Automatic classification of bagworm, Metisa plana (Walker) instar stages using a transfer learning-based framework
Bagworms, particularly Metisa plana Walker (Lepidoptera: Psychidae), are one of the most destructive leaf-eating pests, especially in oil palm plantations, causing severe defoliation which reduces yield. Due to the delayed control of the bagworm population, it was discovered to be the most widesprea...
| Main Authors: | , , , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/106829/ |
| _version_ | 1848864832837648384 |
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| author | Mohd Johari, Siti Nurul Afiah Bejo, Siti Khairunniza Mohamed Shariff, Abdul Rashid Husin, Nur Azuan Mohd Masri, Mohamed Mazmira Kamarudin, Noorhazwani |
| author_facet | Mohd Johari, Siti Nurul Afiah Bejo, Siti Khairunniza Mohamed Shariff, Abdul Rashid Husin, Nur Azuan Mohd Masri, Mohamed Mazmira Kamarudin, Noorhazwani |
| author_sort | Mohd Johari, Siti Nurul Afiah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Bagworms, particularly Metisa plana Walker (Lepidoptera: Psychidae), are one of the most destructive leaf-eating pests, especially in oil palm plantations, causing severe defoliation which reduces yield. Due to the delayed control of the bagworm population, it was discovered to be the most widespread oil palm pest in Peninsular Malaysia. Identification and classification of bagworm instar stages are critical for determining the current outbreak and taking appropriate control measures in the infested area. Therefore, this work proposes an automatic classification of bagworm larval instar stage starting from the second (S2) to the fifth (S5) instar stage using a transfer learning-based framework. Five different deep CNN architectures were used i.e., VGG16, ResNet50, ResNet152, DenseNet121 and DenseNet201 to categorize the larval instar stages. All the models were fine-tuned using two different optimizers, i.e., stochastic gradient descent (SGD) with momentum and adaptive moment estimation (Adam). Among the five models used, the DenseNet121 model, which used SGD with momentum (0.9) had the best classification accuracy of 96.18 with a testing time of 0.048 s per sample. Besides, all the instar stages from S2 to S5 can be identified with high value accuracy (94.52“97.57), precision (89.71“95.87), sensitivity (87.67“96.65), specificity (96.51“98.61) and the F1-score (88.89“96.18). The presented transfer learning approach yields promising results, demonstrating its ability to classify bagworm instar stages. |
| first_indexed | 2025-11-15T13:55:05Z |
| format | Article |
| id | upm-106829 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:55:05Z |
| publishDate | 2023 |
| publisher | Multidisciplinary Digital Publishing Institute |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1068292024-08-12T04:25:46Z http://psasir.upm.edu.my/id/eprint/106829/ Automatic classification of bagworm, Metisa plana (Walker) instar stages using a transfer learning-based framework Mohd Johari, Siti Nurul Afiah Bejo, Siti Khairunniza Mohamed Shariff, Abdul Rashid Husin, Nur Azuan Mohd Masri, Mohamed Mazmira Kamarudin, Noorhazwani Bagworms, particularly Metisa plana Walker (Lepidoptera: Psychidae), are one of the most destructive leaf-eating pests, especially in oil palm plantations, causing severe defoliation which reduces yield. Due to the delayed control of the bagworm population, it was discovered to be the most widespread oil palm pest in Peninsular Malaysia. Identification and classification of bagworm instar stages are critical for determining the current outbreak and taking appropriate control measures in the infested area. Therefore, this work proposes an automatic classification of bagworm larval instar stage starting from the second (S2) to the fifth (S5) instar stage using a transfer learning-based framework. Five different deep CNN architectures were used i.e., VGG16, ResNet50, ResNet152, DenseNet121 and DenseNet201 to categorize the larval instar stages. All the models were fine-tuned using two different optimizers, i.e., stochastic gradient descent (SGD) with momentum and adaptive moment estimation (Adam). Among the five models used, the DenseNet121 model, which used SGD with momentum (0.9) had the best classification accuracy of 96.18 with a testing time of 0.048 s per sample. Besides, all the instar stages from S2 to S5 can be identified with high value accuracy (94.52“97.57), precision (89.71“95.87), sensitivity (87.67“96.65), specificity (96.51“98.61) and the F1-score (88.89“96.18). The presented transfer learning approach yields promising results, demonstrating its ability to classify bagworm instar stages. Multidisciplinary Digital Publishing Institute 2023 Article PeerReviewed Mohd Johari, Siti Nurul Afiah and Bejo, Siti Khairunniza and Mohamed Shariff, Abdul Rashid and Husin, Nur Azuan and Mohd Masri, Mohamed Mazmira and Kamarudin, Noorhazwani (2023) Automatic classification of bagworm, Metisa plana (Walker) instar stages using a transfer learning-based framework. Agriculture, 13 (1). pp. 1-16. ISSN 2077-0472 https://www.mdpi.com/2077-0472/13/2/442 10.3390/agriculture13020442 |
| spellingShingle | Mohd Johari, Siti Nurul Afiah Bejo, Siti Khairunniza Mohamed Shariff, Abdul Rashid Husin, Nur Azuan Mohd Masri, Mohamed Mazmira Kamarudin, Noorhazwani Automatic classification of bagworm, Metisa plana (Walker) instar stages using a transfer learning-based framework |
| title | Automatic classification of bagworm, Metisa plana (Walker) instar stages using a transfer learning-based framework |
| title_full | Automatic classification of bagworm, Metisa plana (Walker) instar stages using a transfer learning-based framework |
| title_fullStr | Automatic classification of bagworm, Metisa plana (Walker) instar stages using a transfer learning-based framework |
| title_full_unstemmed | Automatic classification of bagworm, Metisa plana (Walker) instar stages using a transfer learning-based framework |
| title_short | Automatic classification of bagworm, Metisa plana (Walker) instar stages using a transfer learning-based framework |
| title_sort | automatic classification of bagworm, metisa plana (walker) instar stages using a transfer learning-based framework |
| url | http://psasir.upm.edu.my/id/eprint/106829/ http://psasir.upm.edu.my/id/eprint/106829/ http://psasir.upm.edu.my/id/eprint/106829/ |