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Pixel size and coverage area for the 12-megapixel Fuji FinePix S3 Pro UVIR digital camera at two altitudes (above ground level) and for two lens-focal lengths.
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An Unmanned Airborne Vehicle (UAV) from IntelliTech Microsystems, Inc. was fitted with five down-looking digital cameras, an up-looking quantum sensor, and computer controls based on GPS position. The cameras' internal near-infrared filters were removed and external narrow-band filters at 550, 610, 676, and 780 nm wavelength were fitted over the le...
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... may be some problems with haze that affects the blue band more than the red band; however, UAVs will usually fly close to the ground to acquire higher resolution imagery, so the effect of haze will be small. From the focal length, the size of the camera lens, and the number of detector elements, the spatial resolution and the area covered by each photograph can be determined as a function of altitude above ground level (Table 1). For flights at 400 feet (122 m) and using a wide-angle lens (24 mm), the pixel size is about 2.7 cm and the area covered in one photograph is 0.91 ha. ...Similar publications
Digitally-mediated practices of archaeological data require reflexive thinking about where archaeology stands as a discipline in regard to the ‘digital,’ and where we want to go. To move toward this goal, we advocate a historical approach that emphasizes contextual source-side criticism and data intimacy—scrutinizing maps and 3D data as we do artif...
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... One of the first available UAV for crop monitoring was the Vector-P UAV ( Fig. 11(a)). It was designed to have multiple downwards looking cameras, and equipped with the capability to acquire NIR and multi-spectral images [42]. The system showed the potential of monitoring crop utilizing UAV at a very early stage, where it was used to evaluate the LAI of winter wheat through digital photography with orthomosaic-ed tiles [37], [43]. ...
The applications of remote sensing technologies to precision agriculture were reviewed. The different uses of conventional satellites and unmanned aerial vehicles (UAVs) for data collection were also examined. The ways in which the collected data have been used in terms of precision agriculture are laid out in further detail. Modern UAVs provide a range of advantages over conventional satellites in data collection and analysis. In addition, precision agriculture itself is a wide topic; and practitioners, researchers, and policy planners need to account for a complex value chain with diverse stakeholders. Thus, in order to further the discussion in a meaningful manner, the later sections of this article provide additional analysis on topics deemed crucial for the development of precision agriculture: a discussion on key technological limitations for scaling, a corresponding list of suggestions for the development of solutions, and a brief overview on the current social-economical landscape of the agriculture industry.
... Various unmanned LARS systems have been developed and used in the remote image acquisition for PA applications. Some LARS platforms have been kites (Aber et al., 2002), balloons (Amoroso and Arrowsmith, 2000; Seang and Mund, 2006), high‐clearance tractors (Bausch and Delgado, 2005), and unmanned airplanes and helicopters (Sugiura et al., 2002; Fukagawa et al., 2003; Eisenbiss, 2004; Herwitz el al., 2004; Sugiura et al., 2004; Hunt et al., 2005; MacArthur et al., 2005 MacArthur et al., , 2006 Xiang and Tian, 2006, 2007a, 2007b Huang et al., 2008). These platforms were mounted with image ac‐ quisition devices and location measuring receivers, which can fly over agriculture farms and targeted areas for capturing images. ...
... d that both agricultural aircraft and unmanned aerial vehicles (UAVs) are potentially more easily scheduled and accessible remote sensing platforms than the remote sensing satellites and gen‐ eral aviation aircraft customarily used in the U.S. However, use of agricultural aircraft is limited to those areas where ae‐ rial crop spraying is prevalent. Hunt et al. (2005) used a radio‐ controlled helicopter‐mounted image acquisition system to estimate biomass and nitrogen status for corn, alfalfa, and soybean crops. Digital photographs have been used for site‐ specific weed control for grassland swards (Gebhardt et al., 2006; Beerwinkle, 2001) and for tomato (Zhang et al., 2005). Chen et al. (2003), usin ...
A radio-controlled unmanned helicopter-based low-altitude remote sensing (LARS) platform was used to acquire quality images of high spatial and temporal resolution in order to estimate yield and total biomass of a rice crop ( Oriza sativa L.). Fifteen rice field plots with five N treatments (0, 33, 66, 99, and 132 kg ha-1 ) having three replicates each were arranged in a randomized complete block design for estimating yield and biomass as a function of applied N. Images were obtained by image acquisition sensors mounted on the LARS platform operating at the height of 20 m over experimental plots. The rice yield and total biomass for the five N treatments were found to be significantly different at the 0.05 and 0.1 levels of significance, respectively, and normalized difference vegetation index (NDVI) values at panicle initiation stage were highly correlated with yield and total biomass with regression coefficients (r2 ) of 0.728 (RMSE = 0.458 ton ha-1) and 0.760 (RMSE = 0.598 ton ha-1), respectively. The study demonstrated the suitability of using LARS images as a substitute for satellite images for estimating leaf chlorophyll content in terms of NDVI values (r2 = 0.897, RMSE = 0.012). The LARS system described has potential to evaluate areas that require additional nutrients at critical growth stages to improve final yield in rice cropping.
... Plus UAV is used to estimate ripeness status and evaluate harvest readiness [5,6]. A UAV from IntelliTech Microsystems, Inc., fitted with five down-looking digital cameras, an uplooking quantum sensor, is utilized for precision agriculture [7]. Kaaniche et al. [8] presented a vision algorithm based on a video camera on UAV. ...
This work concentrates on the topic of remote sensing using a multispectral imag- ing system for water management and agriculture applications. The platform, which is a light-weight inexpensive runway-free unmanned aerial vehicle (UAV), namely, AggieAir, is presented initially. A major portion of this work focuses on the development of a light- weight multispectral imager payload for the AggieAir platform, called GhostFoto. The imager is band-recongurable, covering both visual red, green, and blue (RGB) and near infrared (NIR) spectrum, and interfaced with UAV on-board computer. The development of the image processing techniques, which are based on the collected multispectral aerial images, is also presented in this work. One application is to perform fully autonomous river tracking for applications such as river water management. Simulation based on aerial mul- tispectral images is done to demonstrate the feasibility of the developed algorithm. Other eort is made to create a systematic method to generate normalized dierence vegetation index (NDVI) using the airborne imagery. The GhostFoto multispectral imaging system based on AggieAir architecture is proven to be an innovative and useful tool.
The incorporation of advanced technologies into Unmanned Aerial Vehicles (UAVs) platforms have enabled many practical applications in Precision Agriculture (PA) over the past decade. These PA tools offer capabilities that increase agricultural productivity and inputs’ efficiency and minimize operational costs simultaneously. However, these platforms also have some constraints that limit the application of UAVs in agricultural operations. The constraints include limitations in providing imagery of adequate spatial and temporal resolutions, dependency on weather conditions, and geometric and radiometric correction requirements. In this paper, a practical guide on technical characterizations of common types of UAVs used in PA is presented. This paper helps select the most suitable UAVs and on-board sensors for different agricultural operations by considering all the possible constraints. Over a hundred research studies were reviewed on UAVs applications in PA and practical challenges in monitoring and mapping field crops. We concluded by providing suggestions and future directions to overcome challenges in optimizing operational proficiency.
Preharvest monitoring of biomass production is necessary to develop optimized instrumentation and data processing systems for crop growth, health, and stress monitoring and to develop algorithms for field operation scheduling. Some research questions of specific interest are as follows: (1) What are the major crop sensing needs for energy crop health monitoring and productivity improvement? (2) Which sensor/platform should be used for the field data collection? (3) What is the best process for energy crop data-to-knowledge conversion? In this chapter, we first review the basics of remote sensing and its application to energy crops. We then discuss the development of three near-real-time remote sensing systems, namely, a stand-alone tower-based remote sensing system, close proximity data collection vehicle, and an unmanned aerial vehicle-based remote sensing system to monitor crop growth. The physical status of crop growth and biomass accumulation was projected over the growing seasons. The remote sensing systems included multispectral camera, light detection and ranging (LIDAR), and a global position system sensor. The sensing systems were convenient to perform site-specific monitoring of bioenergy crops and collect data in near real time including ground reference information. These nondestructive measurements included bioenergy crop growth monitoring using typical vegetation index and estimation of biomass yield by correlating it with suitable vegetation index. The field experimental data has been presented to correlate with remote sensing data. To understand the crop growth status over the growing season, the remote sensing data could be correlated with ground truth data to develop a model for predicting dry matter biomass. © Springer Science+Business Media New York 2014. All rights are reserved.
Before an aerial image can be used to support a site-specific application it is essential to perform the geometric corrections and geocoding. This research discusses the development of an automatic aerial image georeferencing method for an unmanned aerial vehicle (UAV) image data acquisition platform that does not require use of ground control points (GCP). An onboard navigation system is capable of providing continuous estimates of the position and attitude of the UAV. Based on a navigation data and a camera lens distortion model, the image collected by an onboard multispectral camera can be automatically georeferenced. When compared with 16 presurveyed ground reference points, image automatic georeferenced results indicated that position errors were less than 90 cm. A large field mosaic image can be generated according to the individual image georeferenced information. A 56.9 cm mosaic error was achieved. This accuracy is considered sufficient for most of the intended precision agriculture applications.
Monitoring and maximization of bioenergy yield from biomass feedstock has recently become a critically important goal for researchers. Remote sensing represents a potential method to monitor and estimate biomass so as to increase biomass feedstock production from energy crops. This paper reviews the biophysical properties of biomass and remote sensing methods for monitoring energy crops for site-specific management. While several research studies have addressed the agronomic dimensions of this approach, more research is required on perennial energy crops in order to maximize the yield of biomass feedstock. Assessment of established methods could lead to a new strategy to monitor energy crops for the adoption of site-specific management in biomass feedstock production. In this article, satellite, aerial and ground-based remote sensing’s were reviewed and focused on the spatial and temporal resolutions of imagery to adopt for site-specific management. We have concluded that the biomass yield prediction, the ground-based sensing is the most suitable to establish the calibration model and reference for aerial and satellite remote sensing. The aerial and satellite remote sensing are required for wide converge of planning and policy implementations of biomass feedstock production systems.Highlights► This paper reviews biophysical properties of biomass to increase feedstock production. ► Satellite, aerial and ground-based remote sensing were reviewed for site-specific management. ► Ground based remote sensing is the most suitable reference to establish calibration model. ► Aerial and satellite remote sensing are required for wide coverage of biomass feedstock production.
Application of crop production and protection materials is a crucial component in the high productivity of American agriculture. Agricultural chemical application is frequently needed at specific times and locations for accurate site-specific management of crop pests. Piloted agricultural aircraft are typically used to treat large, unobstructed, continuous acreage crops and are not as efficient when working over small or obstructed plots. An Unmanned Aerial Vehicle (UAV), which can be remotely controlled or fly autonomously based on pre-programmed flight plans, may be used to make timely and efficient applications over these small area plots. This research developed a low volume spray system for use on a fully autonomous UAV to apply crop protection products on specified crop areas. This article discusses the development of the spray system and its integration with the flight control system of a fully autonomous, unmanned vertical take-off and landing helicopter. Sprayer actuation can be triggered by preset positional coordinates as monitored by the equipped Global Positioning System (GPS). The developed spray system has the potential to provide accurate, site-specific crop management when coupled with UAV systems. It also has great potential for vector control in the areas that are not easily accessible by personnel or equipment.
