Machine vision based papaya disease recognition
Over the years little research has been performed for vision-based papaya disease recognition system in order to help distant farmers, most of whom require proper support for cultivation. Due to advancement of vision-based technology we find a good solution to this problem. Papaya disease recognitio...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-03-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157818302404 |
| _version_ | 1819051786336468992 |
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| author | Md. Tarek Habib Anup Majumder A.Z.M. Jakaria Morium Akter Mohammad Shorif Uddin Farruk Ahmed |
| author_facet | Md. Tarek Habib Anup Majumder A.Z.M. Jakaria Morium Akter Mohammad Shorif Uddin Farruk Ahmed |
| author_sort | Md. Tarek Habib |
| collection | DOAJ |
| description | Over the years little research has been performed for vision-based papaya disease recognition system in order to help distant farmers, most of whom require proper support for cultivation. Due to advancement of vision-based technology we find a good solution to this problem. Papaya disease recognition mainly involves two challenging problems: one is disease detection and another is disease classification. Considering this scenario, here we present an online machine vision-based agro-medical expert system that processes an image captured through mobile or handheld device and determines the diseases in order to help distant farmers to address the problem. Some experiments are performed to show the utility of the proposed expert system. First, we propose a set of features from the view point of distinguishing attributes. K-means clustering algorithm is used in order to segment out the disease-attacked region from the captured image and then required features are extracted to classify the diseases with the help of support vector machine. More than 90% classification accuracy has been achieved, which appears to be good as well as promising by comparing performances obtained with recently reported relevant works. Keywords: Papaya disease, Agro-medical expert system, Machine vision, k-means clustering, Support vector machine |
| first_indexed | 2024-12-21T12:09:28Z |
| format | Article |
| id | doaj.art-c51092e678a548608518151136fec9e3 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-12-21T12:09:28Z |
| publishDate | 2020-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-c51092e678a548608518151136fec9e32022-12-21T19:04:37ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782020-03-01323300309Machine vision based papaya disease recognitionMd. Tarek Habib0Anup Majumder1A.Z.M. Jakaria2Morium Akter3Mohammad Shorif Uddin4Farruk Ahmed5Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh; Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh; Corresponding author at: Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh.Department of Computer Science and Engineering, Daffodil International University, Dhaka, BangladeshDepartment of Computer Science and Engineering, Daffodil International University, Dhaka, BangladeshDepartment of Computer Science and Engineering, Jahangirnagar University, Dhaka, BangladeshDepartment of Computer Science and Engineering, Jahangirnagar University, Dhaka, BangladeshDepartment of Computer Science and Engineering, Independent University, Bangladesh, Dhaka, BangladeshOver the years little research has been performed for vision-based papaya disease recognition system in order to help distant farmers, most of whom require proper support for cultivation. Due to advancement of vision-based technology we find a good solution to this problem. Papaya disease recognition mainly involves two challenging problems: one is disease detection and another is disease classification. Considering this scenario, here we present an online machine vision-based agro-medical expert system that processes an image captured through mobile or handheld device and determines the diseases in order to help distant farmers to address the problem. Some experiments are performed to show the utility of the proposed expert system. First, we propose a set of features from the view point of distinguishing attributes. K-means clustering algorithm is used in order to segment out the disease-attacked region from the captured image and then required features are extracted to classify the diseases with the help of support vector machine. More than 90% classification accuracy has been achieved, which appears to be good as well as promising by comparing performances obtained with recently reported relevant works. Keywords: Papaya disease, Agro-medical expert system, Machine vision, k-means clustering, Support vector machinehttp://www.sciencedirect.com/science/article/pii/S1319157818302404 |
| spellingShingle | Md. Tarek Habib Anup Majumder A.Z.M. Jakaria Morium Akter Mohammad Shorif Uddin Farruk Ahmed Machine vision based papaya disease recognition Journal of King Saud University: Computer and Information Sciences |
| title | Machine vision based papaya disease recognition |
| title_full | Machine vision based papaya disease recognition |
| title_fullStr | Machine vision based papaya disease recognition |
| title_full_unstemmed | Machine vision based papaya disease recognition |
| title_short | Machine vision based papaya disease recognition |
| title_sort | machine vision based papaya disease recognition |
| url | http://www.sciencedirect.com/science/article/pii/S1319157818302404 |
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