A flexible unmanned aerial vehicle for precision agriculture

Primicerio, Jacopo; Gennaro, Salvatore Filippo Di; Fiorillo, Edoardo; Genesio, L.; Lugato, Emanuele; Matese, Alessandro; Vaccari, Francesco Primo

doi:10.1007/s11119-012-9257-6

Cited by 282 publications

(136 citation statements)

References 14 publications

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“…NDVI surveys performed with UAS, aircraft, and satellite demonstrate that low-resolution images generally fail in representing intrafield variability and patterns in fields characterized by small vegetation gradients and high vegetation patchiness [7]. Moreover, UAS-derived NDVIs have shown better agreement with ground-based NDVI observations compared to satellite-derived NDVIs in several crop and natural vegetation types [73][74][75]. As an example of the achievable resolution that can be obtained from UAVs, relative to some available high-resolution commercial satellite sensors, Figure 3 shows a multi-sensor sequence of imagery collected over a date palm plantation in Saudi Arabia.…”

Section: Vegetation Monitoring and Precision Agriculturementioning

confidence: 99%

On the Use of Unmanned Aerial Systems for Environmental Monitoring

et al. 2018

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Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small-and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air-and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challenges.

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Section: Vegetation Monitoring and Precision Agriculturementioning

confidence: 99%

On the Use of Unmanned Aerial Systems for Environmental Monitoring

et al. 2018

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“…According to Knipling (1970), crop reflectivity response in the visible spectrum depends on leaf chlorophyll content and information from the visible bands thus enables a distinction between green and yellow crops. However, vegetation indices based on spectrally narrow bands collected with advanced sensors in the visible and near-infrared spectra are most commonly used when crop conditions are assessed (Baluja et al, 2012;Garcia-Ruiz et al, 2013;Lelong et al, 2008;Primicerio et al, 2012;Sugiura et al, 2005;Zarco-Tejada et al, 2012). Rasmussen et al (2016) concludes that there is no difference in the ability to detect spectral crop response between consumer-grade RGB cameras (broad bands) and multispectral sensors providing narrow-band information in visible and near-infrared spectra.…”

Section: Ngrdi Mapsmentioning

confidence: 99%

Crop water stress maps for an entire growing season from visible and thermal UAV imagery

et al. 2016

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Abstract. This study investigates whether a water deficit index (WDI) based on imagery from unmanned aerial vehicles (UAVs) can provide accurate crop water stress maps at different growth stages of barley and in differing weather situations. Data from both the early and late growing season are included to investigate whether the WDI has the unique potential to be applicable both when the land surface is partly composed of bare soil and when crops on the land surface are senescing. The WDI differs from the more commonly applied crop water stress index (CWSI) in that it uses both a spectral vegetation index (VI), to determine the degree of surface greenness, and the composite land surface temperature (LST) (not solely canopy temperature).Lightweight thermal and RGB (red-green-blue) cameras were mounted on a UAV on three occasions during the growing season 2014, and provided composite LST and color images, respectively. From the LST, maps of surface-air temperature differences were computed. From the color images, the normalized green-red difference index (NGRDI), constituting the indicator of surface greenness, was computed. Advantages of the WDI as an irrigation map, as compared with simpler maps of the surface-air temperature difference, are discussed, and the suitability of the NGRDI is assessed. Final WDI maps had a spatial resolution of 0.25 m.It was found that the UAV-based WDI is in agreement with measured stress values from an eddy covariance system. Further, the WDI is especially valuable in the late growing season because at this stage the remote sensing data represent crop water availability to a greater extent than they do in the early growing season, and because the WDI accounts for areas of ripe crops that no longer have the same need for irrigation. WDI maps can potentially serve as water stress maps, showing the farmer where irrigation is needed to ensure healthy growing plants, during entire growing season.

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“…Precision agriculture aims at identifying crop and soil properties in near-real-time (Lebourgeois et al, 2012;Primicerio et al, 2012a) and at delivering results to farmers and decision makers with minimum delay to enable management decisions based on current crop and soil status. The use of input resources such as fertilizers, herbicides or water (Van Alphen and Stoorvogel, 2000;Carrara et al, 2004;Chávez et al, 2010) are matched to the current demand by the crops, Published by Copernicus Publications on behalf of the European Geosciences Union.…”

Section: Introductionmentioning

confidence: 99%

Deploying four optical UAV-based sensors over grassland: challenges and limitations

Burkart²,

et al. 2015

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Abstract. Unmanned aerial vehicles (UAVs) equipped with lightweight spectral sensors facilitate non-destructive, nearreal-time vegetation analysis. In order to guarantee robust scientific analysis, data acquisition protocols and processing methodologies need to be developed and new sensors must be compared with state-of-the-art instruments. Four different types of optical UAV-based sensors (RGB camera, converted near-infrared camera, six-band multispectral camera and high spectral resolution spectrometer) were deployed and compared in order to evaluate their applicability for vegetation monitoring with a focus on precision agricultural applications. Data were collected in New Zealand over ryegrass pastures of various conditions and compared to ground spectral measurements. The UAV STS spectrometer and the multispectral camera MCA6 (Multiple Camera Array) were found to deliver spectral data that can match the spectral measurements of an ASD at ground level when compared over all waypoints (UAV STS: R 2 = 0.98; MCA6: R 2 = 0.92). Variability was highest in the near-infrared bands for both sensors while the band multispectral camera also overestimated the green peak reflectance. Reflectance factors derived from the RGB (R 2 = 0.63) and converted near-infrared (R 2 = 0.65) cameras resulted in lower accordance with reference measurements. The UAV spectrometer system is capable of providing narrow-band information for crop and pasture management. The six-band multispectral camera has the potential to be deployed to target specific broad wavebands if shortcomings in radiometric limitations can be addressed. Large-scale imaging of pasture variability can be achieved by either using a true colour or a modified near-infrared camera.Data quality from UAV-based sensors can only be assured, if field protocols are followed and environmental conditions allow for stable platform behaviour and illumination.

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A flexible unmanned aerial vehicle for precision agriculture

Cited by 282 publications

References 14 publications

On the Use of Unmanned Aerial Systems for Environmental Monitoring

On the Use of Unmanned Aerial Systems for Environmental Monitoring

Crop water stress maps for an entire growing season from visible and thermal UAV imagery

Deploying four optical UAV-based sensors over grassland: challenges and limitations

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