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《Journal of Anhui Agricultural Sciences》 2018-01
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Diagnosis of Heavy Metal Stress in Leaf of Rice in Greenhouse Based on Hyperspectral Image

ZHANG Shuang-yin;WANG Yun-jiang;OUYANG Wei;School of Resources and Environmental Science,Wuhan University;School of Resources and Environment,Huazhong Agricultural University;  
[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 2 nd 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.
【Fund】: 国家自然科学基金项目(213-164538)
【CateGory Index】: S511;X503.231
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