糊性，对茶叶的健康和群体造成威胁。目前缺乏相应的病害程度评价体系，评价指标权重未能准确反映茶叶当前生长情况。OpenCV是一个功能强大的图像处理工具，其图像边缘缺陷识别算法可以降低病害误判率，并具有良好的背景噪声处理效果。该研究利用OpenCV对茶叶病斑占比进行训练分析，得到定性评价结果。同时，通过专家对茶叶病斑大小、茶叶纹理、茶叶色泽和茶叶大小4个特征指标的重要性进行定量评价，引入模糊数学的隶属度理论后进行综合判断，建立茶叶病害的综合表达式。基于适宜评价权重，进一步判断不同因素对茶叶病害程度的影响。实际应用结果表明，结合OpenCV和模糊数学的评价方法通过可靠和稳定的图像识别技术，可以更科学合理地评估茶叶病害程度，提供有关茶叶健康生长和质量保证的参考价值。

Abstract:: The image features of tea diseases are complex,diverse,and fuzzy，posing a threat to the health and population of tea.At present，there is a lack of corresponding disease degree evaluation system，and the weight of evaluation indicators cannot accurately reflect the current growth status of tea.OpenCV is a powerful image processing tool，and its image edge defect recognition algorithm can reduce the misdiagnosis rate of diseases and has good background noise processing effects.This study used OpenCV to train and analyze the proportion of tea lesions，and obtained qualitative evaluation results.At the same time，the importance of the four characteristic indicators,namely the size of tea lesions spot size，tea texture，tea color，and tea size was quantitatively evaluated by experts.The membership degree theory of fuzzy mathematics was introduced for comprehensive judgment，and a comprehensive expression for tea disease was established.Based on the appropriate evaluation weights，further determine the impact of different factors on the degree of tea disease.The practical application results indicated that the combination of OpenCV and fuzzy mathematics evaluation methods，through reliable and stable image recognition technology，can more scientifically and reasonably evaluate the degree of tea diseases，providing reference value for the healthy growth and quality assurance of tea.

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备注/Memo

第一作者简介：叶荣（1993-），男，博士研究生，研究方向为深度学习与智慧农业。E-mail:307176152@qq.com.基金项目：云南省基础研究计划资助项目（202101AU070096）。收稿日期：2023-07-28

更新日期/Last Update: 2024-03-15