UAS, sensors, and data processing in agroforestry: a review towards practical applications

Pádua, Luís; Vanko, Jakub; Hruška, Jonáš; Adão, Telmo; Sousa, Joaquim J.; Peres, Emanuel; Morais, Raul

doi:10.1080/01431161.2017.1297548

Cited by 279 publications

(243 citation statements)

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“…In recent years, numerous studies have reviewed the application of UAVs to a variety of fields, including ecology and plant ecophysiology (Gago et al., 2015; Garrett & Anderson, 2018; Pádua et al., 2017; Sankaran et al., 2015). In this article, we build on these previous efforts by reviewing the emergence of UAVs in the scientific literature and their agricultural applications, focusing particularly on the most recent advances and the obstacles that have prevented use of these technologies by farm managers and other non‐specialists.…”

Section: The Rise Of Unmanned Aerial Vehiclesmentioning

confidence: 99%

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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Section: The Rise Of Unmanned Aerial Vehiclesmentioning

confidence: 99%

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

et al. 2020

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“…Remote sensing relying on unmanned aircraft systems (UAS), although an emerging field of application, has been systematically applied for monitoring vegetation and environmental parameters aiming the optimization of agroforestry activities [1]. In this context, UAS have become suitable to assess crops' conditions by gathering huge amounts of raw data that require further processing to enable a wide range of applications, such as water status [2], vigor assessment [3], biomass estimation [4] and disease monitoring [5].…”

Section: Introductionmentioning

confidence: 99%

“…Together with specialized sensors, UAS are becoming powerful sensing systems [35] that complement the traditional sensing techniques rather than competing with them [1]. According to Pádua et al [1], a UAS can be defined as a power-driven and reusable aircraft, operated without a human pilot on board [36]. Usually, it is composed of a UAV that, in turn, is capable of carrying remote sensing devices.…”

Section: Introductionmentioning

confidence: 99%

“…Lately, a remote sensing platform capable of overcoming not only satellite but also manned aircraft issues by bringing enhanced spectral and spatial resolutions, operational flexibility and affordability to the users is emerging: the UAS [34]. Together with specialized sensors, UAS are becoming powerful sensing systems [35] that complement the traditional sensing techniques rather than competing with them [1]. According to Pádua et al [1], a UAS can be defined as a power-driven and reusable aircraft, operated without a human pilot on board [36].…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

et al. 2017

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Traditional imagery-provided, for example, by RGB and/or NIR sensors-has proven to be useful in many agroforestry applications. However, it lacks the spectral range and precision to profile materials and organisms that only hyperspectral sensors can provide. This kind of high-resolution spectroscopy was firstly used in satellites and later in manned aircraft, which are significantly expensive platforms and extremely restrictive due to availability limitations and/or complex logistics. More recently, UAS have emerged as a very popular and cost-effective remote sensing technology, composed of aerial platforms capable of carrying small-sized and lightweight sensors. Meanwhile, hyperspectral technology developments have been consistently resulting in smaller and lighter sensors that can currently be integrated in UAS for either scientific or commercial purposes. The hyperspectral sensors' ability for measuring hundreds of bands raises complexity when considering the sheer quantity of acquired data, whose usefulness depends on both calibration and corrective tasks occurring in pre-and post-flight stages. Further steps regarding hyperspectral data processing must be performed towards the retrieval of relevant information, which provides the true benefits for assertive interventions in agricultural crops and forested areas. Considering the aforementioned topics and the goal of providing a global view focused on hyperspectral-based remote sensing supported by UAV platforms, a survey including hyperspectral sensors, inherent data processing and applications focusing both on agriculture and forestry-wherein the combination of UAV and hyperspectral sensors plays a center role-is presented in this paper. Firstly, the advantages of hyperspectral data over RGB imagery and multispectral data are highlighted. Then, hyperspectral acquisition devices are addressed, including sensor types, acquisition modes and UAV-compatible sensors that can be used for both research and commercial purposes. Pre-flight operations and post-flight pre-processing are pointed out as necessary to ensure the usefulness of hyperspectral data for further processing towards the retrieval of conclusive information. With the goal of simplifying hyperspectral data processing-by isolating the common user from the processes' mathematical complexity-several available toolboxes that allow a direct access to level-one hyperspectral data are presented. Moreover, research works focusing the symbiosis between UAV-hyperspectral for agriculture and forestry applications are reviewed, just before the paper's conclusions.

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“…Currently, images acquired with Unmanned Aerial Vehicles (UAV) are seen as a good alternative data sources to obtain spatial data that represent the terrain surface with a high level of detail [15,17]. The UAV offers a fast and accurate way to acquire aerial images at a relatively low cost with resolution and accuracy typically in the range of a few centimetres [15,17]. Compared to traditional airborne remote sensing, the UAV enable rapid operation to obtain high frequency multi-temporal and high resolution images [15].…”

Section: Introductionmentioning

confidence: 99%

Improving a DSM Obtained by Unmanned Aerial Vehicles for Flood Modelling

Mourato

Fernandez

Pereira

et al. 2017

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract. According to the EU flood risks directive, flood hazard map must be used to assess the flood risk. These maps can be developed with hydraulic modelling tools using a Digital Surface Runoff Model (DSRM). During the last decade, important evolutions of the spatial data processing has been developed which will certainly improve the hydraulic models results. Currently, images acquired with Red/Green/Blue (RGB) camera transported by Unmanned Aerial Vehicles (UAV) are seen as a good alternative data sources to represent the terrain surface with a high level of resolution and precision. The question is if the digital surface model obtain with this data is adequate enough for a good representation of the hydraulics flood characteristics. For this purpose, the hydraulic model HEC-RAS was run with 4 different DSRM for an 8.5 km reach of the Lis River in Portugal. The computational performance of the 4 modelling implementations is evaluated. Two hydrometric stations water level records were used as boundary conditions of the hydraulic model. The records from a third hydrometric station were used to validate the optimal DSRM. The HEC-RAS results had the best performance during the validation step were the ones where the DSRM with integration of the two altimetry data sources.

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UAS, sensors, and data processing in agroforestry: a review towards practical applications

Cited by 279 publications

References 99 publications

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

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

Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

Improving a DSM Obtained by Unmanned Aerial Vehicles for Flood Modelling

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