Use of high-resolution multispectral imagery from an unmanned aerial vehicle in precision agriculture

Al-Arab, Manal; Torres‐Rua, Alfonso F.; Ticlavilca, Andres M.; Jensen, Austin M.; McKee, Mac

doi:10.1109/igarss.2013.6723419

Cited by 14 publications

(9 citation statements)

References 8 publications

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“…The RVM is a nonlinear model based on a Bayesian probabilistic treatment to determine a descriptive function based on existing information. The RVM has been extensively used in hydrological, water resources, and Earth image processing [6,15,18,19,54,55], with excellent results.…”

Section: The Relevance Vector Machinementioning

confidence: 99%

Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines

Torres‐Rua

Ticlavilca

Bachour

et al. 2016

Water

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Spatial surface soil moisture can be an important indicator of crop conditions on farmland, but its continuous estimation remains challenging due to coarse spatial and temporal resolution of existing remotely-sensed products. Furthermore, while preceding research on soil moisture using remote sensing (surface energy balance, weather parameters, and vegetation indices) has demonstrated a relationship between these factors and soil moisture, practical continuous spatial quantification of the latter is still unavailable for use in water and agricultural management. In this study, a methodology is presented to estimate volumetric surface soil moisture by statistical selection from potential predictors that include vegetation indices and energy balance products derived from satellite (Landsat) imagery and weather data as identified in scientific literature. This methodology employs a statistical learning machine called a Relevance Vector Machine (RVM) to identify and relate the potential predictors to soil moisture by means of stratified cross-validation and forward variable selection. Surface soil moisture measurements from irrigated agricultural fields in Central Utah in the 2012 irrigation season were used, along with weather data, Landsat vegetation indices, and energy balance products. The methodology, data collection, processing, and estimation accuracy are presented and discussed.

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Section: The Relevance Vector Machinementioning

confidence: 99%

Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines

Torres‐Rua

Ticlavilca

Bachour

et al. 2016

Water

Self Cite

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“…As a first step verification of the RATC controller, a simulation using a 6 degree of freedom aircraft mathematical model was implemented. Matlab Simulink TM environment was employed to numerically integrate the equations of motion (1)(2)(3)(4). An autopilot was also placed inside the simulation to allow control of the aircraft based on the methods found in [5].…”

Section: Flight Test Resultsmentioning

confidence: 99%

“…The use of small unmanned aerial vehicles (UAVs) in remote sensing programs is growing tremendously [1]. Often, these UAVs carry small scientific cameras to gather aerial imagery [2] [3].…”

Section: Introductionmentioning

confidence: 99%

Rudder-Augmented Trajectory Correction for Small Unmanned Aerial Vehicle to Minimize Lateral Image Errors

Fisher¹,

Sharma

2018

Journal of Aerospace Information Systems

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Civil applications for unmanned aerial vehicles (UAVs) have increased rapidly over the last few years. In the realm of civil applications, many aircraft carry cameras that are physically fixed to the airframe. While this yields a simple and robust remote sensing platform, the imagery quality diminishes with increasing attitude errors. A rudder augmented trajectory correction method for small unmanned aerial vehicles is discussed in this paper. The goal of this type of controller is to minimize the lateral image errors of body fixed non-gimbaled cameras. We present a comparison to current aileron only trajectory correction autopilots. Simulation and flight test results are presented that show significant reduction in the roll angle present during trajectory correction resulting in a large effect on total flight line image deviations.

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“…La agricultura de precisión requiere una alta gestión espacial de los insumos para la producción agrícola. Esto necesita que la información procesable sobre el estado del cultivo y el campo se adquiera con la misma resolución espacial alta y en una frecuencia temporal apropiada para las respuestas oportunas (Al-Arab et al, 2013).…”

Section: Las Actividades Agrícolasunclassified

“…La estimación usando drones del estado de los nutrientes del cultivo puede beneficiar directamente la tasa de aplicación al incluir la totalidad del campo. En este sentido, los resultados de la investigación indican que es posible realizar el monitoreo con vehículos aéreos no tripulados científicos y sensores de cámara especializados como cámaras ópticas y térmicas (Al-Arab et al, 2013;Torres-Rua et al, 2018), junto con sensores especializados filtros ópticos como Red Edge o cámaras hiperespectrales.…”

Section: Los Nutrientes En Los Cultivosunclassified

Los drones una herramienta para una agricultura eficiente: un futuro de alta tecnología

2019

Idesia

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Este artículo es una revisión sobre el estado actual del uso de la tecnología de drones y los productos que se obtienen con sus vuelos programados. Se establecen los tipos utilizados con más frecuencia en la agricultura de precisión, los tipos de cámaras y/o sensores usados, las imágenes obtenidas luego del procesamiento de la información lograda con los sensores. Asimismo se especifican las aplicaciones que se pueden realizar en la actualidad en las actividades o labores agrícolas, estimación de la evapotranspiración y contenido de humedad del suelo, nutrientes en los cultivos y rendimiento de los cultivos. También se establecen las ventajas y desventajas del uso de esta tecnología, desde aspectos técnicos hasta económicos, dando alternativas factibles para su utilización. Palabras clave: drones, agricultura eficiente, agricultura de precisión, drones y agricultura ABSTRACT This article is a review on the current status of the use of drones technology and the products obtained with scheduled flights. It establishes the types most frequently used in precision agriculture, the types of cameras and / or sensors used, the images obtained after the processing of the information obtained with the sensors used. It also establishes the possible applications that can be made to the present in agricultural activities or work, estimation of evapotranspiration and soil moisture content, nutrients in crops and crop yield. The advantages and disadvantages of the use of this technology are also established from technical to economic aspects, giving feasible alternatives for its use.

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Use of high-resolution multispectral imagery from an unmanned aerial vehicle in precision agriculture

Cited by 14 publications

References 8 publications

Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines

Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines

Rudder-Augmented Trajectory Correction for Small Unmanned Aerial Vehicle to Minimize Lateral Image Errors

Los drones una herramienta para una agricultura eficiente: un futuro de alta tecnología

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