Advances and challenges in the applications of drone systems in precision agriculture: A review

Authors

  • Rehana Kaousar 1. College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, Shandong, China; 2. Shandong University of Technology Sub-center of National Center for International Collaboration Research on Precision AgriculturalAviation Pesticide Spraying Technology, Zibo 255000, Shandong, China;
  • Wang Guobin 1. College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, Shandong, China; 2. Shandong University of Technology Sub-center of National Center for International Collaboration Research on Precision AgriculturalAviation Pesticide Spraying Technology, Zibo 255000, Shandong, China;
  • Mujahid Hussain 1. College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, Shandong, China; 3. Department of Plant Science, Faculty of Agricultural and Food Sciences, University of Manitoba, 66 Dafoe Road,Winnipeg, MB, R3T 2N2, Canada;
  • Muhammet Fatih Aslan 4. Department of Artificial Intelligence and Machine Learning, Faculty of Computer and Information Sciences,Konya Technical University, Konya 42000, Turkey;
  • Baoju Wang 1. College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, Shandong, China; 2. Shandong University of Technology Sub-center of National Center for International Collaboration Research on Precision AgriculturalAviation Pesticide Spraying Technology, Zibo 255000, Shandong, China;
  • Yu Yan 1. College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, Shandong, China; 2. Shandong University of Technology Sub-center of National Center for International Collaboration Research on Precision AgriculturalAviation Pesticide Spraying Technology, Zibo 255000, Shandong, China;
  • Nadia Rafique 1. College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, Shandong, China; 2. Shandong University of Technology Sub-center of National Center for International Collaboration Research on Precision AgriculturalAviation Pesticide Spraying Technology, Zibo 255000, Shandong, China;
  • Cancan Song 1. College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, Shandong, China; 2. Shandong University of Technology Sub-center of National Center for International Collaboration Research on Precision AgriculturalAviation Pesticide Spraying Technology, Zibo 255000, Shandong, China;
  • Xuejian Zhang 5. Economy and Information Technology, Ningxia Academy of Agriculture and Forestry Sciences, Yinchuan 750003, China
  • Yubin Lan 1. College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, Shandong, China; 2. Shandong University of Technology Sub-center of National Center for International Collaboration Research on Precision AgriculturalAviation Pesticide Spraying Technology, Zibo 255000, Shandong, China;

Keywords:

Unmanned aerial Vehicles, Precision agriculture, Remote sensing, Sensor development, UAV limitations, Internet of Things.

Abstract

Climate change, resource limitations, and increasing global food demand are accelerating the need for efficient and sustainable agricultural management practices. Unmanned aerial vehicles (UAVs) have emerged as a transformative technology in precision agriculture (PA) because of their capability to provide high-resolution, real-time, and site-specific crop monitoring. This review critically examines recent advancements (2016–2025) in UAV-assisted PA, focusing on UAV platforms, sensing technologies, data acquisition systems, information fusion methods, and artificial intelligence (AI)-driven analytical frameworks. Particular emphasis is placed on applications including crop monitoring, disease and pest detection, weed mapping, irrigation management, soil assessment, yield estimation, phenotyping, and precision spraying. The review highlights that integrating RGB, multispectral, hyperspectral, thermal, and LiDAR sensors with machine learning (ML) and deep learning (DL) algorithms substantially improves monitoring accuracy, operational efficiency, and agricultural decision-making compared with conventional practices. Algorithms such as Random Forest (RF), Support Vector Machine (SVM), convolutional neural networks (CNNs), and YOLO-based models have demonstrated strong effectiveness in yield prediction, disease recognition, and weed discrimination. Despite these advancements, several challenges continue to limit large-scale implementation, including restricted flight endurance, payload limitations, environmental sensitivity, data-processing complexity, interoperability issues, and limited AI model transferability across different agricultural environments. Furthermore, model performance remains highly dependent on sensor configuration, dataset quality, and field-specific environmental conditions. Recent developments indicate rapid commercialization of UAV technologies together with emerging trends in edge AI, explainable AI (XAI), UAV–IoT integration, cloud-based analytics, and autonomous multi-UAV systems. Overall, this review identifies major technological advancements, key operational limitations, and future research directions required to support scalable, reliable, and climate-resilient UAV-assisted agricultural systems.

Keywords: precision agriculture, unmanned aerial vehicles, artificial intelligence, machine learning, deep learning, cropmonitoring

DOI: 10.25165/j.ijabe.20261903.9288


Citation: Kaousar R, Wang G B, Hussain M, Aslan M F, Wang B J, Yan Y, et al. Advances and challenges in the applications of drone systems in precision agriculture: A review. Int J Agric & Biol Eng, 2026; 19(3): 1–19.

 

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2026-07-14

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Kaousar, R.; Guobin, W.; Hussain, M.; Aslan, M. F.; Wang, B.; Yan, Y.; Rafique, N.; Song, C.; Zhang, X.; Lan, Y. Advances and Challenges in the Applications of Drone Systems in Precision Agriculture: A Review. Int J Agric & Biol Eng 2026, 19, 1-19.

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