An Explorative Analysis on the Machine-Vision-Based Disease Recognition of Three Available Fruits of Bangladesh
Bangladesh, being a densely populated country, hinges on agriculture for the security of finance and food to a large extent. Hence, both the fruits’ quantity and quality turn out to be very important, which can be degraded due to the attacks of various diseases. Automated fruit disease recognition c...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2022-05-01
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| Series: | Vietnam Journal of Computer Science |
| Subjects: | |
| Online Access: | https://www.worldscientific.com/doi/10.1142/S2196888822500087 |
| _version_ | 1811250222819442688 |
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| author | Md. Tarek Habib Md. Jueal Mia Mohammad Shorif Uddin Farruk Ahmed |
| author_facet | Md. Tarek Habib Md. Jueal Mia Mohammad Shorif Uddin Farruk Ahmed |
| author_sort | Md. Tarek Habib |
| collection | DOAJ |
| description | Bangladesh, being a densely populated country, hinges on agriculture for the security of finance and food to a large extent. Hence, both the fruits’ quantity and quality turn out to be very important, which can be degraded due to the attacks of various diseases. Automated fruit disease recognition can help fruit farmers, especially remote farmers, for whom adequate cultivation support is required. Two daunting problems, namely disease detection, and disease classification are raised by automated fruit disease recognition. In this research, we conduct an intense investigation of the applicability of automated recognition of the diseases of three available Bangladeshi local fruits, viz. guava, jackfruit, and papaya. After exerting four notable segmentation algorithms, [Formula: see text]-means clustering segmentation algorithm is selected to segregate the disease-contaminated parts from a fruit image. Then some discriminatory features are extracted from these disease-contaminated parts. Nine noteworthy classification algorithms are applied for disease classification to thoroughly get the measure of their merits. It is observed that random forest outperforms the eight other classifiers by disclosing an accuracy of 96.8% and 89.59% for guava and jackfruit, respectively, whereas support vector machine attains an accuracy of 94.9% for papaya, which can be claimed good as well as attractive for forthcoming research. |
| first_indexed | 2024-04-12T16:00:15Z |
| format | Article |
| id | doaj.art-2cb372ddd274461fadb4a647529387dd |
| institution | Directory Open Access Journal |
| issn | 2196-8888 2196-8896 |
| language | English |
| last_indexed | 2024-04-12T16:00:15Z |
| publishDate | 2022-05-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Vietnam Journal of Computer Science |
| spelling | doaj.art-2cb372ddd274461fadb4a647529387dd2022-12-22T03:26:13ZengWorld Scientific PublishingVietnam Journal of Computer Science2196-88882196-88962022-05-01090211513410.1142/S2196888822500087An Explorative Analysis on the Machine-Vision-Based Disease Recognition of Three Available Fruits of BangladeshMd. Tarek Habib0Md. Jueal Mia1Mohammad Shorif Uddin2Farruk Ahmed3Department of Computer, Science and Engineering, Jahangirnagar 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, Independent University, Dhaka, BangladeshBangladesh, being a densely populated country, hinges on agriculture for the security of finance and food to a large extent. Hence, both the fruits’ quantity and quality turn out to be very important, which can be degraded due to the attacks of various diseases. Automated fruit disease recognition can help fruit farmers, especially remote farmers, for whom adequate cultivation support is required. Two daunting problems, namely disease detection, and disease classification are raised by automated fruit disease recognition. In this research, we conduct an intense investigation of the applicability of automated recognition of the diseases of three available Bangladeshi local fruits, viz. guava, jackfruit, and papaya. After exerting four notable segmentation algorithms, [Formula: see text]-means clustering segmentation algorithm is selected to segregate the disease-contaminated parts from a fruit image. Then some discriminatory features are extracted from these disease-contaminated parts. Nine noteworthy classification algorithms are applied for disease classification to thoroughly get the measure of their merits. It is observed that random forest outperforms the eight other classifiers by disclosing an accuracy of 96.8% and 89.59% for guava and jackfruit, respectively, whereas support vector machine attains an accuracy of 94.9% for papaya, which can be claimed good as well as attractive for forthcoming research.https://www.worldscientific.com/doi/10.1142/S2196888822500087Fruit diseaseimage segmentationsubjective evaluationfeature extractionclassification modelperformance metric |
| spellingShingle | Md. Tarek Habib Md. Jueal Mia Mohammad Shorif Uddin Farruk Ahmed An Explorative Analysis on the Machine-Vision-Based Disease Recognition of Three Available Fruits of Bangladesh Vietnam Journal of Computer Science Fruit disease image segmentation subjective evaluation feature extraction classification model performance metric |
| title | An Explorative Analysis on the Machine-Vision-Based Disease Recognition of Three Available Fruits of Bangladesh |
| title_full | An Explorative Analysis on the Machine-Vision-Based Disease Recognition of Three Available Fruits of Bangladesh |
| title_fullStr | An Explorative Analysis on the Machine-Vision-Based Disease Recognition of Three Available Fruits of Bangladesh |
| title_full_unstemmed | An Explorative Analysis on the Machine-Vision-Based Disease Recognition of Three Available Fruits of Bangladesh |
| title_short | An Explorative Analysis on the Machine-Vision-Based Disease Recognition of Three Available Fruits of Bangladesh |
| title_sort | explorative analysis on the machine vision based disease recognition of three available fruits of bangladesh |
| topic | Fruit disease image segmentation subjective evaluation feature extraction classification model performance metric |
| url | https://www.worldscientific.com/doi/10.1142/S2196888822500087 |
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