Agricultural Pest Identification Enhanced with Deep Learning Features and Machine Learning Models

Abstract— The research proposes a novel approach combining deep feature extraction using machine learning and traditional machine learning techniques to classify 12 agricultural pests. Individual features were extracted through AlexNet, GoogLeNet, and feature fusion; afterwards, they were classified using K-Nearest Neighbors, Support Vector Machine, and Random Forest. GoogLeNet achieved 86.21% accuracy with SVM, while the fused features achieved 82.03% with Random Forest. The proposed method makes good use of deep learning with feature representation and classical models for accurate and computationally efficient pest identification in agricultural applications.