Muhathir

Work place: Universitas Medan Area, Fakultas Teknik, Program Studi Teknik Informatika, Medan, Indonesia

E-mail: muhathir@staff.uma.ac.id

Website: https://orcid.org/0000-0002-8432-3100

Research Interests:

Biography

Muhathir is a dedicated computer science researcher at Universitas Medan Area. Developing and comprehending a wide range of cutting-edge technologies has been the main focus of my career, with a focus on artificial intelligence (AI), machine learning, deep learning, image processing, computer vision, and optimization. In addition, my computer science knowledge has been extensively used in a range of settings, such as engineering, medical, and agriculture.

Author Articles
Optimizing VGG16 for Accurate Pest Identification in Oil Palm: A Comparative Study of Fine-Tuning Techniques

By Muhathir Andre Hasudungan Lubis Dwika Karima Wardani Mahardika Gama Pradana Ilham Sahputra Mutammimul Ula

DOI: https://doi.org/10.5815/ijieeb.2024.05.03, Pub. Date: 8 Oct. 2024

Recent advancements in pest classification using deep learning models have shown promising results in various agricultural contexts. The VGG16 model, known for its robust performance in image classification, has been applied to the task of classifying pests in oil palm plants. This study aims to evaluate the effectiveness of the VGG16 model in identifying pests on oil palm, comparing the performance of default settings with models fine-tuned using grid search and random search techniques. We employed a quantitative approach, training the VGG16 model with three different configurations: default, fine-tuned with grid search, and fine-tuned with random search. Evaluation metrics including precision, recall, F1-Score, and overall accuracy were used to assess model performance across different pest categories: Metisa plana, Setora nitens, and Setothosea asigna. The default VGG16 model achieved precision, recall, and F1-Score values around 90% for Metisa plana, Setora nitens, and Setothosea asigna, with an overall accuracy of 91.00%. Fine-tuning with grid search improved these metrics, with precision, recall, and F1-Score reaching approximately 93.88%, 92%, and 92.93% respectively, and an overall accuracy of 93%. The random search fine-tuning resulted in even higher performance, with precision of about 95.92%, recall of 94%, and F1-Score of 94.95% for Metisa plana, and overall accuracy of 94.67%. The VGG16 model demonstrated strong performance in pest classification on oil palm, with significant improvements achieved through fine-tuning techniques. The study confirms that grid search and random search fine-tuning can substantially enhance model accuracy and efficacy. Future research should focus on expanding the dataset to include more diverse pest species, incorporating attention mechanisms, and leveraging automated control technologies like drones and the Internet of Things (IoT) to further improve pest management practices.

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