Feasibility of Online Diagnosis of Botrytis elliptica Disease in the Lilium Plant using the Machine Vision System and K-means

IntroductionThe automatic detection of plant diseases in early stages in large farms, in addition to increasing the quality of the final product, could prevent the occurrence of irreparable damage. To this end, accurate and timely diagnosis of farm conditions is of great importance. In order to faci...

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Autores principales:	H Biabi, S Abdanan Mehdizadeh, M Salehi Salmi
Formato:	article
Lenguaje:	EN FA
Publicado:	Ferdowsi University of Mashhad 2021
Materias:	diagnosis of plant disease image processing linguistic hedges algorithm k-means Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/93730191504b41c3b04029f3ddc2d162
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id	oai:doaj.org-article:93730191504b41c3b04029f3ddc2d162
record_format	dspace
institution	DOAJ
collection	DOAJ
language	EN FA
topic	diagnosis of plant disease image processing linguistic hedges algorithm k-means Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	diagnosis of plant disease image processing linguistic hedges algorithm k-means Agriculture (General) S1-972 Engineering (General). Civil engineering (General) TA1-2040 H Biabi S Abdanan Mehdizadeh M Salehi Salmi Feasibility of Online Diagnosis of Botrytis elliptica Disease in the Lilium Plant using the Machine Vision System and K-means
description	IntroductionThe automatic detection of plant diseases in early stages in large farms, in addition to increasing the quality of the final product, could prevent the occurrence of irreparable damage. To this end, accurate and timely diagnosis of farm conditions is of great importance. In order to facilitate production potential and prevent a significant decline in yield, disease diagnosis is necessary periodically throughout the whole life of the. On the other hand, early detection of the disease in its early stages of growth can also prevent the spread of diseases. One of the most common methods for diagnosing plant diseases is the use of visual methods, but this method is difficult to evaluate the performance of a number of parameters such as the effects of the environment, nutrients, and organisms and so on. Furthermore, the accuracy of repetitions is very much related to individual fatigue of inspector. Research on activities that have the ability to identify diseases at an early stage and prevent the spread of contagious diseases are of great importance. Therefore, the use of new applications and new detection technologies to protect can significantly reduce the risk of product loss. Therefore, the purpose of this research is to design and construct an intelligent control system that automatically detects the health of the lilium plant and to improve the plant's condition.Materials and MethodsSample collectionIn this study, 80 pots of four kilograms (including healthy and disease plants) were considered for plant growth in vegetative stage. The spring onions were grown in pots with 20 cm diameter and 30 cm height. Experiments were carried out in a greenhouse with a temperature of 27.15°C day/night and a relative humidity of 70-75%.Image processing In this research, the camera was placed at a constant distance of 50 cm from the flower to evaluate the stem and the leaves attached to it. The images were captured under the constant light conditions in the greenhouse during a specific hour of the day (10 to 12) every other day. The image was taken in RGB color space with a resolution of 1024 × 840 pixels, and after image transfer to the computer, image processing was performed using Matlab 2016a. After examining the plant image, 9 color channels (R, G, B, L, a, b, H, S, and V) were examined from three color spaces (RGB, Lab and HSV) and stem length to diagnosis of Botrytis elliptica disease.Feature selection and classification In this research, after improving the image and extracting the feature, the linguistic hedges method was used to select the features and the K-means clustering was applied in the N-division of the k-clustering specified by the user. In this method, each attribute was assigned to a cluster closer to the mean vector. This method continues until there was no significant change in the mean vectors between successive repetitions of the algorithm.Results and DiscussionAccording to the results of feature selection L leaf, L stem, a leaf, b leaf, H leaf, b stem, H stem, V leaf and stem length, were the best features. Moreover, the accuracy of diagnosis for the diseased and healthy plants were 96.42 and 100 percent, respectively, and the overall classification accuracy was 97.63 percent. Therefore, in general, it can be said that the proposed image processing method is desirable and acceptable in order to diagnose the disease. According to this, zhuang et al. (2017) used sparse representation (SR) classification and K-means clustering to identify leaf-based cucumber disease. In the proposed method, it has been shown that system could detect cucumber diseases with accuracy rate of 85.7%. Therefore, the proposed image processing technique seems to be able to diagnose the disease quickly and easily.ConclusionsToday, in the modern agricultural systems, numerous computational methods have been designed to help farmers to control the proper growth of their products. However, there are still major problems with the rapid, accurate and classification of diseases in the early days of the disease. Therefore, the purpose of this study was to design, construct and evaluate a smart system based on image processing in order to identify and classify the leaf disease of the leaves of the lilium plant and remove it by spraying the contaminated parts. For this purpose, the linguistic hedges method was used to select the characteristics and k-means method to classify the infected plant from healthy. The results of the classification for the diseased and healthy plants were 96.42 and 100 percent, respectively, and the overall classification accuracy was 97.63 percent, which indicates the acceptable accuracy of the machine vision system in detecting the disease.
format	article
author	H Biabi S Abdanan Mehdizadeh M Salehi Salmi
author_facet	H Biabi S Abdanan Mehdizadeh M Salehi Salmi
author_sort	H Biabi
title	Feasibility of Online Diagnosis of Botrytis elliptica Disease in the Lilium Plant using the Machine Vision System and K-means
title_short	Feasibility of Online Diagnosis of Botrytis elliptica Disease in the Lilium Plant using the Machine Vision System and K-means
title_full	Feasibility of Online Diagnosis of Botrytis elliptica Disease in the Lilium Plant using the Machine Vision System and K-means
title_fullStr	Feasibility of Online Diagnosis of Botrytis elliptica Disease in the Lilium Plant using the Machine Vision System and K-means
title_full_unstemmed	Feasibility of Online Diagnosis of Botrytis elliptica Disease in the Lilium Plant using the Machine Vision System and K-means
title_sort	feasibility of online diagnosis of botrytis elliptica disease in the lilium plant using the machine vision system and k-means
publisher	Ferdowsi University of Mashhad
publishDate	2021
url	https://doaj.org/article/93730191504b41c3b04029f3ddc2d162
work_keys_str_mv	AT hbiabi feasibilityofonlinediagnosisofbotrytisellipticadiseaseintheliliumplantusingthemachinevisionsystemandkmeans AT sabdananmehdizadeh feasibilityofonlinediagnosisofbotrytisellipticadiseaseintheliliumplantusingthemachinevisionsystemandkmeans AT msalehisalmi feasibilityofonlinediagnosisofbotrytisellipticadiseaseintheliliumplantusingthemachinevisionsystemandkmeans
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spelling	oai:doaj.org-article:93730191504b41c3b04029f3ddc2d1622021-11-14T06:40:21ZFeasibility of Online Diagnosis of Botrytis elliptica Disease in the Lilium Plant using the Machine Vision System and K-means2228-68292423-394310.22067/jam.v11i2.77237https://doaj.org/article/93730191504b41c3b04029f3ddc2d1622021-09-01T00:00:00Zhttps://jame.um.ac.ir/article_34750_374f8c05ab82723838285d3e5154258e.pdfhttps://doaj.org/toc/2228-6829https://doaj.org/toc/2423-3943IntroductionThe automatic detection of plant diseases in early stages in large farms, in addition to increasing the quality of the final product, could prevent the occurrence of irreparable damage. To this end, accurate and timely diagnosis of farm conditions is of great importance. In order to facilitate production potential and prevent a significant decline in yield, disease diagnosis is necessary periodically throughout the whole life of the. On the other hand, early detection of the disease in its early stages of growth can also prevent the spread of diseases. One of the most common methods for diagnosing plant diseases is the use of visual methods, but this method is difficult to evaluate the performance of a number of parameters such as the effects of the environment, nutrients, and organisms and so on. Furthermore, the accuracy of repetitions is very much related to individual fatigue of inspector. Research on activities that have the ability to identify diseases at an early stage and prevent the spread of contagious diseases are of great importance. Therefore, the use of new applications and new detection technologies to protect can significantly reduce the risk of product loss. Therefore, the purpose of this research is to design and construct an intelligent control system that automatically detects the health of the lilium plant and to improve the plant's condition.Materials and MethodsSample collectionIn this study, 80 pots of four kilograms (including healthy and disease plants) were considered for plant growth in vegetative stage. The spring onions were grown in pots with 20 cm diameter and 30 cm height. Experiments were carried out in a greenhouse with a temperature of 27.15°C day/night and a relative humidity of 70-75%.Image processing In this research, the camera was placed at a constant distance of 50 cm from the flower to evaluate the stem and the leaves attached to it. The images were captured under the constant light conditions in the greenhouse during a specific hour of the day (10 to 12) every other day. The image was taken in RGB color space with a resolution of 1024 × 840 pixels, and after image transfer to the computer, image processing was performed using Matlab 2016a. After examining the plant image, 9 color channels (R, G, B, L, a, b, H, S, and V) were examined from three color spaces (RGB, Lab and HSV) and stem length to diagnosis of Botrytis elliptica disease.Feature selection and classification In this research, after improving the image and extracting the feature, the linguistic hedges method was used to select the features and the K-means clustering was applied in the N-division of the k-clustering specified by the user. In this method, each attribute was assigned to a cluster closer to the mean vector. This method continues until there was no significant change in the mean vectors between successive repetitions of the algorithm.Results and DiscussionAccording to the results of feature selection L leaf, L stem, a leaf, b leaf, H leaf, b stem, H stem, V leaf and stem length, were the best features. Moreover, the accuracy of diagnosis for the diseased and healthy plants were 96.42 and 100 percent, respectively, and the overall classification accuracy was 97.63 percent. Therefore, in general, it can be said that the proposed image processing method is desirable and acceptable in order to diagnose the disease. According to this, zhuang et al. (2017) used sparse representation (SR) classification and K-means clustering to identify leaf-based cucumber disease. In the proposed method, it has been shown that system could detect cucumber diseases with accuracy rate of 85.7%. Therefore, the proposed image processing technique seems to be able to diagnose the disease quickly and easily.ConclusionsToday, in the modern agricultural systems, numerous computational methods have been designed to help farmers to control the proper growth of their products. However, there are still major problems with the rapid, accurate and classification of diseases in the early days of the disease. Therefore, the purpose of this study was to design, construct and evaluate a smart system based on image processing in order to identify and classify the leaf disease of the leaves of the lilium plant and remove it by spraying the contaminated parts. For this purpose, the linguistic hedges method was used to select the characteristics and k-means method to classify the infected plant from healthy. The results of the classification for the diseased and healthy plants were 96.42 and 100 percent, respectively, and the overall classification accuracy was 97.63 percent, which indicates the acceptable accuracy of the machine vision system in detecting the disease.H BiabiS Abdanan MehdizadehM Salehi SalmiFerdowsi University of Mashhadarticlediagnosis of plant diseaseimage processinglinguistic hedges algorithmk-meansAgriculture (General)S1-972Engineering (General). Civil engineering (General)TA1-2040ENFAJournal of Agricultural Machinery, Vol 11, Iss 2, Pp 277-291 (2021)

Feasibility of Online Diagnosis of Botrytis elliptica Disease in the Lilium Plant using the Machine Vision System and K-means

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