Insect Classification Using a Hybrid Deep Learning Framework with EfficientNetB5 and DenseNet201

Identifying insects is essential for examining species behavior, ecological stability, and agricultural effects, but conventional techniques are labor-intensive, require specialized knowledge, and aren’t easily accessible. To address these challenges, this research introduces an AI-based insect classification system employing a hybrid deep learning framework that merges Effi-cientNetB5 and DenseNet201 to improve accuracy and reliability. The system analyzes images uploaded by users and extracts organized information from a set database to offer insights regarding insect behavior, disease transmission, climate impact, and agricultural threats. In contrast to traditional classification models that frequently face challenges with precision and access to real-time data, this method guarantees quicker processing and dependable information retrieval. The model’s performance is assessed through Precision, Recall, and F1-score, showcasing enhanced classification capabilities, rendering it a significant resource for researchers, farmers, and conservationists.