安徽农业科学2018，Vol.46Issue(1)：5-9,5.
基于成像高光谱数据的温室水稻重金属胁迫诊断研究
Diagnosis of Heavy Metal Stress in Leaf of Rice in Greenhouse Based on Hyperspectral Image
摘要
Abstract
[Objective] This study aimed to diagnose specific stress categories and stress gradients from the cross-stress of Cd and Pb with high spectral imagery data of greenhouse rice leaves.[Method] After double factor variance analysis,the characteristic bands for diagnosis were selected,and two models of SVM and BP neural network were compared in terms of diagnostic ability.[Result] The results showed that with the pretreatment of 2nd spectral derivative,SVM could achieve very good diagnostic effect for Cd and Pb stress.6 characteristic bands were identified sensitive to Cd stress,and 10 characteristic bands were identified sensitive to Pb stress.The accuracy of diagnostic Cd stress based on SVM was 86％,and the diagnostic accuracy of three gradients were 75％,90％ and 96％,while the accuracy of diagnostic Pb stress based on SVM was 85％,and the diagnostic accuracy of three gradients were 83％,85％ and 88％.The diagnostic accuracy of Pb stress based BP neutral network was 88％,and the diagnostic accuracy of three gradients were 69％,75％ and 75％,while the diagnostic accuracy of Pb stress was 88％,and the diagnostic accuracy of three gradients were 81％,69％ and 69％.[Conclusion] It is feasible to diagnose heavy metal Cd and Pb stress from hyperspectral spectral imaging data of vegetation,and the accuracy of SVM is satisfied.关键词
高光谱/重金属诊断/SVM/BP神经网络Key words
Hyperspectral/Heavy metal diagnosis/SVM/BP neural network分类
农业科技引用本文复制引用
张双印,王云将,欧阳炜,费腾..基于成像高光谱数据的温室水稻重金属胁迫诊断研究[J].安徽农业科学,2018,46(1):5-9,5.基金项目
国家自然科学基金项目(213-164538). （213-164538）
