Enhanced Potato Pest Identification: A Deep Learning Approach for Identifying Potato Pests
Potato crops and their salability are influenced by potato pests in that both crop yield and quality are reduced. This in turn reduces the income for potato farmers due to lower prices for the crop, lower crop yield, trade restriction and reduced market access. Agricultural viability over the long r...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10740274/ |
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| author | Amir Sohel Md. Shahriar Shakil Shah Md. Tanvir Siddiquee Ahmed Al Marouf Jon G. Rokne Reda Alhajj |
| author_facet | Amir Sohel Md. Shahriar Shakil Shah Md. Tanvir Siddiquee Ahmed Al Marouf Jon G. Rokne Reda Alhajj |
| author_sort | Amir Sohel |
| collection | DOAJ |
| description | Potato crops and their salability are influenced by potato pests in that both crop yield and quality are reduced. This in turn reduces the income for potato farmers due to lower prices for the crop, lower crop yield, trade restriction and reduced market access. Agricultural viability over the long run therefore depends on sustainable pest management. In order to efficiently detect potato pests, a dataset was constructed which contains eight prevalent potato species that were taken from several sources. Image pre-processing techniques were employed enhance image quality for compatibility with deep learning models. Among InceptionV3, VGG-16, and MobileNetV2 models, VGG-16 attained the highest accuracy of 94.44%, outperforming others. Inception-V3 achieved 58% accuracy, while MobileNetV2 reached 75%. Pre-processing has a major influence on improving result accuracy, which emphasizes its significance in enhancing model performance, according to an evaluation of its effects. These findings might lead to the development of pest management strategies for potato farming that are more effective. The efficient use of VGG-16 in potato pest identification systems is demonstrated by its excellent performance. Using deep learning models can therefore reduce financial losses and promote sustainable potato production. This study provides an approach for further investigation into the best ways to control pests in potato production, allowing farmers to overcome the obstacles and take advantage of valuable market prospects even in the face of pest threats. |
| format | Article |
| id | doaj-art-bc6ca6b7fd6d441fb8ae6782e6c865f9 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-bc6ca6b7fd6d441fb8ae6782e6c865f92025-08-20T02:01:54ZengIEEEIEEE Access2169-35362024-01-011217214917216110.1109/ACCESS.2024.348873010740274Enhanced Potato Pest Identification: A Deep Learning Approach for Identifying Potato PestsAmir Sohel0https://orcid.org/0009-0006-3493-0929Md. Shahriar Shakil1https://orcid.org/0000-0002-8483-2204Shah Md. Tanvir Siddiquee2https://orcid.org/0009-0005-0598-9983Ahmed Al Marouf3https://orcid.org/0000-0001-6520-0749Jon G. Rokne4https://orcid.org/0000-0002-3439-2917Reda Alhajj5https://orcid.org/0000-0001-6657-9738Computer Science and Engineering Department, Multidisciplinary Action Research Laboratory, Daffodil International University, Dhaka, BangladeshComputer Science and Engineering Department, Multidisciplinary Action Research Laboratory, Daffodil International University, Dhaka, BangladeshComputer Science and Engineering Department, Multidisciplinary Action Research Laboratory, Daffodil International University, Dhaka, BangladeshDepartment of Computer Science, University of Calgary, Calgary, AB, CanadaDepartment of Computer Science, University of Calgary, Calgary, AB, CanadaDepartment of Computer Science, University of Calgary, Calgary, AB, CanadaPotato crops and their salability are influenced by potato pests in that both crop yield and quality are reduced. This in turn reduces the income for potato farmers due to lower prices for the crop, lower crop yield, trade restriction and reduced market access. Agricultural viability over the long run therefore depends on sustainable pest management. In order to efficiently detect potato pests, a dataset was constructed which contains eight prevalent potato species that were taken from several sources. Image pre-processing techniques were employed enhance image quality for compatibility with deep learning models. Among InceptionV3, VGG-16, and MobileNetV2 models, VGG-16 attained the highest accuracy of 94.44%, outperforming others. Inception-V3 achieved 58% accuracy, while MobileNetV2 reached 75%. Pre-processing has a major influence on improving result accuracy, which emphasizes its significance in enhancing model performance, according to an evaluation of its effects. These findings might lead to the development of pest management strategies for potato farming that are more effective. The efficient use of VGG-16 in potato pest identification systems is demonstrated by its excellent performance. Using deep learning models can therefore reduce financial losses and promote sustainable potato production. This study provides an approach for further investigation into the best ways to control pests in potato production, allowing farmers to overcome the obstacles and take advantage of valuable market prospects even in the face of pest threats.https://ieeexplore.ieee.org/document/10740274/Potato pestclassificationdeep learningVGG-16 |
| spellingShingle | Amir Sohel Md. Shahriar Shakil Shah Md. Tanvir Siddiquee Ahmed Al Marouf Jon G. Rokne Reda Alhajj Enhanced Potato Pest Identification: A Deep Learning Approach for Identifying Potato Pests IEEE Access Potato pest classification deep learning VGG-16 |
| title | Enhanced Potato Pest Identification: A Deep Learning Approach for Identifying Potato Pests |
| title_full | Enhanced Potato Pest Identification: A Deep Learning Approach for Identifying Potato Pests |
| title_fullStr | Enhanced Potato Pest Identification: A Deep Learning Approach for Identifying Potato Pests |
| title_full_unstemmed | Enhanced Potato Pest Identification: A Deep Learning Approach for Identifying Potato Pests |
| title_short | Enhanced Potato Pest Identification: A Deep Learning Approach for Identifying Potato Pests |
| title_sort | enhanced potato pest identification a deep learning approach for identifying potato pests |
| topic | Potato pest classification deep learning VGG-16 |
| url | https://ieeexplore.ieee.org/document/10740274/ |
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