Optimizing VGG19 for Accurate Pest Identification in Oil Palm

Recent advancements in pest classification using deep learning models have shown promising results in various agricultural contexts. The VGG19 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 VGG19 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 VGG19 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 VGG19 model achieved precision, recall, and F1-Score values around 92,36%, 92.33% and 92.34 for Metisa plana, Setora nitens, and Setothosea asigna, with an overall accuracy of 92.33%. Fine-tuning with grid search improved these metrics, with precision, recall, and F1-Score reaching approximately 94.20, 94, and 94.01% respectively, and an overall accuracy of 94%. The random search fine-tuning resulted in even higher performance, with precision of about 95.68%, recall of 95.67%, and F1-Score of 95.67% for Metisa plana, and overall accuracy of 95.67%. The VGG19 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.