Boost Precision Agriculture with Unmanned Aerial Vehicle Remote Sensing and Edge Intelligence: A Survey

TLDR

Unmanned aerial vehicles provide high‑resolution remote sensing for precision agriculture, reducing costs and environmental impact, while deep learning offers powerful analysis but demands high computation, and edge intelligence promises low‑latency, on‑device analytics close to data sources. This paper surveys the latest developments in precision agriculture that combine UAV remote sensing with edge intelligence. The survey compiles and analyzes recent UAV systems, sensor datasets, deep‑learning methods, and edge‑computing strategies. The survey finds that small or light UAVs are widely used, RGB sensors dominate public datasets, deep‑learning tasks include classification, detection, and segmentation with CNNs and RNNs, cloud computing is common but edge computing offers lower latency, and edge intelligence relies mainly on model compression such as pruning and quantization on CPUs, GPUs, and FPGAs.

Abstract

In recent years unmanned aerial vehicles (UAVs) have emerged as a popular and cost-effective technology to capture high spatial and temporal resolution remote sensing (RS) images for a wide range of precision agriculture applications, which can help reduce costs and environmental impacts by providing detailed agricultural information to optimize field practices. Furthermore, deep learning (DL) has been successfully applied in agricultural applications such as weed detection, crop pest and disease detection, etc. as an intelligent tool. However, most DL-based methods place high computation, memory and network demands on resources. Cloud computing can increase processing efficiency with high scalability and low cost, but results in high latency and great pressure on the network bandwidth. The emerging of edge intelligence, although still in the early stages, provides a promising solution for artificial intelligence (AI) applications on intelligent edge devices at the edge of the network close to data sources. These devices are with built-in processors enabling onboard analytics or AI (e.g., UAVs and Internet of Things gateways). Therefore, in this paper, a comprehensive survey on the latest developments of precision agriculture with UAV RS and edge intelligence is conducted for the first time. The major insights observed are as follows: (a) in terms of UAV systems, small or light, fixed-wing or industrial rotor-wing UAVs are widely used in precision agriculture; (b) sensors on UAVs can provide multi-source datasets, and there are only a few public UAV dataset for intelligent precision agriculture, mainly from RGB sensors and a few from multispectral and hyperspectral sensors; (c) DL-based UAV RS methods can be categorized into classification, object detection and segmentation tasks, and convolutional neural network and recurrent neural network are the mostly common used network architectures; (d) cloud computing is a common solution to UAV RS data processing, while edge computing brings the computing close to data sources; (e) edge intelligence is the convergence of artificial intelligence and edge computing, in which model compression especially parameter pruning and quantization is the most important and widely used technique at present, and typical edge resources include central processing units, graphics processing units and field programmable gate arrays.