Automatic Classification of Bagworm, Metisa plana (Walker) Instar Stages Using a Transfer Learning-Based Framework

Siti Nurul Afiah Mohd Johari, Siti Khairunniza-Bejo (), Abdul Rashid Mohamed Shariff, Nur Azuan Husin, Mohamed Mazmira Mohd Masri and Noorhazwani Kamarudin
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Siti Nurul Afiah Mohd Johari: Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
Siti Khairunniza-Bejo: Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
Abdul Rashid Mohamed Shariff: Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
Nur Azuan Husin: Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
Mohamed Mazmira Mohd Masri: Malaysian Palm Oil Board (MPOB), No. 6, Persiaran Institusi, Bandar Baru Bangi, Kajang 43000, Selangor, Malaysia
Noorhazwani Kamarudin: Malaysian Palm Oil Board (MPOB), No. 6, Persiaran Institusi, Bandar Baru Bangi, Kajang 43000, Selangor, Malaysia

Agriculture, 2023, vol. 13, issue 2, 1-16

Abstract: 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.

Keywords: bagworm; hyperspectral image; deep learning; transfer learning; instar stage (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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