Exploring the YOLO-FT Deep Learning Algorithm for UAV-Based Smart Agriculture Detection in Communication Networks

Abstract

Advancements in technology hold significant promise for the future of smart agriculture. Drones, serving as innovative tools for data acquisition, play a pivotal role in this context. Traditional pollination drones, which rely on extensive spraying methods, suffer from poor efficiency. To address this challenge, our focus is on enhancing pollination through object detection technology, leading to the introduction of the YOLO-FT detection algorithm based on YOLOv7. To validate our algorithm, we have curated a dataset of fruit tree flowers. Our approach involves several key components. Firstly, we propose a lightweight adaptive backbone network, which effectively extracts feature information through partial convolution and seamlessly integrates feature data from diverse channels. Secondly, we introduce a non-parametric attention module into neck network, bolstering the fusion of critical feature information. Finally, we leverage a rapid convergence function based on centroid distance to enhance bounding box regression performance. Experimental results demonstrate the superiority of the YOLO-FT algorithm, achieving <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$mAP_{50}$ </tex-math></inline-formula> of 92.8%, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$mAP_{50-95}$ </tex-math></inline-formula> of 48.8%, and F1 score of 0.90, marking improvements of 3.6%, 1.6%, and 0.17, respectively, over the baseline. Furthermore, YOLO-FT exhibits a significant reduction in model complexity, with parameters decreased by 13.4% and FLOPs by 17.8%. This research serves as a valuable theoretical reference for advancements in smart agriculture.

Topics & Concepts