Unmanned Aircraft Systems Platforms Research Articles

Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 1: Measurements, processing, and accuracy assessment

Abstract. Snow conditions in the Northern Hemisphere are rapidly changing, and information on snow depth is critical for decision-making and other societal needs. Uncrewed or unmanned aircraft systems (UASs) can offer data resolutions of a few centimeters at a catchment-scale and thus provide a low-cost solution to bridge the gap between sparse manual probing and low-resolution satellite data. In this study, we present a series of snow depth measurements using different UAS platforms throughout the winter in the Finnish subarctic site Pallas, which has a heterogeneous landscape. We discuss the different platforms, the methods utilized, difficulties working in the harsh northern environment, and the UAS snow depth results compared to in situ measurements. Generally, all UASs produced spatially representative estimates of snow depth in open areas after reliable georeferencing by using the structure from motion (SfM) photogrammetry technique. However, significant differences were observed in the accuracies produced by the different UASs compared to manual snow depth measurements, with overall root mean square errors (RMSEs) varying between 13.0 and 25.2 cm, depending on the UAS. Additionally, a reduction in accuracy was observed when moving from an open mire area to forest-covered areas. We demonstrate the potential of low-cost UASs to efficiently map snow surface conditions, and we give some recommendations on UAS platform selection and operation in a harsh subarctic environment with variable canopy cover.

Unmanned Aircraft System Applications in Damage Detection and Service Life Prediction for Bridges: A Review

The increasing need for inexpensive, safe, highly efficient, and time-saving damage detection technology, combined with emerging technologies, has made damage detection by unmanned aircraft systems (UAS) an active research area. In the past, numerous sensors have been developed for damage detection, but these sensors have only recently been integrated with UAS. UAS damage detection specifically concerns data collection, path planning, multi-sensor fusion, system integration, damage quantification, and data processing in building a prediction model to predict the remaining service life. This review provides an overview of crucial scientific advances that marked the development of UAS inspection: underlying UAS platforms, peripherals, sensing equipment, data processing approaches, and service life prediction models. Example equipment includes a visual camera, a multispectral sensor, a hyperspectral sensor, a thermal infrared sensor, and light detection and ranging (LiDAR). This review also includes highlights of the remaining scientific challenges and development trends, including the critical need for self-navigated control, autonomic damage detection, and deterioration model building. Finally, we conclude with a brief discussion regarding the pros and cons of this emerging technology, along with a prospect of UAS technology research for damage detection.

Machine Learning with UAS LiDAR for Winter Wheat Biomass Estimations

Abstract. Biomass is an important indicator in the ecological and management process that can now be estimated at higher temporal and spatial resolutions because of unmanned aircraft systems (UAS). LiDAR sensor technology has advanced enabling more compact sizes that can be integrated with UAS platforms. Its signals are capable of penetrating through vegetation canopies enabling the capture of more information along the plant structure. Separate studies have used LiDAR for crop height, rate of canopy penetrations as related to leaf area index (LAI), and signal intensity as an indicator of plant chlorophyll status or green area index (GAI). These LiDAR products are combined within a machine learning method such as an artificial neural network (ANN) to assess the potential in making accurate biomass estimations for winter wheat.

Protecting farm privacy while researching large‐scale unmanned aircraft systems platforms for agricultural applications

AbstractThe objective of this paper is to explore data privacy and sharing challenges associated with conducting regional data collection using large‐scale unmanned aircraft systems (UAS) technologies. During the 2016 growing season, the North Dakota State University Extension Service and Elbit Systems of America conducted a proof‐of‐concept UAS Research and Development (R&D) project to use large‐scale UAS platforms to address some of the issues limiting UAS technology adoption. While the researchers initially thought that the high‐resolution spatial and temporal field imagery collected would be well received by area farmers, the team learned that some had concerns over farm operations privacy and about who would have access to the data. The team conducted multiple stakeholder meetings to address the issues raised. While the flights were successfully executed, problems related to farmer interest in the data collected and how to get that data to farmers were identified. Though the project demonstrated how third party operation of large‐scale UAS can remove the operational burden from farmers, the project introduced additional privacy concerns and highlighted the need for better rural broadband to make grower access to data in the cloud practical.

Fusion of Reflected GPS Signals With Multispectral Imagery to Estimate Soil Moisture at Subfield Scale From Small UAS Platforms

This study proposes a low-cost and &#x201C;proof-of-concept&#x201D; methodology to obtain high spatial resolution soil moisture (SM) via processing reflected Global Positioning System (GPS) and a multispectral camera data acquired by small Unmanned Aircraft System (UAS) platforms. An SM estimation model is developed using a random forest (RF) machine-learning (ML) algorithm by combining features obtained from reflected GPS signals (collected by smartphones and commercial off the shelf receivers) in conjunction with ancillary vegetation indices from the multispectral camera data. The proposed ML algorithm uses <i>in-situ</i> SM measurements acquired via SM probes as labels. A preliminary field experiment was conducted on 210&#x00A0;m by 110&#x00A0;m (2.31 ha) crop fields (corn and cotton) in 2020 (from January to November, including crop planting through senescence time period) at Mississippi State University (MSU)&#x0027;s the heavily instrumented North Farm to acquire data needed for the ML model to train and test. Our results showed that both fields could be covered by GPS reflectometry measurements with about 13 minutes of flight time at a 15-m altitude, and SM can be mapped with 5m&#x00A0;&#x00D7;&#x00A0;5m spatial resolution (corresponding to the elongated first Fresnel zone). The model is trained with and validated against eight <i>in-situ</i> SM station datasets via 10-fold and leave-one-probe-out cross-validation techniques. Overall root-mean-square errors (RMSE) of 0.013 m <inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> m<inline-formula><tex-math notation="LaTeX">$^{-3}$</tex-math></inline-formula> volumetric SM and R-value of 0.95 [-] are obtained for 10-fold cross-validation. The proposed model reached an RMSE of 0.033 m <inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> m<inline-formula><tex-math notation="LaTeX">$^{-3}$</tex-math></inline-formula> and an R-value of 0.5 [-] in leave-one-probe-out cross-validation. While having limited data, the results indicate that high resolution SM measurement can be achieved with a low-cost GPS reflectometry system onboard a small UAS platform for use in precision agriculture (PA) applications.

Integrating Unmanned Aircraft Systems into State Department of Transportation Highway Bridge Inspection Procedures: Challenges, Implications, and Lessons Learned

Rapid proliferation of small, unmanned aircraft systems (UAS) promises to revolutionize traditional methods used to carry out civil engineering surveys and analyses and conduct physical infrastructure inspections. One of the most promising areas of implementation of innovative UAS technology includes the integration of UAS into current state Department of Transportation (DOT) bridge inspections. While regular bridge inspections are paramount for road user safety, many traditional inspection methods and procedures are cumbersome, expensive, and time consuming; present significant hazards to both the traveling public and the inspection personnel; and are disruptive to normal operations of the transportation facilities. The results of recent studies indicate that UAS can serve as a useful tool in many highway bridge inspection procedures, while significantly reducing costs and time and improving safety. The major factors that affect the success of integrating UAS into the bridge inspection process relate to selection of the proper types of UAS platforms and avionics, data collection sensors and processing software, as well as conduct of task-specific pilot training. The paper provides an examination of current standard bridge inspection procedures and protocols currently carried out by state DOTs; an evaluation of state DOT experiences with the integration of UAS technology into bridge inspections; and an assessment of the issues and challenges associated with this technology. It is expected that this paper will be of interest to a wide range of stakeholders representing state and federal governments, academia, and industry.

Unmanned Aircraft Systems Performance in a Climate-Controlled Laboratory

Despite many research studies focus on strategies to improve autopilot capabilities and bring artificial intelligence onboard Unmanned Aircraft Systems (UAS), there are still few experimental activities related to these vehicle performance under unconventional weather conditions. Air temperature and altitudes directly affect thrust and power coefficients of small scale propeller for UAS applications. Reynolds numbers are usually within the range 10,000 to 100,000 and important aerodynamic effects, such as the laminar separation bubbles, occur with a negative impact on propulsion performance. The development of autonomous UAS platforms to reduce pilot work-load and allow Beyond Visual Line of Sight (BVLOS) operations requires experimental data to validate capabilities of these innovative vehicles. High quality data are needed for a deep understanding of limitations and opportunities of UAS under unconventional flight conditions. The primary objective of this article is to present the characterization of a propeller and a quadrotor capabilities in a pressure-climate-controlled chamber. Mechanical and electrical data are measured with a dedicated test setup over a wide range of temperatures and altitudes. Test results are presented in terms of thrust and power coefficient trends. The experimental data shows low Reynolds numbers are responsible for degraded thrust performance. Moreover, details on brushless motor capabilities are also discussed considering different temperature and pressure conditions. The experimental data collected in the test campaign will be leveraged to improve UAS design, propulsion system modelling as well as to provide guidelines for safe UAS operations in extreme environments.

Viability and Application of Mounting Personal PID VOC Sensors to Small Unmanned Aircraft Systems

Using a UAS-mounted sensor to allow for a rapid response to areas in which it may be difficult to reach or may not be safe for humans to encounter can increase the situational awareness of first responders of an aircraft crash site through the remote detection, identification, and quantification of airborne hazardous materials. The primary purpose of this research is to evaluate the innovative remote sensing viability and application of integrating existing commercial-off-the-shelf (COTS) sensors with small unmanned aircraft system (UAS) technology to detect potentially hazardous airborne contaminants at optimum range resolutions using UAS platforms.

Framework for UAS-Integrated Airport Runway Design Code Compliance Using Incremental Mosaic Imagery

Airport inspections are essential to ensuring the safety of aircraft operation for the general public. The current, manual, approach to inspection consumes a significant amount of time. Unmanned aircraft systems (UASs) are often employed to improve the efficiency of visual inspections of various infrastructure facilities. However, only a few studies have documented their use in the context of airport inspections. This study proposes a framework for inspecting runway design codes (RDCs) for airfields that relies on mosaic imagery. Scale-invariant feature transform and best bin first algorithms were integrated to generate accurate UAS-based mosaic imagery for airports. A fixed-wing UAS platform was deployed to capture aerial images of an airport testbed provided by the DOT. The validation results showed that the framework had a high enough level of accuracy to measure pixel-based distances for runway design code (RDC) items that were comparable to the results of manual airport inspections. Across all RDC items in this study, an average error rate of 4.664% between the two types of inspection was documented. The lowest level of error (1.520%) was recorded for the distance between the runway centerline and aircraft parking area, and the highest (11.200%) corresponded to taxiway width. This study contributes to a better understanding of UAS-based airport inspection applications and strengthens and broadens the utility of UASs in visual inspections of civil infrastructure systems.

A Robust Control Strategy With Perturbation Estimation for the Parrot Mambo Platform

This article addresses theoretical and practical challenges associated with a commercially available and ready-to-fly small-scale unmanned aircraft system (UAS) developed by Parrot SA: the Mambo quad rotorcraft. The dynamic model and the structure of the controller running onboard the UAS autopilot are not disclosed by its manufacturers. For this reason, a novel robust controller for discrete-time systems under time delays and input saturation is first developed for this platform. Then, three fundamental estimation and control challenges are addressed. The first challenge is the system identification of the X and Y translational dynamics of the UAS. To accomplish this goal, input-output data pairs are collected from different UAS platforms during real-time experimental flights. A group of dynamic models are identified from the data pairs through an extended least-squares algorithm. The obtained models are similar in nature but exhibit parametrical variations due to uncertainties in the fabrication process and different levels of wear and tear. Using a time-varying modeling approach, the second challenge addresses the development of a robust controller, which guarantees the stability of all the identified dynamic models. The third challenge addresses the development of a nonlinear controller enhanced with a perturbation estimation, which can reject, from the nominal model, the effects of model uncertainties and perturbations. These theoretical developments are presented in the form of two original theorems. The proposed strategies are ultimately validated in a set of real-time experiments, demonstrating their effectiveness and applicability.

Exploratory study of user-perceived effectiveness of unmanned aircraft system (UAS) integration in visual inspections of transportation agency

This research assesses the effectiveness of unmanned aircraft systems (UAS) and provides further implications and recommendations for UAS-based visual inspections in the construction and infrastructure environment. This study includes field tests at infrastructure projects provided by Georgia DOT, as well as semi-structured interviews and survey questionnaires after the field tests. A total of three field test locations were selected, including an airport, bridge, and road. During the field tests, eight UAS platforms were used to collect various data, including still images, infrared images, and video. Interviews with and a survey of industry professionals participating in the field tests were employed to measure the effectiveness of UAS integration into their construction-related task environments. The results of this study will assist in implementing UAS in infrastructure construction and operations. Since only a small number of potential UAS users from the field tests and post-test interviews, this study qualitatively describes further implications based on users’ perceptions, prompting the integration of UAS into the domain of infrastructure construction and operation.

UAS-Based Archaeological Remote Sensing: Review, Meta-Analysis and State-of-the-Art

Over the last decade, we have witnessed momentous technological developments in unmanned aircraft systems (UAS) and in lightweight sensors operating at various wavelengths, at and beyond the visible spectrum, which can be integrated with unmanned aerial platforms. These innovations have made feasible close-range and high-resolution remote sensing for numerous archaeological applications, including documentation, prospection, and monitoring bridging the gap between satellite, high-altitude airborne, and terrestrial sensing of historical sites and landscapes. In this article, we track the progress made so far, by systematically reviewing the literature relevant to the combined use of UAS platforms with visible, infrared, multi-spectral, hyper-spectral, laser, and radar sensors to reveal archaeological features otherwise invisible to archaeologists with applied non-destructive techniques. We review, specific applications and their global distribution, as well as commonly used platforms, sensors, and data-processing workflows. Furthermore, we identify the contemporary state-of-the-art and discuss the challenges that have already been overcome, and those that have not, to propose suggestions for future research.

Meteorological Profiling in the Fire Environment Using UAS

With the increase in commercially available small unmanned aircraft systems (UAS), new observations in extreme environments are becoming more obtainable. One such application is the fire environment, wherein measuring both fire and atmospheric properties are challenging. The Fire and Smoke Model Evaluation Experiment offered the unique opportunity of a large controlled wildfire, which allowed measurements that cannot generally be taken during an active wildfire. Fire–atmosphere interactions have typically been measured from stationary instrumented towers and by remote sensing systems such as lidar. Advances in UAS and compact meteorological instrumentation have allowed for small moving weather stations that can move with the fire front while sampling. This study highlights the use of DJI Matrice 200, which was equipped with a TriSonica Mini Wind and Weather station sonic anemometer weather station in order to sample the fire environment in an experimental and controlled setting. The weather station was mounted on to a carbon fiber pole extending off the side of the platform. The system was tested against an RM-Young 81,000 sonic anemometer, mounted at 6 and 2 m above ground levelto assess any bias in the UAS platform. Preliminary data show that this system can be useful for taking vertical profiles of atmospheric variables, in addition to being used in place of meteorological tower measurements when suitable.

Flight Planning for LiDAR-Based UAS Mapping Applications

In the last two decades, unmanned aircraft systems (UAS) were successfully used in different environments for diverse applications like territorial mapping, heritage 3D documentation, as built surveys, construction monitoring, solar panel placement and assessment, road inspections, etc. These applications were correlated to the onboard sensors like RGB cameras, multi-spectral cameras, thermal sensors, panoramic cameras, or LiDARs. According to the different onboard sensors, a different mission plan is required to satisfy the characteristics of the sensor and the project aims. For UAS LiDAR-based mapping missions, requirements for the flight planning are different with respect to conventional UAS image-based flight plans because of different reasons related to the LiDAR scanning mechanism, scanning range, output scanning rate, field of view (FOV), rotation speed, etc. Although flight planning for image-based UAS missions is a well-known and solved problem, flight planning for a LiDAR-based UAS mapping is still an open research topic that needs further investigations. The article presents the developments of a LiDAR-based UAS flight planning tool, tested with simulations in real scenarios. The flight planning simulations considered an UAS platform equipped, alternatively, with three low-cost multi-beam LiDARs, namely Quanergy M8, Velodyne VLP-16, and the Ouster OS-1-16. The specific characteristics of the three sensors were used to plan flights and acquired dense point clouds. Comparisons and analyses of the results showed clear relationships between point density, flying speeds, and flying heights.

Logistical and technical considerations for the use of unmanned aircraft systems in coastal habitat monitoring: A case study in high-resolution subaquatic vegetation assessment

In recent years, the technology and regulation surrounding the use of unmanned aircraft systems (UASs) has rapidly advanced. This has resulted in the availability of such technology for more common applications. Here we compare manned versus UAS platforms for acquiring high-resolution imagery of subaquatic habitat for the purpose of boat propeller scar delineation in seagrass meadows in Redfish Bay, Texas. We acquired aerial seagrass imagery in three 20-hectare plots using two UASs and one manned aircraft platform. The three plots represented a priori designations of low, moderate, and high seagrass scarring intensity. Overall, we observed that a smaller amount of scarring was detected in the manned aircraft imagery compared to that collected by the two UAS platforms, and that this disparity was much greater for the high scarring intensity plot. The observed differences in scar feature delineations were at least partially related to logistical difference between these two platforms — specifically, the lower altitude flown by the UASs results in a higher spatial resolution of the imagery that is less dependent on the camera specifications. From a logistical standpoint, the potential gain in spatial resolution via lower altitude flight could result in a reduced pricetag for high-resolution mapped products. Further, the rapid deployment and local operation typically resulting from the accessibility of UAS training greatly simplify the logistics of planning imagery acquisition at the appropriate scale. However, we realize that the current trade-off with regard to higher altitude is the ability to cover large areas with fewer transects and shorter flight time. Coverage limitations for UASs is currently rooted in both technological and legal issues. However, as technology and regulations evolve, the technical and logistical comparison of imagery products from UAS and manned platforms will become increasingly important to natural resource managers and researchers looking to make this transition to UAS.

A methodology for multirotor aircraft power budget analysis

Purpose The primary purpose of this study is to analyse the performance of multirotor unmanned aircraft system platforms for passenger transport and compare them with an ordinary helicopter solution. This study aims to define a standard procedure for power budget analysis of unconventional vehicles recently proposed in the aerospace industry, providing guidelines on rotor sizing in terms of required power and the total number of rotors. The ultimate purpose of the proposed work is to describe a methodology for power estimation with regard to emerging electric vertical takeoff and landing (EVTOL) vehicles. Design/methodology/approach In the context of urban mobility, short-range passenger transport between critical hubs in cities is taken into account and innovative aircraft and traditional helicopters are compared according to a common mission profile. The power budget equations used in the helicopter literature are revisited to consider different multirotor configurations (up to 20 rotors) and evaluate the feasibility of innovative aerospace vehicle design. Findings The paper includes insights into the maximum number of rotors that ensure a significative, relative power reduction compared to helicopter platforms (the power-to-cruise over power-to-hover ratio appears to be improved). Based on this preliminary analysis, the results suggest the benefit of reducing the installed rotors to avoid excessive power loss in forward flight. Practical implications The proposed study provides guidelines for further design considerations and the future development of EVTOL multirotor aircraft. Originality/value This paper fulfils the identified need for a systematic approach on performance analysis for innovative vehicles involved in commercial applications.

Footprint Determination of a Spectroradiometer Mounted on an Unmanned Aircraft System

Unmanned aircraft system (UAS)-mounted spectroradiometers offer a new capability to measure spectral reflectance and solar-induced chlorophyll fluorescence at detailed canopy scales. This capability offers potential for upscaling and comparison with airborne and space-borne observations [e.g., the upcoming European Space Agency (ESA) Fluorescence Explorer (FLEX) satellite mission]. In this respect, the accurate spatial characterization and georeferencing of the UAS acquisition footprints are essential to unravel the origin and spatial variability of optical signals acquired within the extent of airborne/satellite pixels. In this article, we present and validate a novel algorithm to georeference the footprint extent of a nonimaging spectroradiometer mounted on a multirotor UAS platform. We used information about the spectroradiometer position and orientation during flight and about topography of observed terrain to calculate the footprint geolocation. In a recursive process, the field of view (FOV) of the spectroradiometer projected on the ground. Multiple FOV ground projections retrieved during a spectroradiometer reading (i.e., a single integration time) were aggregated to calculate the footprint extent. The spatial accuracy of the footprint geolocation was validated by applying the georeferencing algorithm on checkpoint pixels of image acquired with a comounted digital camera. Geolocations of the checkpoint pixels, which served as a proxy for the spectroradiometer footprint, were successfully compared with surveyed ground checkpoints. Finally, the spectral and radiometric quality of UAS-acquired reflectance signatures was compared with ground-measured reflectance of four natural targets (three different types of grass and a bare soil), and a strong agreement was observed.

Robustness Analysis of Flight Controllers for Fixed-Wing Unmanned Aircraft Systems Using Integral Quadratic Constraints

A robust analysis framework for mathematical validation of flight control systems is presented. The framework is specifically developed for the complete uncertainty characterization, quantification, and analysis of small fixed-wing unmanned aircraft systems (UAS). The analytical approach presented within is based on integral quadratic constraint (IQC) analysis and uses linear fractional transformations on uncertainties to represent system models. While IQC analysis has a sound theoretical foundation, there remains a lack of literature on applying IQCs to complex engineering systems and evaluating their effectiveness with physical data. One difficulty lies in appropriately characterizing and quantifying the uncertainties such that the resulting uncertain model is representative of the physical system while forming a computationally tractable problem that is not overly conservative. This paper addresses these challenges by applying IQC analysis tools to analyze the robustness of a UAS flight control system. Uncertainties are characterized and quantified based on mathematical models and flight test data obtained in-house for a small commercial off-the-shelf platform with a custom autopilot. Furthermore, an established gradient-based minimization routine is implemented with IQC analysis to demonstrate how IQC analysis can guide the control design process. This approach also reveals the controlled system’s sensitivities to uncertainties, thereby assisting the designer in determining how much uncertainty allowance ought to exist in certain aspects of the UAS. Finally, these methods are tested in physical flight to showcase the effectiveness of IQC analysis and assess the conservativeness of the approach. The proposed framework is also transferable to other fixed-wing UAS platforms, effectively taking IQC analysis beyond academic examples to practical application in UAS system design, control design, and airworthiness certification.

Assessing the Geospatial Accuracy of Aerial Imagery Collected with Various UAS Platforms

Abstract. Recent development of small unmanned aircraft systems (UAS) provides a relatively low-cost solution to collect aerial imagery with very high spatial and temporal resolutions. The geospatial accuracy of collected data can range from a few centimeters to several meters, and the use of ground control points (GCPs) is recommended to correct for large geospatial errors. However, whether or not GCPs are used, the true geospatial accuracy of collected UAS data remains unknown. The objective of this study was to measure and compare the geospatial accuracy of images obtained with various UAS platforms at two flight altitudes. Aerial imagery was collected using four platforms equipped with different RGB cameras: Phantom 4, eBee Ag, eBee Plus, and Trimble UX5. All platforms were equipped with manufacturer GPS receivers, and RTK was activated on the eBee Plus. Each platform was flown at 75 and 120 m altitudes, and the experiment was replicated three times. Results demonstrated that using GCPs during data processing improved the horizontal and vertical accuracies of the Phantom 4, eBee Ag, and Trimble UX, decreased the between-flight variability, and accounted for the negative effect of flight altitude. On the other hand, the RTK technology used with the eBee Plus resulted in images with very high geospatial accuracy with or without GCPs. Using GCPs during data processing or RTK technology at the time of flight provided aerial imagery with horizontal accuracies of 1.5 to 10 cm and vertical accuracies of 0.0 to 0.4 m. These results are within an acceptable range for data utilization, unlike the horizontal and vertical accuracies obtained without GCPs or RTK, which ranged from 32 to 441 cm and from 1 to 126 m, respectively. Results from this study quantify the geospatial accuracy of UAS imagery and provide a better understanding of the relationships between the accuracy of the GPS receivers in UAS, flight altitude, and horizontal and vertical accuracies of collected images. Keywords: Accuracy, Drone, Ground control points, Precision agriculture, UAV.

What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?

ABSTRACTRemote sensing from unmanned aircraft systems (UAS) was expected to be an important new technology to assist farmers with precision agriculture, especially crop nutrient management. There are three advantages using UAS platforms compared to manned aircraft platforms with the same sensor for precision agriculture: (1) smaller ground sample distances, (2) incident light sensors for image calibration, and (3) canopy height models created from structure-from-motion point clouds. These developments hold promise for future data products. In order to better match vendor capabilities with farmer requirements, we classify applications into three general niches: (1) scouting for problems, (2) monitoring to prevent yield losses, and (3) planning crop management operations. The three different niches have different requirements for sensor calibration and have different costs of operation. Planning crop management operations may have the most environmental and economic benefits. However, a USDA Economic Research Report showed that only about 20% of farmers in the USA have adopted variable rate applicators; so, most farmers in the USA may not have the technology to benefit from management plans. In the near-term, monitoring to prevent yield losses from weeds, insects, and diseases may provide the most economic and environmental benefits, but the costs for data acquisition need to be reduced.