Enhancing Pest Classification in Oil Palm Farming: A Deep Learning Approach with GoogleNet Architecture and Fine-Tuning Strategies

Abstract

Recent developments in the field of deep learning models for agricultural pest classification, particularly in relation to oil palm cultivation, highlight the possibility of accurate and effective pest identification. The goal of this study is to use deep learning computation to classify pests in oil palm. The main goal is to evaluate this method's effectiveness in identifying pests. The GoogleNet architecture, GoogleNet with fine-tuned grid search, and GoogleNet with fine-tuned random search are all used in the study. A thorough examination of the performance of the three models is carried out using a variety of assessment metrics, including accuracy, precision, recall, and F1-Score. Photographs of pests on oil palm plants are included in the dataset. While models improved by grid search and random search show significant performance improvement, approaching nearly perfect evaluation metrics, the default GoogleNet model exhibits high accuracy. These results imply that customization improves the model's precision and effectiveness. The study's findings highlight the efficiency of GoogleNet -based models in oil palm farms for classifying pests, with fine-tuning considerably improving their output. In order to advance pest monitoring and management in oil palm cultivation, future research avenues should prioritize dataset expansion, additional model optimization, and the integration of drone-based automatic control and Internet of Things (IoT) technologies.