Early Detection of Fire Blight Disease of Pome Fruit Trees Using Visible-NIR Spectrometry and Dimensionality Reduction Methods

Fire Blight (FB) is the most destructive bacterial disease of pome fruit trees around the world. In recent years, spectrometry has been shown to be an accurate and real-time sensing technology for plant disease detection. So, the main objective of this research is early detecting FB of pear trees by...

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Autores principales:	N Bagheri, H Mohamadi-Monavar
Formato:	article
Lenguaje:	EN FA
Publicado:	Ferdowsi University of Mashhad 2020
Materias:	classification early detection fire blight near-infrared spectrometry Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/53f5ede4c3b84fd6af675ba5e4b12466
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spelling	oai:doaj.org-article:53f5ede4c3b84fd6af675ba5e4b124662021-11-14T06:35:16ZEarly Detection of Fire Blight Disease of Pome Fruit Trees Using Visible-NIR Spectrometry and Dimensionality Reduction Methods2228-68292423-394310.22067/jam.v10i1.71911https://doaj.org/article/53f5ede4c3b84fd6af675ba5e4b124662020-03-01T00:00:00Zhttps://jame.um.ac.ir/article_34022_ad0cd282647e04b85afe902f50414bb0.pdfhttps://doaj.org/toc/2228-6829https://doaj.org/toc/2423-3943Fire Blight (FB) is the most destructive bacterial disease of pome fruit trees around the world. In recent years, spectrometry has been shown to be an accurate and real-time sensing technology for plant disease detection. So, the main objective of this research is early detecting FB of pear trees by using Visible-Near-infrared spectrometry. To get this goal, the reflectance spectra of healthy leaves (ND), non-symptomatic (NS), and symptomatic diseased leaves (SY) were captured in the visible–NIR spectral regions. In order to keep the important information of spectra and reduce the dimension of data, three linear and non-linear manifold-based learning techniques were applied such as, Principal Component Analysis (PCA), Sammon mapping and Multilayer auto-encoder (MAE). The output of manifold-based learning techniques was used as an input of the SIMCA (Soft independent modeling by class analogy) classification model to discriminate NS and ND leaves. Based on the results, the best classification accuracy obtained by using PCA on the 1st derivative spectra, with accuracy of 95.8%, 89.3%, and 91.6% for ND, NS, and SY samples, respectively. These results support the capability of manifold-based learning techniques for early detection of FB via spectrometry method.N BagheriH Mohamadi-MonavarFerdowsi University of Mashhadarticleclassificationearly detectionfire blightnear-infraredspectrometryAgriculture (General)S1-972Engineering (General). Civil engineering (General)TA1-2040ENFAJournal of Agricultural Machinery, Vol 10, Iss 1, Pp 37-48 (2020)
institution	DOAJ
collection	DOAJ
language	EN FA
topic	classification early detection fire blight near-infrared spectrometry Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	classification early detection fire blight near-infrared spectrometry Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040 N Bagheri H Mohamadi-Monavar Early Detection of Fire Blight Disease of Pome Fruit Trees Using Visible-NIR Spectrometry and Dimensionality Reduction Methods
description	Fire Blight (FB) is the most destructive bacterial disease of pome fruit trees around the world. In recent years, spectrometry has been shown to be an accurate and real-time sensing technology for plant disease detection. So, the main objective of this research is early detecting FB of pear trees by using Visible-Near-infrared spectrometry. To get this goal, the reflectance spectra of healthy leaves (ND), non-symptomatic (NS), and symptomatic diseased leaves (SY) were captured in the visible–NIR spectral regions. In order to keep the important information of spectra and reduce the dimension of data, three linear and non-linear manifold-based learning techniques were applied such as, Principal Component Analysis (PCA), Sammon mapping and Multilayer auto-encoder (MAE). The output of manifold-based learning techniques was used as an input of the SIMCA (Soft independent modeling by class analogy) classification model to discriminate NS and ND leaves. Based on the results, the best classification accuracy obtained by using PCA on the 1st derivative spectra, with accuracy of 95.8%, 89.3%, and 91.6% for ND, NS, and SY samples, respectively. These results support the capability of manifold-based learning techniques for early detection of FB via spectrometry method.
format	article
author	N Bagheri H Mohamadi-Monavar
author_facet	N Bagheri H Mohamadi-Monavar
author_sort	N Bagheri
title	Early Detection of Fire Blight Disease of Pome Fruit Trees Using Visible-NIR Spectrometry and Dimensionality Reduction Methods
title_short	Early Detection of Fire Blight Disease of Pome Fruit Trees Using Visible-NIR Spectrometry and Dimensionality Reduction Methods
title_full	Early Detection of Fire Blight Disease of Pome Fruit Trees Using Visible-NIR Spectrometry and Dimensionality Reduction Methods
title_fullStr	Early Detection of Fire Blight Disease of Pome Fruit Trees Using Visible-NIR Spectrometry and Dimensionality Reduction Methods
title_full_unstemmed	Early Detection of Fire Blight Disease of Pome Fruit Trees Using Visible-NIR Spectrometry and Dimensionality Reduction Methods
title_sort	early detection of fire blight disease of pome fruit trees using visible-nir spectrometry and dimensionality reduction methods
publisher	Ferdowsi University of Mashhad
publishDate	2020
url	https://doaj.org/article/53f5ede4c3b84fd6af675ba5e4b12466
work_keys_str_mv	AT nbagheri earlydetectionoffireblightdiseaseofpomefruittreesusingvisiblenirspectrometryanddimensionalityreductionmethods AT hmohamadimonavar earlydetectionoffireblightdiseaseofpomefruittreesusingvisiblenirspectrometryanddimensionalityreductionmethods
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Early Detection of Fire Blight Disease of Pome Fruit Trees Using Visible-NIR Spectrometry and Dimensionality Reduction Methods

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