201. Detection and Differentiation between Laurel Wilt Disease, Phytophthora Disease, and Salinity Damage Using a Hyperspectral Sensing Technique
- Author
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Ana Castro, Jaafar Abdulridha, Reza Ehsani, Department of Agriculture (US), and Ministry of Higher Education and Scientific Research (Iraq)
- Subjects
0106 biological sciences ,Hyperspectral classification ,Veterinary medicine ,Laurel wilt ,spectroradiometer ,hyperspectral classification ,remote sensing ,multilayer perceptron ,Plant Science ,Biology ,medicine.disease_cause ,01 natural sciences ,Infestation ,Botany ,Spectroradiometer ,medicine ,Multilayer perceptron ,lcsh:Agriculture (General) ,Hyperspectral imaging ,04 agricultural and veterinary sciences ,Remote sensing ,biology.organism_classification ,lcsh:S1-972 ,Salinity ,040103 agronomy & agriculture ,0401 agriculture, forestry, and fisheries ,Classification methods ,Phytophthora ,Agronomy and Crop Science ,010606 plant biology & botany ,Food Science - Abstract
Laurel wilt (Lw) is a fatal disease. It is a vascular pathogen and is considered a major threat to the avocado industry in Florida. Many of the symptoms of Lw resemble those that are caused by other diseases or stress factors. In this study, the best wavelengths with which to discriminate plants affected by Lw from stress factors were determined and classified. Visible-near infrared (400–950 nm) spectral data from healthy trees and those with Lw, Phytophthora, or salinity damage were collected using a handheld spectroradiometer. The total number of wavelengths was averaged in two ranges: 10 nm and 40 nm. Three classification methods, stepwise discriminant (STEPDISC) analysis, multilayer perceptron (MLP), and radial basis function (RBF), were applied in the early stage of Lw infestation. The classification results obtained for MLP, with percent accuracy of classification as high as 98% were better than STEPDISC and RBF. The MLP neural network selected certain wavelengths that were crucial for correctly classifying healthy trees from those with stress trees. The results showed that there were sufficient spectral differences between laurel wilt, healthy trees, and trees that have other diseases; therefore, a remote sensing technique could diagnose Lw in the early stage of infestation., The author would like to thank the Florida Dept. of Agriculture and Consumer Services, USDA Specialty Block Grant No. 019730 for partially funding this study and the Ministry of Higher Education and Scientific Research of Republic of Iraq for supporting the graduate student working on this project.
- Published
- 2016
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