A Transfer Learning And CNN Based Computer Vision System For Identifying Dangerous Form Insect

Abstract

Agricultural crop production is significantly affected by harmful insects that damage plants, reduce yield, and impact food quality. Early and accurate identification of dangerous insects is essential for effective pest management and sustainable farming practices. This paper presents a Transfer Learning and Convolutional Neural Network (CNN)-based computer vision system for identifying dangerous farm insects from image data. The proposed system utilizes pre-trained deep learning models such as ResNet50, MobileNetV2, or EfficientNet to extract discriminative visual features from insect images, reducing training time while improving classification accuracy. Image preprocessing techniques, including resizing, normalization, and data augmentation, are applied to enhance model robustness against variations in lighting, orientation, and background conditions. The extracted features are further processed through CNN layers to classify insects into different harmful species categories. Experimental evaluation demonstrates that the proposed approach achieves high accuracy, precision, recall, and F1-score in insect identification, outperforming traditional machine learning methods. The system can assist farmers and agricultural experts in real-time pest monitoring and decision-making, enabling timely intervention and reducing crop losses. The proposed intelligent insect detection framework contributes to the advancement of precision agriculture by providing an automated, scalable, and efficient solution for dangerous farm insect recognition and management.