Development of unmanned aerial systems for use in precision agriculture: The AggieAir experience

Torres‐Rua, Alfonso F.; Arab, Manal Al; Hassan-Esfahani, Leila; Jensen, Austin M.; McKee, Mac

doi:10.1109/sustech.2015.7314326

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…For these requirements, manned aircraft and sUAS equipped with temperature sensors have been used. Examples of airborne and sUAS thermal applications can be found in [ 1 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. Despite its importance in satellite temperature related research, little attention has been paid to atmospheric calibration of thermal imagery from manned aircraft and sUAS.…”

Section: Introductionmentioning

confidence: 99%

Vicarious Calibration of sUAS Microbolometer Temperature Imagery for Estimation of Radiometric Land Surface Temperature

Torres‐Rua

2017

Sensors

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In recent years, the availability of lightweight microbolometer thermal cameras compatible with small unmanned aerial systems (sUAS) has allowed their use in diverse scientific and management activities that require sub-meter pixel resolution. Nevertheless, as with sensors already used in temperature remote sensing (e.g., Landsat satellites), a radiance atmospheric correction is necessary to estimate land surface temperature. This is because atmospheric conditions at any sUAS flight elevation will have an adverse impact on the image accuracy, derived calculations, and study replicability using the microbolometer technology. This study presents a vicarious calibration methodology (sUAS-specific, time-specific, flight-specific, and sensor-specific) for sUAS temperature imagery traceable back to NIST-standards and current atmospheric correction methods. For this methodology, a three-year data collection campaign with a sUAS called “AggieAir”, developed at Utah State University, was performed for vineyards near Lodi, California, for flights conducted at different times (early morning, Landsat overpass, and mid-afternoon”) and seasonal conditions. From the results of this study, it was found that, despite the spectral response of microbolometer cameras (7.0 to 14.0 μm), it was possible to account for the effects of atmospheric and sUAS operational conditions, regardless of time and weather, to acquire accurate surface temperature data. In addition, it was found that the main atmospheric correction parameters (transmissivity and atmospheric radiance) significantly varied over the course of a day. These parameters fluctuated the most in early morning and partially stabilized in Landsat overpass and in mid-afternoon times. In terms of accuracy, estimated atmospheric correction parameters presented adequate statistics (confidence bounds under ±0.1 for transmissivity and ±1.2 W/m2/sr/um for atmospheric radiance, with a range of RMSE below 1.0 W/m2/sr/um) for all sUAS flights. Differences in estimated temperatures between original thermal image and the vicarious calibration procedure reported here were estimated from −5 °C to 10 °C for early morning, and from 0 to 20 °C for Landsat overpass and mid-afternoon times.

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Section: Introductionmentioning

confidence: 99%

Vicarious Calibration of sUAS Microbolometer Temperature Imagery for Estimation of Radiometric Land Surface Temperature

Torres‐Rua

2017

Sensors

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“…Hence, visual and measurement-based quantitative scientific analysis can be conducted to evaluate the quality of art, or the time of origin. Moreover, in agriculture and geoinformatics, a lot of applications recently came up, e.g., classification of different types of crops [5], or monitoring agricultural field conditions [6]. Other methods estimate coastal water depth [7], do land cover classification in urban and rural landscape [8], or measure the temperature of different seashore sections [9].…”

Section: A Applicationsmentioning

confidence: 99%

Camera Array for Multi-Spectral Imaging

Genser

Seiler

Kaup

2020

IEEE Trans. on Image Process.

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Recently, many new applications arose for multispectral and hyper-spectral imaging. Besides modern biometric systems for identity verification, also agricultural and medical applications came up, which measure the health condition of plants and humans. Despite the growing demand, the acquisition of multi-spectral data is up to the present complicated. Often, expensive, inflexible, or low resolution acquisition setups are only obtainable for specific professional applications. To overcome these limitations, a novel camera array for multi-spectral imaging is presented in this article for generating consistent multispectral videos. As differing spectral images are acquired at various viewpoints, a geometrically constrained multi-camera sensor layout is introduced, which enables the formulation of novel registration and reconstruction algorithms to globally set up robust models. On average, the novel acquisition approach achieves a gain of 2.5 dB PSNR compared to recently published multi-spectral filter array imaging systems. At the same time, the proposed acquisition system ensures not only a superior spatial, but also a high spectral, and temporal resolution, while filters are flexibly exchangeable by the user depending on the application. Moreover, depth information is generated, so that 3D imaging applications, e.g., for augmented or virtual reality, become possible. The proposed camera array for multi-spectral imaging can be set up using off-the-shelf hardware, which allows for a compact design and employment in, e.g., mobile devices or drones, while being cost-effective.

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“…UAV estimation of crop nutrient status can directly benefit the application rate recommendations by producer or agronomist consultant by including the entirety of the field. Research efforts indicate that it is possible to perform the monitoring with scientific UAVs and specialized camera sensors such as optical and thermal cameras [67,68] along with specialized optical filters such as Red Edge or hyper spectral cameras [69,70,71] . Accelerometer and Gyroscope Sensors were used for spraying fertilizer and pesticide; it has ability to reduce time and human efforts [72] .…”

Section: Sprayingmentioning

confidence: 99%

Application of drone in agriculture: A review

Dutta¹,

Goswami²

2020

Int. J. Chem. Stud.

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The population is increasing tremendously and with this increase the demand of food. The traditional methods which were used by the farmers were not sufficient enough to fulfil these requirements. Thus, new automated methods (Drone technology) were introduced. These new methods satisfied the food requirements and also provided employment opportunities to billions of people. Drones technologies saves the excess use of water, pesticides, and herbicides, maintains the fertility of the soil, also helps in the efficient use of man power and elevate the productivity and improve the quality. The objective of this paper is to review the usage of Drones in agriculture applications. Based on the literature, we found that a lot of agriculture applications can be done by using Drone. In the methodology, we used a comprehensive review from other researches in this world. This paper summarizes the current state of drone technology for agricultural uses, including crop health monitoring and farm operations like weed management, Evapotranspiration estimation, spraying etc. The research article concludes by recommending that more farmers invest in drone technology to better their agricultural outputs.

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Development of unmanned aerial systems for use in precision agriculture: The AggieAir experience

Cited by 8 publications

References 6 publications

Vicarious Calibration of sUAS Microbolometer Temperature Imagery for Estimation of Radiometric Land Surface Temperature

Vicarious Calibration of sUAS Microbolometer Temperature Imagery for Estimation of Radiometric Land Surface Temperature

Camera Array for Multi-Spectral Imaging

Application of drone in agriculture: A review

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