Unmanned Aerial Vehicle Surveillance Research Articles

Perceptive Mobile Networks for Unmanned Aerial Vehicle Surveillance: From the Perspective of Cooperative Sensing

Perceptive Mobile Networks for Unmanned Aerial Vehicle Surveillance: From the Perspective of Cooperative Sensing

High-tech herding: Exploring the use of IoT and UAV networks for improved health surveillance in dairy farm system

The integration of IoT and UAVs in traditional farming has revolutionized operations, offering farmers increased output, better decision-making, and sustainability. This paper focuses on their use in dairy farming, aiming to enhance operational efficiency and health monitoring by providing real-time data for decision-making. The paper proposed an IoT-UAV system for cattle health monitoring. The proposed system is one in which IoT sensors on cattle gather health data, which is subsequently sent to UAVs for aerial surveillance and intervention. It evaluates two scenarios: one with 10 cattle and another with 50. Scenario 1 shows UAVs covering a large area with low overlap, while Scenario 2 focuses on close monitoring of cow clusters, with UAVs switching routes due to low energy. The cattle health status varies notably between the two scenarios. In Scenario 1, there's more diversity, with some cattle having higher health statuses around 0.9, while in Scenario 2, most fall into lower ranges, mainly between 0.2 and 0.6, indicating more uniformity. Additionally, UAV energy levels in Scenario 1 show considerable variation, from nearly full to around 20 %, suggesting energy depletion during observation. In Scenario 2, all UAVs maintain energy levels close to 100 %, indicating efficient and less frequent use compared to Scenario 1. Nearest Neighbor response plots show more uniform distances between cattle and UAVs in Scenario 2, where energy levels are considered, unlike in Scenario 1. This suggests a conservative approach to distance coverage, likely to conserve energy by avoiding long-distance travel.

Strategies for Optimized UAV Surveillance in Various Tasks and Scenarios: A Review

This review paper provides insights into optimization strategies for Unmanned Aerial Vehicles (UAVs) in a variety of surveillance tasks and scenarios. From basic path planning to complex mission execution, we comprehensively evaluate the multifaceted role of UAVs in critical areas such as infrastructure inspection, security surveillance, environmental monitoring, archaeological research, mining applications, etc. The paper analyzes in detail the effectiveness of UAVs in specific tasks, including power line and bridge inspections, search and rescue operations, police activities, and environmental monitoring. The focus is on the integration of advanced navigation algorithms and artificial intelligence technologies with UAV surveillance and the challenges of operating in complex environments. Looking ahead, this paper predicts trends in cooperative UAV surveillance networks and explores the potential of UAVs in more challenging scenarios. This review not only provides researchers with a comprehensive analysis of the current state of the art, but also highlights future research directions, aiming to engage and inspire readers to further explore the potential of UAVs in surveillance missions.

Predefined-time control design for tracking chaotic trajectories around a contour with an UAV

The surveillance or monitoring of places is crucial to ensuring security, protecting people and assets, preventing crimes, and detecting emergencies, to mention some. Unmanned Aerial Vehicles (UAVs) play a vital role in these applications, offering versatility, agility, and aerial vision. A crucial step for such tasks is to protect the UAV path ahead. This paper focuses on a methodology harnessing the unpredictable nature of chaotic systems to generate trajectories around a closed area or contour. However, although a vast quantity of research papers mention the use of chaotic path generation, they have yet to learn about the control system and the dynamics affecting the UAV, where developing the control theory is challenging. In this paper, we design controllers based on predetermined-time stability, ensuring the achievement of the desired trajectory before a specified time. Additionally, adjusting control parameters is a crucial step during the control design, impacting the control performance. Hence, we present a method to optimize and adapt controller parameters through evolutionary optimization, demonstrating precision enhancement. We validate the proposed system’s performance and the controllers through numerical simulations, indicating that the UAV effectively and accurately follows some types of chaotic trajectories like a square contour, aiming at the feasibility of this methodology in real UAV surveillance applications.

UAV Detection Using Reinforcement Learning.

Unmanned Aerial Vehicles (UAVs) have gained significant popularity in both military and civilian applications due to their cost-effectiveness and flexibility. However, the increased utilization of UAVs raises concerns about the risk of illegal data gathering and potential criminal use. As a result, the accurate detection and identification of intruding UAVs has emerged as a critical research concern. Many algorithms have shown their effectiveness in detecting different objects through different approaches, including radio frequency (RF), computer vision (visual), and sound-based detection. This article proposes a novel approach for detecting and identifying intruding UAVs based on their RF signals by using a hierarchical reinforcement learning technique. We train a UAV agent hierarchically with multiple policies using the REINFORCE algorithm with entropy regularization term to improve the overall accuracy. The research focuses on utilizing extracted features from RF signals to detect intruding UAVs, which contributes to the field of reinforcement learning by investigating a less-explored UAV detection approach. Through extensive evaluation, our findings show the remarkable results of the proposed approach in achieving accurate RF-based detection and identification, with an outstanding detection accuracy of 99.7%. Additionally, our approach demonstrates improved cumulative return performance and reduced loss. The obtained results highlight the effectiveness of the proposed solution in enhancing UAV security and surveillance while advancing the field of UAV detection.

Mission‐based design of UAVs

AbstractOver the past decades, Unmanned Aerial Vehicles (UAVs) have increasingly been used in a wide variety of missions that range from surveillance to delivery. Unlike aircraft that always carry goods and passengers from an airport to another, UAVs do not systematically implement the same type of mission. UAVs are indeed multi‐mission during their time in operation, and the systems engineering approaches developed for one mission aircraft must be adapted to the multi‐mission context. Therefore, UAV design requires application of mission engineering upstream systems engineering, either to assess there is a UAV system that may accomplish a new mission, or to specify a new UAV system according to a given mission. To achieve that goal, the authors of the paper support the use of Model‐Based Mission Engineering. A three‐layer architecture ‐ purpose, operation, functions or capabilities ‐ is proposed as a design framework for missions. The Goal‐Oriented Requirements Language (GRL) serves as mission description language. The paper extends GRL to better address mission‐based design of UAVs. It is proposed to distinguish between internal and external resources. A goal detailing mechanism is introduced. A degraded mode evaluation becomes possible. GRL tools make it possible to evaluate how much a UAV system ‐ at least, an operator, a ground station, and a UAV ‐ may satisfy every stakeholder in both nominal and degraded modes. The proposed approach is applied to a high voltage surveillance UAV. The case study enables the introduction of four actors—Authority, Client, UAV and MissionSupervisor—that turn out to be generic and can be reused for other missions and UAV designs.

Optimal Placement of UAVs to Provide Surveillance Coverage for a Ground Vehicle in a Collaborative Search-and-Rescue Operation

A drone-truck combined search-and-rescue operation involves a ground vehicle and a swarm of unmanned aerial vehicles (UAVs), where the UAVs provide surveillance coverage to guide the ground vehicle to navigate through the environment and carry out the search and rescue, and the ground vehicle functions as a service hub for carrying and recharging the UAVs. An effective strategy for providing persistent UAV surveillance coverage around the ground vehicle consists of initially forming the UAV swarm coverage and then controlling the UAV formation to follow the ground vehicle. This paper focuses on the formation of coverage and presents a method for planning an optimal placement of the UAVs to form seamless surveillance coverage around the ground vehicle. The optimization problem is formulated to determine the number and positions of UAVs that minimize the energy consumption in deploying and collecting those UAVs, subject to a set of constraints in UAV positioning, communication, and coverage, specifically the available number of UAVs, allowable range of UAV altitude, allowable energy consumption for deploying and collecting each UAV, communication ranges of UAVs and ground vehicle, safety distance between UAVs for collision and interference avoidance, and seamless coverage. A bi-layer optimization procedure is developed, with an outer layer searching through the allowable numbers of UAVs and an inner layer searching for the optimal positions for each specific number of UAVs. The optimal number and positions of UAVs are chosen by comparing among the solutions for different numbers of UAVs. A simulation study is carried out to validate the proposed optimization formulation and solution approach, where the simulation settings of UAVs, particularly the critical parameters including the UAV energy constants, visibility angle, altitude, and communication range, use the representative values presented in the cited literature. The simulation results show that the proposed approach is effective in planning the optimal number and positions of UAVs to provide seamless surveillance coverage for a ground vehicle. The next step of research will set priorities on comprehending the complexity of the solution space and enhancing the global optimality of the solution.

Minimizing Fuel Consumption for Surveillance Unmanned Aerial Vehicles Using Parallel Particle Swarm Optimization.

This paper presents a method based on particle swarm optimization (PSO) for optimizing the power settings of unmanned aerial vehicle (UAVs) along a given trajectory in order to minimize fuel consumption and maximize autonomy during surveillance missions. UAVs are widely used in surveillance missions and their autonomy is a key characteristic that contributes to their success. Providing a way to reduce fuel consumption and increase autonomy provides a significant advantage during the mission. The method proposed in this paper included path smoothing techniques in 3D for fixed-wing UAVs based on circular arcs that overfly the waypoints, an essential feature in a surveillance mission. It used the equations of motions and the decomposition of Newton's equation to compute the fuel consumption based on a given power setting. The proposed method used PSO to compute optimized power settings while respecting the absolute physical constraints, such as the load factor, the lift coefficient, the maximum speed and the maximum amount of fuel onboard. Finally, the method was parallelized on a multicore processor to accelerate the computation and provide fast optimization of the power settings in case the trajectory was changed in flight by the operator. Our results showed that the proposed PSO was able to reduce fuel consumption by up to 25% in the trajectories tested and the parallel implementation provided a speedup of 21.67× compared to a sequential implementation on the CPU.

Crowd Emotion Prediction for Human-Vehicle Interaction Through Modified Transfer Learning and Fuzzy Logic Ranking

In metropolitan environments, unmanned aerial vehicles (UAVs) equipped with video surveillance equipment can monitor crowd behavior and maintain public safety. In high-traffic areas where humans are more likely to make mistakes, a smart city needs modern technology to forecast the behavior of its residents. In order to improve citywide traffic flow, urban transportation systems (UTSs) monitor and learn how people behave in crowds. Using UAVs for video surveillance in smart cities, our research describes a unique way to assess crowd condition, which expands the scope of human-vehicle interactions. Moreover, we use fuzzy logic ranking to improve the system’s ability to detect anomalies in crowds. In order to improve decision-making, a novel deep transfer learning (DTL) technique is applied to the UAV’s received frames. A 98.5% accuracy rate, satisfactory performance, and robustness to population behavior are all characteristics of the proposed integrated model. In UTSs and urban areas, our novel intelligent system analyzes human behavior based on vehicle-human interactions. In areas with low and high traffic congestion, the modified ResNet (mResNet) architecture predicts the crowd’s condition based on fuzzy logic (FLA). Through decision-making based on accurate crowd conditions, best general paths can be selected using the algorithm.

QuickNav: An Effective Collision Avoidance and Path-Planning Algorithm for UAS

Obstacle avoidance is a desirable capability for Unmanned Aerial Systems (UASs)/drones which prevents crashes and reduces pilot fatigue, particularly when operating in the Beyond Visual Line of Sight (BVLOS). In this paper, we present QuickNav, a solution for obstacle detection and avoidance designed to function as a pre-planned onboard navigation system for UAS flying in a known obstacle-cluttered environment. Our method uses a geometrical approach and a predefined safe perimeter (square area) based on Euclidean Geometry for the estimation of intercepting points, as a simple and efficient way to detect obstacles. The square region is treated as the restricted zone that the UAS must avoid entering, therefore providing a perimeter for manoeuvring and arriving at the next waypoints. The proposed algorithm is developed in a MATLAB environment and can be easily translated into other programming languages. The proposed algorithm is tested in scenarios with increasing levels of complexity, demonstrating that the QuickNav algorithm is able to successfully and efficiently generate a series of avoiding waypoints. Furthermore, QuickNav produces shorter distances as compared to those of the brute force method and is able to solve difficult obstacle avoidance problems in fractions of the time and distance required by the other methods. QuickNav can be used to improve the safety and efficiency of UAV missions and can be applied to the deployment of UAVs for surveillance, search and rescue, and delivery operations.

Efficient and Lightweight Automatic Wheat Counting Method with Observation-Centric SORT for Real-Time Unmanned Aerial Vehicle Surveillance

The number of wheat ears per unit area is crucial for assessing wheat yield, but automated wheat ear counting still faces significant challenges due to factors like lighting, orientation, and density variations. Departing from most static image analysis methodologies, this study introduces Wheat-FasterYOLO, an efficient real-time model designed to detect, track, and count wheat ears in video sequences. This model uses FasterNet as its foundational feature extraction network, significantly reducing the model’s parameter count and improving the model’s inference speed. We also incorporate deformable convolutions and dynamic sparse attention into the feature extraction network to enhance its ability to capture wheat ear features while reducing the effects of intricate environmental conditions. To address information loss during up-sampling and strengthen the model’s capacity to extract wheat ear features across varying feature map scales, we integrate a path aggregation network (PAN) with the content-aware reassembly of features (CARAFE) up-sampling operator. Furthermore, the incorporation of the Kalman filter-based target-tracking algorithm, Observation-centric SORT (OC-SORT), enables real-time tracking and counting of wheat ears within expansive field settings. Experimental results demonstrate that Wheat-FasterYOLO achieves a mean average precision (mAP) score of 94.01% with a small memory usage of 2.87MB, surpassing popular detectors such as YOLOX and YOLOv7-Tiny. With the integration of OC-SORT, the composite higher order tracking accuracy (HOTA) and counting accuracy reached 60.52% and 91.88%, respectively, while maintaining a frame rate of 92 frames per second (FPS). This technology has promising applications in wheat ear counting tasks.

Preliminary Approach for UAV-Based Multi-Sensor Platforms for Reconnaissance and Surveillance applications

Context: Unmanned Aerial Vehicles (UAVs) equipped with remote sensing platforms have become increasingly popular due to their applications in aerial surveillance, environmental control, and disaster response. However, the limited flight range and on-board energy resources of UAVs pose significant challenges to their practical deployment and operating efficiency, which has led to the exploration of energy-efficient platforms for remote sensing. Method: This paper proposes a preliminary approach for UAV multi-sensor reconnaissance and surveillance platforms (MRSS) that target low energy consumption. The approach implemented four sensor modules controlled by one multi-functional integrated edge computer for control and data collection, which can be interchanged according to battery lifetime requirements. Results: The main contribution of this work was an analysis of the energy consumption behavior of sensor modules managed by an embedded system with edge computing capabilities as the central control unit. Conclusions: The high energy consumption associated with modules such as GEOINT leads to deep discharge in excess of 20 % DOD, resulting in a maximum battery degradation of 2,4 years.

UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey

Unmanned aerial vehicles (UAVs) have gained significant popularity across various domains, but their proliferation also raises concerns about security, public safety, and privacy. Consequently, the detection and tracking of UAVs have become crucial. Among the UAV-monitoring technologies, those suitable for urban Internet-of-Things (IoT) environments primarily include radio frequency (RF), acoustic, and visual technologies. In this article, we provide a comprehensive review of passive UAV surveillance technologies, encompassing RF-based, acoustic-based, and vision-based methods for UAV detection, localization, and tracking. Our research reveals that certain lightweight UAV depth detection models have been effectively downsized for deployment on edge devices, facilitating the integration of edge computing and deep learning. In the city-wide anti-UAV, the integration of numerous urban infrastructure monitoring facilities presents a challenge in achieving a centralized computing center due to the large volume of data. To address this, calculations can be performed on edge devices, enabling faster UAV detection. Currently, there is a wide range of anti-UAV systems that have been deployed in both commercial and military sectors to address the challenges posed by UAVs. In this article, we provide an overview of the existing military and commercial anti-UAV systems. Furthermore, we propose several suggestions for developing general-purpose UAV-monitoring systems tailored for urban environments. These suggestions encompass considering the specific requirements of the application scenario, integrating detection and tracking mechanisms with appropriate countermeasures, designing for scalability and modularity, and leveraging advanced data analytics and machine learning techniques. To promote further research in the field of UAV-monitoring systems, we have compiled publicly available datasets comprising visual, acoustic, and radio frequency data. These datasets can be employed to evaluate the effectiveness of various UAV-monitoring techniques and algorithms. All of the datasets mentioned are linked in the text or in the references. Most of these datasets have been validated in multiple studies, and researchers can find more specific information in the corresponding papers or documents. By presenting this comprehensive overview and providing valuable insights, we aim to advance the development of UAV surveillance technologies, address the challenges posed by UAV proliferation, and foster innovation in the field of UAV monitoring and security.

Feasibility of Live Video Feed Transmission from Unmanned Aerial Vehicles for Medical Surveillance During the 2022 Montreal Marathon.

In recent years, unmanned aerial vehicles (UAVs) have been increasingly used for medical surveillance purposes in mass-gathering events. No studies have investigated the reliability of live video transmission from UAVs for accurate identification of distressed race participants in need of medical attention. The aim of this study was to determine the proportion of time during which live medical surveillance UAV video feed was successfully transmitted and considered of sufficient quality to identify acute illness in runners participating in the 2022 Montreal Marathon (Canada). Four UAVs equipped with high-resolution cameras were deployed at two pre-defined high-risk areas for medical incidents located within the last 500 meters of the race. The video footage was transmitted in real-time during four consecutive hours to a remote viewing station where four research assistants monitored it on large screens. Interruptions in live feed transmission and moments with inadequate field of view (FOV) on runners were documented. On September 25, 2022, a total of 6,916 athletes ran during the Montreal Marathon and Half Marathon. Out of the eight hours of video footage analyzed (four hours per high-risk area), 91.7% represented uninterrupted live video feed with an adequate view of the runners passing through the high-risk areas. There was a total of 18 live feed interruptions leading to a total interruption time of 22 minutes and 19 seconds (median interruption time of 32 seconds) and eight distinct moments with inadequate FOV on runners which accounted for 17 minutes and 33 seconds (median of 1 minute 47 seconds per moments with inadequate FOV). Active surveillance of drone-captured footage allowed early identification of two race participants in need of medical attention. Appropriate resources were dispatched, and UAV repositioning allowed for real-time viewing of the medical response. Live video transmission from UAVs for medical surveillance of runners passing through higher risk segments of a marathon for four consecutive hours is feasible. Live feed interruptions and moments with inadequate FOV could be minimized through practice and additional equipment redundancy.

Joint frame rate adaptation and object recognition model selection for stabilized unmanned aerial vehicle surveillance

AbstractWe propose an adaptive unmanned aerial vehicle (UAV)‐assisted object recognition algorithm for urban surveillance scenarios. For UAV‐assisted surveillance, UAVs are equipped with learning‐based object recognition models and can collect surveillance image data. However, owing to the limitations of UAVs regarding power and computational resources, adaptive control must be performed accordingly. Therefore, we introduce a self‐adaptive control strategy to maximize the time‐averaged recognition performance subject to stability through a formulation based on Lyapunov optimization. Results from performance evaluations on real‐world data demonstrate that the proposed algorithm achieves the desired performance improvements.

Differentiated Attention Guided Network Over Hierarchical and Aggregated Features for Intelligent UAV Surveillance

Intelligent unmanned aerial vehicle (UAV) surveillance based on infrared imaging has wide applications in the antiUAV system for protecting urban security and aerial safety. However, weak target features and complex background distraction pose great challenges for the accurate detection of UAVs. To address this issue, we propose a novel differentiated attention guided network to adaptively strengthen the discriminative features between UAV targets and complex background. First, a novel spatial-aware channel attention (SCA) is introduced into deep layers via preserving critical spatial features and leveraging channel interdependencies to focus on the large-scale targets. The channel-modulated deformable spatial attention is introduced into shallow layers via refining channel context and dynamically perceiving the spatial features for focusing on the small-scale targets. A combination of the above two attention mechanisms is employed in intermediate layers of the network for concentrating on the medium-scale targets. Then, we embed a feature aggregator at the detection branches to guide the information exchange of high-level feature maps and low-level feature maps with a bottom-up context modulation, and integrate an SCA at the end to further boost the distinctive feature representation for task-awareness. The above design can adaptively enhance multiscale UAV target features and suppress complex background interferences, leading to better detection performance, especially for small targets. Extensive experiments on real infrared UAV datasets reveal that the proposed method outperforms the baseline object detectors by a large margin, validating its feasibility in real-world infrared UAV detection. The source code can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/KALEIDOSCOPEIP/DAGNet</uri> .

Feasibility of Live Video Feed Transmission from UAVs for Medical Surveillance During the 2022 Montreal Marathon

Introduction:In recent years, unmanned aerial vehicles (UAVs) have been increasingly used for medical surveillance purposes in mass gathering events. No studies have investigated the reliability of live video transmission from UAVs for accurate identification of distressed race participants in need of medical attention. During the 2022 Montreal marathon, the aim of this study was to determine the proportion of data collection time during which live medical surveillance UAV video feed was successfully transmitted and considered of sufficient quality to identify acute illness.Method:Four UAVs equipped with high resolution cameras were deployed at two predefined high-risk areas for medical incidents located within the last 800m of the race. The video footage was transmitted in real-time during four consecutive hours to a remote viewing station where four research assistants monitored it on large screens. Interruptions in live feed transmission and moments with inadequate field of view on runners were documented.Results:On September 25, 2022, 8,577 athletes registered in the Montreal marathon and half marathon. Out of the eight hours of video footage analyzed (four hours per high-risk area), 91.7% represented uninterrupted live video feed with an adequate view of the runners passing through the high-risk areas. The total interruption time was 22 minutes and 19 seconds, and the field of view was considered inadequate for a total of 17 minutes and 33 seconds. Active surveillance of drone-captured footage allowed identification of two race participants in need of medical attention. Appropriate resources were dispatched, and UAV repositioning allowed for real-time viewing of the medical response.Conclusion:Live video transmission from UAVs for medical surveillance of runners passing through higher-risk segments of a marathon race for four consecutive hours is feasible. Live feed interruptions and segments with an inadequate field of view could be minimized through practice and additional equipment redundancy.

Railway intrusion detection based on refined spatial and temporal features for UAV surveillance scene

Railway intrusion detection is important for the safety of the railway operation. However, how to detect intruding events in the scene of unmanned aerial vehicle (UAV) surveillance is a challenging task, especially when there are multiple railway regions or unknown types of intruding objects. To tackle the above problems, a railway intrusion detection method is proposed for the scene of UAV surveillance. It can detect intruding events accurately without pre-setting the intruding object type by detecting the normal railway region and the abnormal railway region with intruding objects rather than recognizing the intruding objects directly. The proposed network consists of a novel Fused-convolutional long short-term memory (Fused-ConvLSTM) module for efficiently propagating spatial and temporal information; several attention modules for adaptively enhancing the feature information, such as the improved feature fusion layer; several lightweight strategies for reducing the computation complexity, such as lightweight feature extraction and width multiplier. Experiments are performed on the real aerial videos obtained by a micro UAV. The experimental results demonstrate that the proposed method can accurately detect railway intrusion events in aerial video, and effectively deal with the case of multiple railway regions and unknown intruding object types. The experimental results also demonstrate that the proposed network can achieve better performance than other object detection networks.

Managing the cyber-physical security for unmanned aerial vehicles used in perimeter surveillance

This paper discusses the vulnerabilities of perimeter surveillance unmanned aerial vehicles to cyber-physical security threats and discusses some approaches to manage them, as most cyber threats to the UAVs coming through their onboard wireless transceiver, we are suggesting an Antennas propagation type that limit the vector of the threat, also the importance of vulnerabilities scanner to evaluate the system risks. And addressing the limited energy and computation power resources onboard, a computation efficient onboard encryption method is proposed, and a sign cutting machine vision algorithm to provide warning of suspicious activity detected on interrupted surveillance imagery. The focus of this analysis is to manage the vulnerabilities of the system during both its operation time and its standby time, this is done by performing a checklist of computerized tests periodically and comparing two or more results to discover any unexpected changes. We showed that the onboard resources can be utilized more efficiently securing itself and the system from possible intrusion, however, some heavy calculation tasks still need to be performed on the ground control station which is causing some latency problem to such time sensitive operation. We have demonstrated that image registration techniques have produced useful results when applied to analyze the differences between two scene images.

Predictive analysis of smart agriculture using IoT-based UAV and propagation models of machine learning

Every year, unfavourable weather conditions cause many crops to fail. Every time, over 12 million dollar losses are recorded. This article provides a proper background for delivering the yield's current state. The project proposes to employ IoT-based unmanned aerial vehicles (UAVs) and tensor-flow machine learning to estimate crop yields. This framework enhances agricultural yield accuracy by using UAVs. The IoT-enabled UAV module captures data and texts it to the farmer or rancher. The data cloud storage's server uses MQTT for safe data transmission. The cloud server leverages UAV for continuous surveillance and harvest forecasts. Predictive analysis using propagation model has an accuracy of roughly 85% compared to real-time analysis for the same crops at the farm.