Detection of citrus Huanglongbing based on image feature extraction and two-stage BPNN modeling
作者机构：International Lab of Agricultural Aviation Pesticides Spraying TechnologyGuangzhou 510642China College of Electronical EngineeringSouth China Agricultural UniversityGuangzhou 510642China College of EngineeringSouth China Agricultural UniversityGuangzhou 510642China
出 版 物：《International Journal of Agricultural and Biological Engineering》 (国际农业与生物工程学报（英文）)
年 卷 期：2016年第9卷第6期
页 面：20-26页
核心收录：
学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：the supporting of National Natural Science Foundation of China:The research of citrus Huanglongbing in-field detection based on low-altitude multi-sensor fusion(Grant No.61675003) the National Key Research and Development Plan:High efficient ground and aerial spraying technology and intelligent equipment(Grant No.2016YFD0200700)
主 题：citrus leaf Huanglongbing texture and color features feature extraction two-stage back propagation neural network
摘 要：Citrus Huanglongbing(HLB),which is spread by the citrus psyllid,is the most destructive disease of citrus *** no effective cure for the disease has been reported,detection and removal of infected trees can prevent *** indicative of HLB can be present in both HLB-positive trees and HLB-negative trees,making identification of infected trees difficult.A detection method for citrus HLB based on image feature extraction and two-stage back propagation neural network(BPNN)modeling was investigated in this *** identification method for eight different classes including healthy,HLB and non-HLB symptoms was ***-four statistical features including color and texture were extracted for each leaf sample,following the two-stage BPNN to model and identify HLB-positive leaves from HLB-negative *** discrimination accuracy can reach approximately 92%which shows that this method based on visual image processing can perform well in detecting citrus HLB.
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