摘要
根据锈病、弯孢菌叶斑病、灰斑病、小斑病及褐斑病等五种玉米病斑图像的实际情况,在图像分割和特征提取的基础上,利用朴素贝叶斯分类器的统计学习方法,实现玉米叶部病斑的分类识别。研究结果表明,对五种玉米叶部病害的诊断精度在83%以上。贝叶斯分类器具有网络结构简单、易于扩展等特点,对玉米叶部病害的分类识别效果较好,也为其它作物病害图像识别的研究提供了借鉴。
A naive Bayesian classifier method is proposed to classify maize leaf disease according to five kinds of actual maize disease images,which are segmented and extracted feather from at first.The result shows that the precision of maize disease identifying is higher than 83%.Bayesian classifier is excellent at simple network structure and extending easily.It is effective for classifying maize disease and it can use for reference for the image recognition research on other crops.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第5期193-195,共3页
Computer Engineering and Applications
基金
国家高技术研究发展计划(863)(the National High- Tech Research and Development Plan of China under Grant No.2002AA243041)
国家自然科学基金(the National Natural Science Foundation of China under Grant No.30360047)
关键词
朴素贝叶斯方法
玉米叶部病害
特征提取
分类识别
特征约简
naive Bayesian method
maize leaf disease
feather extracting
classify and identify
feather reduction
