Improved Grapes Detection and Tracking in Drones Imagery by Integrating the Coordinate Attention Mechanism

Recent advancements in deep learning algorithms have revolutionised computer vision, significantly impacting precision agriculture and farming automation. Object detection and tracking techniques, supported by drones equipped with vision sensors, play pivotal roles in monitoring and enhancing agricultural processes. This study focuses on improving object detection for precision viticulture, addressing challenges such as the small size of grape bunches and drone-induced distortions. By integrating the Coordinate Attention mechanism into the GELAN network, we enhance the detection of grapes in high-resolution images. Additionally, the study evaluates three methods for stabilising grape tracking, with ORB feature matching proving superior. Overall, these advancements contribute to automated crop monitoring and increased agricultural productivity through robust computer vision techniques. The findings underscore the significance of tailored approaches in adapting object detection technologies to specific precision agriculture applications.