In the face of growing challenges in modern agriculture, such as climate change, sustainable resource management, and food security, drones are emerging as essential tools for transforming precision agriculture. This systematic review, based on an in-depth analysis of recent scientific literature (2020–2024), provides a comprehensive synthesis of current drone applications in the agricultural sector, primarily focusing on studies from this period while including a few notable exceptions of particular interest. Our study examines in detail the technological advancements in drone systems, including innovative aerial platforms, cutting-edge multispectral and hyperspectral sensors, and advanced navigation and communication systems. We analyze diagnostic applications, such as crop monitoring and multispectral mapping, as well as interventional applications like precision spraying and drone-assisted seeding. The integration of artificial intelligence and IoTs in analyzing drone-collected data is highlighted, demonstrating significant improvements in early disease detection, yield estimation, and irrigation management. Specific case studies illustrate the effectiveness of drones in various crops, from viticulture to cereal cultivation. Despite these advancements, we identify several obstacles to widespread drone adoption, including regulatory, technological, and socio-economic challenges. This study particularly emphasizes the need to harmonize regulations on beyond visual line of sight (BVLOS) flights and improve economic accessibility for small-scale farmers. This review also identifies key opportunities for future research, including the use of drone swarms, improved energy autonomy, and the development of more sophisticated decision-support systems integrating drone data. In conclusion, we underscore the transformative potential of drones as a key technology for more sustainable, productive, and resilient agriculture in the face of global challenges in the 21st century, while highlighting the need for an integrated approach combining technological innovation, adapted policies, and farmer training.

Surface water monitoring often requires frequent and reliable sampling at locations that are difficult or unsafe to access using conventional methods. To address these constraints, unmanned aerial vehicles (UAVs) have increasingly been used as platforms for automated water collection. This paper presents a UAV-assisted water sampling system designed to provide a high degree of operational flexibility through control of sampling depth and collected volume. The proposed system can collect up to 3 L of water distributed across six individual containers, enabling a variety of sampling strategies, including discrete, composite, and multi-depth sampling. Sampling depths of up to 3.25 m can be achieved, supporting depth-resolved investigations such as stratified water-column analyses. Compared with existing UAV-based samplers, the proposed system combines configurable sampling depth, configurable sampling volume, and a multi-bottle architecture within a single platform. To support sample integrity, an automated cleaning sequence is executed before each collection step to reduce the risk of cross-contamination between samples. Experimental validation demonstrated repeatable depth deployment under controlled conditions and consistent volume-control performance, with relative errors generally within ±3% and a maximum observed deviation of 5%. In addition, a task-based analysis provided an initial characterization of the energy demand associated with the sampling process. Overall, the results indicate that the proposed architecture can support flexible and protocol-oriented water sampling operations while extending the sampling capabilities of existing UAV-based systems.

In this paper we propose a generic algorithm to calculate the rotation parameters of CORDIC angles required for the Discrete Cosine Transform algorithm (DCT). This leads us to increase the precision of calculation meeting any accuracy.Our contribution is to use this decomposition in CORDIC based DCT which is appropriate for domains which require high quality and top precision. We then propose a hardware implementation of the novel transformation, and as expected, a substantial improvement in PSNR quality is found.