湖北农业科学2025，Vol.64Issue(8)：17-23,7.DOI:10.14088/j.cnki.issn0439-8114.2025.08.003
基于机器视觉技术的辣椒果实炭疽病病害分级方法研究
Research on anthracnose disease grading method for pepper fruits based on machine vision technology
摘要
Abstract
To address the issues of strong subjectivity and low detection efficiency in traditional pepper(Capsicum annuum L.)disease grading methods,this study proposed a machine vision-based semantic segmentation model for automated rapid grading and identifica-tion of anthracnose-infected pepper fruits.Under controlled enclosed environments,sunlight was simulated,and images of healthy fruits and four disease severity levels across different pepper varieties were collected.Principal component analysis was em-ployed to reduce redundant image features,extracting three key color components(Cb,Cr,R)with a cumulative contribution rate of 95%.Model 1(Decision Tree),model 2(Naive Bayes),model 3(SVM),and model 4(KNN)were trained.Model 1(Decision Tree)demonstrated the shortest training time and highest precision,establishing it as the optimal prediction model for anthracnose dis-ease grading.It required low computational resources and occupied minimal memory,facilitating future edge deployment.Model 1 achieved precision rates of 90.3%～98.2%for pepper fruits and 75.3%～80.7%for disease spots.Its recall rate for anthracnose disease grading was 73.3%～93.3%,with the recall rate for healthy peppers(Level 0)exceeding 90.0%.The prediction results of model 1 showed high consistency with manual annotations across all disease levels,verifying its applicability in automated disease monitoring systems as a replacement for manual visual grading methods.关键词
辣椒(Capsicum annuum L.)果实/机器视觉技术/炭疽病/病害分级Key words
pepper(Capsicum annuum L.)fruit/machine vision technology/anthracnose/disease grading分类
信息技术与安全科学引用本文复制引用
邹玮,岳延滨,李莉婕,陈维榕,韩威,朱存洲..基于机器视觉技术的辣椒果实炭疽病病害分级方法研究[J].湖北农业科学,2025,64(8):17-23,7.基金项目
贵州省农业科学院青年基金项目(黔农科一般基金[2024]25号) （黔农科一般基金[2024]25号）
贵州省科技计划项目(黔科合支撑[2021]一般173) （黔科合支撑[2021]一般173）
