Pest Counting

Automated pest counting, crucial for efficient pest management and crop protection, is a rapidly developing field focusing on accurately estimating pest populations from digital images, often captured by light traps. Current research emphasizes the development of deep learning models, particularly adaptations of CenterNet architectures, incorporating multiscale and deformable attention mechanisms to address challenges like occlusion and variations in pest size and pose. These advancements enable more precise and efficient pest monitoring, leading to improved crop yields and reduced pesticide use, with applications extending to broader ecological monitoring and precision pollination strategies.