农业工程学报2012，Vol.28Issue(11)：152-157,后插6,7.DOI:10.3969/j.issn.1002-6819.2012.11.025
基于补偿模糊神经网络的脐橙不同病虫害图像识别
Image recognition of navel orange diseases and insect pests based on compensatory fuzzy neural networks
摘要
Abstract
In order to develop a universal machine vision alogorithm to identify disease and pests of naval orange, blue component of images of naval orange with disease and insect pests was processed with background removed to detect and extract the boundary of disease and insect pests symptoms with improved watershed algorithm. With this boundary the disease and insect pests areas of the original color image were marked. Red, green, and blue components in marked area were used to characterize the color features, and boundary fractal dimension of disease and insect pests area was taken as the shape feature. With the four feature values as compensatory fuzzy neural networks (CFNN) inputs, the CFNN mapper was established to identify diseases and insect pests. The test results showed that the average recognition correctness rate was up to 85.51% for four kinds of plant diseases and insect pests and mechanical damage. This method can be used to identify navel oranges plant diseases and insect pests.关键词
图像识别/模糊神经网络/水果/病虫害/机器视觉/脐橙Key words
image recognition, fuzzy neural network, fruits, plant diseases and insect pests, machine vision, navel orange分类
农业科技引用本文复制引用
温芝元,曹乐平..基于补偿模糊神经网络的脐橙不同病虫害图像识别[J].农业工程学报,2012,28(11):152-157,后插6,7.基金项目
湖南省科技计划项目(项目编号:20011NK3005) （项目编号:20011NK3005）
