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.

Unmanned aerial vehicle (UAV) wind field airflow is the main factor affecting spraying width and operational effect. Under the soybean and maize compound planting mode, the width of the spray droplets of UAV aerial spraying is easily sprayed on other crops around the target crop by mistake due to the influence of the wind field. In order to solve this problem, a narrow-width spraying UAV equipped with an airflow guidance device was developed in this study, which can achieve precise spraying on target crops within a narrow operation width and reduce the impact of droplet drift on surrounding non-target crops. The wind field of the flight platform was simulated via simulation software, and the wind field distribution characteristics corresponding to three sizes of the guidance device were analyzed. It was verified that the guidance device has a segmentation effect on the wind field, and the optimal size of the guidance device was determined as 0.92 m accordingly. Meanwhile, the installation position and quantity of nozzles were determined to be set at the position with the minimum airflow disturbance. The wind speed at measuring points with different angles on four diameters at five heights under the UAV hovering state was tested through bench tests, to further verify the simulation results and appropriately adjust the nozzle position according to the measured wind speed. Outdoor flight spraying tests were carried out, and the results showed that when the 0.92 m guidance device was applied at a flight altitude of 1.00 m, the effective spray width was only 1.46 m according to the droplet deposition density on the coated paper of three test collection belts. Under the maize-soybean composite planting pattern, the field spraying test yielded results of 18.0%-30.0% deposition rate in the maize area and 0.1%-1.6% in the soybean area with maize as the operational target. A predictive effect of wind field simulation on the installation position of nozzles where droplets suffer the least wind field disturbance under aerial spraying conditions was confirmed. The UAV wind field can be effectively segmented by the airflow guidance device, and the diameter of the droplet-laden airflow column can be reduced, thus realizing narrow-width spraying.

Droplet drift can be generated during pesticide application by plant-protection Unmanned Aerial Vehicles (UAVs) in agricultural production. Excessive spray drift can reduce pesticide utilization efficiency, leading to a series of environmental and ecological risks, such as environmental contamination, off-target deposition, and phytotoxic effects on non-target organisms. It is often required to accurately characterize and then predict the droplet drift behavior under diverse operations, particularly for safe and sustainable spraying. International standards, such as ISO 22866:2005, have also been established to quantify the spray drift assessment in the field measurement protocols. However, conventional drift prediction models of manned aircraft or ground sprayers cannot be extended into the UAV spraying scenarios, due to the aerodynamic characteristics of multi-rotor UAVs—such as strong downwash airflow, low-altitude operation, and flexible flight parameters. In this study, the data-driven prediction models were developed to explicitly incorporate UAV operational features and environmental variability. The spray drift was measured on four plant-protection UAVs under typical operation. 150 datasets were then collected after measurement. Spearman correlation analysis was used to effectively capture the complex coupling relationships between multiple influencing factors and droplet drift rate. Feature importance was ranked to identify key variables using Random Forest. A prediction model was constructed for the droplet drift in the plant-protection UAVs using a Backpropagation (BP) neural network. SHAP (SHapley Shapley Additive exPlanations) was introduced to interpret the outputs and then quantify the contribution rates of each influencing factor. Furthermore, a specific UAVDP (UAV Droplet Drift Prediction Platform) was developed to facilitate practical application. A systematic comparison was made using the physical drift prediction tool (AGDISPpro). The results demonstrate that the BP neural network model was achieved in the high prediction accuracy and generalization, outperforming several conventional machine learning models. The accuracy was also improved over AGDISPpro. SHAP interpretability analysis revealed that the wind speed and spray flow rate were dominant positive contributors to spray drift. While operational height and nozzle number exhibited the notable suppression. The drift risk level of plant-protection UAV operations was quantitatively assessed after prediction. The required downwind buffer distancebuffer distance downwind was generally less than 20 m under compliant wind speed conditions (<5 m/s). The drift risk can be found between manned agricultural aircraft and large ground boom sprayers. Overall, this finding can provide a data-driven modeling framework, interpretable insights into drift mechanisms, and a practical prediction platform. Robust technical support can also offer the precision pesticide applications, drift risk control, and the decision-making on safe UAV spraying.

UAV spreading has been widely used in modern agriculture in recent years. However, the scattering behavior of granular materials can often be produced during spreading. At the same time, the material residue in the trough cannot be fully cleaned up, when the grooved wheel is used to discharge materials. In this study, a strip seeding device of the unmanned aerial vehicle was proposed for the granular fertilizer application. Firstly, a variable-diameter grooved wheel was designed, according to the principle of cam motion. The traditional grooved wheel was split into several parts, such as the groove and the central shaft. The central shaft was replaced with a camshaft as a fixed part. The rotation of the camshaft was used to realize the discharging fertilizers for the cyclic changes. The discharge volume rose in steps with the increase in rotational speed. Finally, the gap then decreased to stabilize and slightly decline in the trend of change. A deflector device was also designed with adjustable work spacing. A layered pattern was used to centralize the overall device. The spacing between rows was adjusted via the outlet direction of the guide tube and the angle between adjacent tubes. The row alignment was realized to avoid the redundant structure in the actual operation. The whole device was driven by electric power. According to man-machine communication, the microprocessor was used to realize the regulation on the rotational speed of the grooved wheel, the size of the wind power of the culvert system, and the working distance of the rows. The simulation model was also established for the fertilizer discharging of the strip applicator. A bench test was adopted to verify the simulation using the discrete element software (EDEM). A systematic investigation was then implemented to explore the effects of the structural parameters of the variable-diameter grooved wheel and the rotational speed on the movement of the fertilizer particles during operation. The optimal structural parameters of the fertilizer discharger were obtained after optimization. A comparative test was carried out to verify the variable diameter grooved wheel. The bench results show that the better uniform discharge was achieved among different pipes of the device, with the highest coefficient of variation of 1.30%, and the highest discharge of 453 g/s at 120 r/min; The better performance of the grooved wheel was achieved at the same rotational speed, compared with the ordinary ones. The simulation results show that the variable-diameter chute wheel was better and faster at discharging the material in the trough. A comparison was also made on the amount of fertilizer particles backfilled under the same rotational speed of the two types of tank wheels. The reducer tank wheel significantly improved the issue of material residue. The field test showed that the angle of the guide tube also dominated the row spacing and striping index. The better striping was achieved in the minimum striping index of 29.1%, indicating the rationality and feasibility of this device. The finding can also provide a strong reference to design the striping device in unmanned aircraft.