Work place: Universitas Medan Area, Fakultas Pertanian, Program Studi Agroteknologi, Medan, Indonesia
E-mail: dwika@staff.uma.ac.id
Website: https://orcid.org/0000-0001-6537-4642
Research Interests:
Biography
Dwika Karima Wardani is an expert in the field of Plant Pests and Diseases, plays a crucial role in the academic environment of Universitas Medan Area. With her degree in the relevant field, Dwika has established herself as a dedicated researcher and educator. Her academic credentials strengthen her knowledge foundation in understanding the complex dynamics related to plant pests and diseases. As a part of the Universitas Medan Area community, Dwika actively engages in in-depth research, focusing on the identification, analysis, and development of prevention and mitigation strategies for plant pests and diseases affecting plant growth.
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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