Disease Detection in Tropical Tomato Leaves via Machine Learning Models
This study addresses the significant threat of tomato diseases to production in Ghana, which has led to substantial yield and quality losses, adversely affecting the livelihoods of local farmers and the availability of this essential dietary staple. Traditional disease identification methods are tim...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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P3M Politeknik Negeri Banjarmasin
2024-12-01
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| Series: | Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer |
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| Online Access: | https://eltikom.poliban.ac.id/index.php/eltikom/article/view/1340 |
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| _version_ | 1850059752828043264 |
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| author | Benjamin Kommey Elvis Tamakloe Daniel Opoku Tibilla Crispin Jeffrey Danquah |
| author_facet | Benjamin Kommey Elvis Tamakloe Daniel Opoku Tibilla Crispin Jeffrey Danquah |
| author_sort | Benjamin Kommey |
| collection | DOAJ |
| description | This study addresses the significant threat of tomato diseases to production in Ghana, which has led to substantial yield and quality losses, adversely affecting the livelihoods of local farmers and the availability of this essential dietary staple. Traditional disease identification methods are time-consuming and rely on subjective visual inspections, hindering early detection and control. This study develops a machine learning model capable of accurately identifying tomato plant diseases through image processing. The methodology involves processing a dataset of tomato plant images displaying healthy and diseased symptoms. The proposed model employs the YOLOv5 architecture and is deployed on a mobile platform for accessible disease identification. The model achieved a validation mAP@.5 of 0.715, demonstrating strong performance during live, on-site testing. This system provides a swift, accurate, and automated solution for detecting tomato diseases, supporting the sustainability of tomato production in Ghana. |
| format | Article |
| id | doaj-art-028f9b816f684998a3972c9dfd61ae21 |
| institution | DOAJ |
| issn | 2598-3245 2598-3288 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | P3M Politeknik Negeri Banjarmasin |
| record_format | Article |
| series | Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer |
| spelling | doaj-art-028f9b816f684998a3972c9dfd61ae212025-08-20T02:50:48ZengP3M Politeknik Negeri BanjarmasinJurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer2598-32452598-32882024-12-018217919110.31961/eltikom.v8i2.13401296Disease Detection in Tropical Tomato Leaves via Machine Learning ModelsBenjamin Kommey0Elvis Tamakloe1Daniel Opoku2Tibilla Crispin3Jeffrey Danquah4Kwame Nkrumah University of Science and Technology, GhanaKwame Nkrumah University of Science and Technology, GhanaKwame Nkrumah University of Science and Technology, GhanaKwame Nkrumah University of Science and Technology, GhanaKwame Nkrumah University of Science and Technology, GhanaThis study addresses the significant threat of tomato diseases to production in Ghana, which has led to substantial yield and quality losses, adversely affecting the livelihoods of local farmers and the availability of this essential dietary staple. Traditional disease identification methods are time-consuming and rely on subjective visual inspections, hindering early detection and control. This study develops a machine learning model capable of accurately identifying tomato plant diseases through image processing. The methodology involves processing a dataset of tomato plant images displaying healthy and diseased symptoms. The proposed model employs the YOLOv5 architecture and is deployed on a mobile platform for accessible disease identification. The model achieved a validation mAP@.5 of 0.715, demonstrating strong performance during live, on-site testing. This system provides a swift, accurate, and automated solution for detecting tomato diseases, supporting the sustainability of tomato production in Ghana.https://eltikom.poliban.ac.id/index.php/eltikom/article/view/1340cnndisease detectionimage processingleafmachine learningtomato |
| spellingShingle | Benjamin Kommey Elvis Tamakloe Daniel Opoku Tibilla Crispin Jeffrey Danquah Disease Detection in Tropical Tomato Leaves via Machine Learning Models Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer cnn disease detection image processing leaf machine learning tomato |
| title | Disease Detection in Tropical Tomato Leaves via Machine Learning Models |
| title_full | Disease Detection in Tropical Tomato Leaves via Machine Learning Models |
| title_fullStr | Disease Detection in Tropical Tomato Leaves via Machine Learning Models |
| title_full_unstemmed | Disease Detection in Tropical Tomato Leaves via Machine Learning Models |
| title_short | Disease Detection in Tropical Tomato Leaves via Machine Learning Models |
| title_sort | disease detection in tropical tomato leaves via machine learning models |
| topic | cnn disease detection image processing leaf machine learning tomato |
| url | https://eltikom.poliban.ac.id/index.php/eltikom/article/view/1340 |
| work_keys_str_mv | AT benjaminkommey diseasedetectionintropicaltomatoleavesviamachinelearningmodels AT elvistamakloe diseasedetectionintropicaltomatoleavesviamachinelearningmodels AT danielopoku diseasedetectionintropicaltomatoleavesviamachinelearningmodels AT tibillacrispin diseasedetectionintropicaltomatoleavesviamachinelearningmodels AT jeffreydanquah diseasedetectionintropicaltomatoleavesviamachinelearningmodels |