Advances and challenges in the applications of drone systems in precision agriculture: A review.
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| Title: | Advances and challenges in the applications of drone systems in precision agriculture: A review. |
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| Authors: | Kaousar, Rehana1,2 rehanakaousar916@gmail.com, Wang, Guobin1,2 guobinwang@sdut.edu.cn, Hussain, Mujahid1,3 mujahidagr@gmail.com, Aslan, Muhammet Fatih4 mfatihaslan@kmu.edu.tr, Wang, Baoju1,2 wbj@sdut.edu.cn, Yan, Yu1,2 24603010025@stumail.sdut.edu.cn, Rafique, Nadia1,2 nadiarafique5555@gmail.com, Song, Cancan1,2 songcc@sdut.edu.cn, Zhang, Xuejian5 xuejian-zhang@21cn.com, Lan, Yubin1,2 ylan@sdut.edu.cn |
| Source: | International Journal of Agricultural & Biological Engineering. Jun2026, Vol. 19 Issue 3, p1-19. 19p. |
| Subjects: | Drone aircraft, Precision farming, Drone aircraft control systems, Machine learning, Remote sensing, Vegetation monitoring, Artificial intelligence |
| 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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