Deep Learning for Pest Detection and Extraction

Pest infestations pose a significant challenge to agriculture, resulting in substantial crop damage and economic losses. Traditional pest detection systems primarily rely on Convolutional Neural Networks (CNNs) for image classification. While CNNs are effective at categorizing images and identifying pests, they face limitations in handling scenarios involving multiple pests, varying orientations, and complex backgrounds. Additionally, CNNs lack the ability to localize pests within images, providing only image-level classifications rather than detailed spatial information. To address these limitations, the proposed system introduces YOLOv5, a state-of-the-art object detection model. Unlike CNN-based approaches, YOLOv5 excels in detecting and localizing multiple pests in real-time, even in challenging conditions. By producing both bounding boxes and class labels, YOLOv5 offers precise localization and identification of pests, enabling targeted pest management. Its real-time detection capabilities and superior accuracy in complex environments make it a powerful tool for agricultural applications. The transition from CNNs to YOLOv5 brings several advantages, including enhanced detection performance, the ability to identify multiple pests in a single frame, and scalability across diverse datasets and environmental conditions.