Agricultural UAVs in the U.S.: potential, policy, and hype

Freeman, Patricia K.; Freeland, R. S.

doi:10.1016/j.rsase.2015.10.002

Cited by 55 publications

(56 citation statements)

References 18 publications

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“…Thus, UAVs are becoming more extensively applied to the monitoring of crop growth [23]. By utilizing a miniature hyperspectral infrared thermograph on a UAV, Zarco-Tejada et al [24] obtained hyperspectral image information on a citrus canopy of large area.…”

Section: Introductionmentioning

confidence: 99%

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

Yao

JingChao

et al. 2017

Sensors

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In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online acquisition of various major indexes, e.g., the normalized difference vegetation index (NDVI) of the crop canopy, ratio vegetation index (RVI), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW). By carrying out three-dimensional numerical simulations based on computational fluid dynamics, spatial distributions were obtained for the UAV down-wash flow fields on the surface of the crop canopy. Based on the flow-field characteristics and geometrical dimensions, a UAV-borne crop-growth sensor was designed. Our field experiments show that the monitoring system has good dynamic stability and measurement accuracy over the range of operating altitudes of the sensor. The linear fitting determination coefficients (R2) for the output RVI value with respect to LNA, LAI, and LDW are 0.63, 0.69, and 0.66, respectively, and the Root-mean-square errors (RMSEs) are 1.42, 1.02 and 3.09, respectively. The equivalent figures for the output NDVI value are 0.60, 0.65, and 0.62 (LNA, LAI, and LDW, respectively) and the RMSEs are 1.44, 1.01 and 3.01, respectively.

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Section: Introductionmentioning

confidence: 99%

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

Yao

JingChao

et al. 2017

Sensors

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“…A wide variety of UAV platforms, including multi‐copter and fixed‐wing types, that can carry a range of sensors (RGB, multi‐spectral, hyperspectral, or thermal cameras) are available, and the data that they collect can be analyzed to provide important information on crop status and health (Berni, Zarco‐Tejada, Sepulcre‐Cantó, Fereres, & Villalobos, 2009; Díaz‐Varela, de la Rosa, León, & Zarco‐Tejada, 2015; Gago et al., 2017; Lelong et al., 2008; Zarco‐Tejada, González‐Dugo, & Berni, 2012). However, these potential benefits may not be fully realized due to public concerns about the threats to safety and privacy posed by UAVs (Freeman & Freeland, 2015). Another set of barriers to uptake of UAVs in agricultural management are the methods and expertise that are required to derive crop‐relevant information from UAV‐collected data.…”

Section: The Rise Of Unmanned Aerial Vehiclesmentioning

confidence: 99%

“…Another set of barriers to uptake of UAVs in agricultural management are the methods and expertise that are required to derive crop‐relevant information from UAV‐collected data. These challenges include complex aerial image processing and calibration requirements and the complexity of interpreting the resulting biophysical data (Berni et al., 2009; Freeman & Freeland, 2015; Gago et al., 2015; Zarco‐Tejada et al., 2012). These combined factors represent significant hurdles that serve to limit practical use of UAVs to researchers and specialized companies, excluding lay users.…”

Section: The Rise Of Unmanned Aerial Vehiclesmentioning

confidence: 99%

“…These combined factors represent significant hurdles that serve to limit practical use of UAVs to researchers and specialized companies, excluding lay users. Freeman & Freeland (2015) suggest that the UAV industry in the USA overhyped the agricultural uses of UAVs, most likely in an effort to overcome negative social and political perceptions and to facilitate funding support. However, these same authors envisage that within the near future, further developments will make it possible for there to be a genuine increase in farm‐based use of UAVs.…”

Section: The Rise Of Unmanned Aerial Vehiclesmentioning

confidence: 99%

See 1 more Smart Citation

Nano and Micro Unmanned Aerial Vehicles (UAVs): A New Grand Challenge for Precision Agriculture?

et al. 2020

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By collecting data at spatial and temporal scales that are inaccessible to satellite and field observation, unmanned aerial vehicles (UAVs) are revolutionizing a number of scientific and management disciplines. UAVs may be particularly valuable for precision agricultural applications, offering strong potential to improve the efficiency of water, nutrient, and disease management. However, some authors have suggested that the UAV industry has overhyped the potential value of this technology for agriculture, given that it is difficult for non‐specialists to operate UAVs as well as to process and interpret the resulting data. Here, we analyze the barriers to applying UAVs for precision agriculture, which range from regulatory issues to technical requirements. We then evaluate how new developments in the nano‐ and micro‐UAV (NAV and MAV, respectively) markets may help to overcome these barriers. Among the possible breakthroughs that we identify is the ability of NAV/MAV platforms to directly quantify plant traits using methods (e.g., object‐oriented classification) that require less image calibration and interpretation than spectral index–based approaches. We suggest that this potential, when combined with steady improvements in sensor miniaturization, flight precision, and autonomy as well as cloud‐based image processing, will make UAVs a tool with much broader adoption by agricultural managers in the near future. If this wider uptake is realized, then UAVs have real potential to improve agriculture's resource‐use efficiency. © 2020 by John Wiley & Sons, Inc.

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“…, n C . Therefore, the collected motion profiles of each cluster ({Θ i : i ∈ cluster c}) is used to refine the relevant clusters hyper parameters Ψ c by applying maximum likelihood estimation (MLE) to (5).…”

Section: Joint Mobility Prediction and Profilingmentioning

confidence: 99%

A unified framework for joint mobility prediction and object profiling of drones in UAV networks

et al. 2018

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In recent years, using a network of autonomous and cooperative unmanned aerial vehicles (UAVs) without command and communication from the ground station has become more imperative, in particular in search-and-rescue operations, disaster management, and other applications where human intervention is limited. In such scenarios, UAVs can make more efficient decisions if they acquire more information about the mobility, sensing and actuation capabilities of their neighbor nodes. In this paper, we develop an unsupervised online learning algorithm for joint mobility prediction and object profiling of UAVs to facilitate control and communication protocols. The proposed method not only predicts the future locations of the surrounding flying objects, but also classifies them into different groups with similar levels of maneuverability (e.g. rotatory, and fixed-wing UAVs) without prior knowledge about these classes. This method is flexible in admitting new object types with unknown mobility profiles, thereby applicable to emerging flying Ad-hoc networks with heterogeneous nodes.

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Agricultural UAVs in the U.S.: potential, policy, and hype

Cited by 55 publications

References 18 publications

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

Nano and Micro Unmanned Aerial Vehicles (UAVs): A New Grand Challenge for Precision Agriculture?

A unified framework for joint mobility prediction and object profiling of drones in UAV networks

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