Sunflower yield related to multi-temporal aerial photography, land elevation and weed infestation

Peña-Barragãn, J. M.; López‐Granados, Francisca; Jurado-Expósito, Montserrat; García‐Torres, L.

doi:10.1007/s11119-009-9149-6

Cited by 22 publications

(12 citation statements)

References 35 publications

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“…Tremblay et al (2014), who were working with a maize crop and multispectral imagery acquired from an UAV, observed that image segmentation did not practically improve the correlation coefficient between the soil-adjusted vegetation index (SAVI) and fresh biomass or leaf area index. In a sunflower crop, Peña-Barragan et al (2010) found that the best correlation coefficients between NDVI calculated from an aerial image and harvest indexes were associated with images that were acquired around maximum vegetative development or during early productive development. To explain these results, they theorized that the images acquired outside these stages had pixels that were composed of a mixture of vegetation and bare soil.…”

Section: 2mentioning

confidence: 98%

Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop

Agüera-Vega

Ramírez

Saiz

et al. 2015

Biosystems Engineering

172

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Section: 2mentioning

confidence: 98%

Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop

Agüera-Vega

Ramírez

Saiz

et al. 2015

Biosystems Engineering

172

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“…Pena˜- Barraga´n et al (2010) found that the yield of sunflower correlated well with values at the green, red and the NIR bands of airborne imagery during the early development stage but the correlation was poor if images were taken during flowering stage. The AEROCam images were taken during the flowering stage; but we found a linear model that incorporated red and green bands accounted for 75% (adjusted R 2 , n ¼ 105, F value ¼ 155.6 and p 5 0.0001) of the variability in sunflower height values (Figure 5(b)).…”

Section: Deriving Sunflower Height From Aerocam Imagerymentioning

confidence: 99%

“…Everitt and Nixon 1985, Ahern et al 1986, Everitt et al 1986, King 1995 for a variety of precision agriculture practices, such as monitoring crop condition, growth and yield (e.g. Yang et al 2001, Pen˜a-Barraga´n et al 2010, delineating management zones (e.g. Fleming et al 2000), and detecting weeds (e.g.…”

Section: Introductionmentioning

confidence: 99%

Near real-time high-resolution airborne camera, AEROCam, for precision agriculture

Zhang¹,

Kim²,

Streeter³

et al. 2011

Geocarto International

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Precision agriculture often relies on high-resolution imagery to delineate the variability within a field. Airborne Environmental Research Observational Camera (AEROCam) was designed to meet the needs of agriculture producers, ranchers, and researchers, who require high-resolution imagery in a near realtime environment for rapid decision support. AEROCam was developed and operated through a unique collaboration between several departments at the University of North Dakota, including the Upper Midwest Aerospace Consortium (UMAC), the School of Engineering and Mines, and flight operations at the John D. Odegard School of Aerospace Sciences. AEROCam consists of a Redlake MS4100 area-scan multi-spectral digital camera that features a 1920 6 1080 CCD array (7.4-mm detector) with 8-bit quantization. When operated at *2 km above ground level, multispectral images with four bands in the visible and near infrared have a ground sample distance of 1 m with a horizontal extent of just over 1.6 km. Depending on the applications, flying at different altitudes can adjust the spatial resolution from 0.25 to 2 m. Rigorous spectral and radiometric calibrations allow AEROCam to be used in a variety of applications, qualitative and quantitative. Equipped with an inertial measurement unit (IMU) system, the images acquired can be geo-referenced automatically and delivered to end users near real time through our Digital Northern Great Plains system (DNGP). The images are also available to zone mapping application for precision farming (ZoneMAP), an online decision support tool for creating management zones from remote sensing imagery and data from other sources. Operational since 2004, AEROCam has flown over 250 sorties and delivered over 150,000 images to the users in the Northern Great Plains region, resulting in numerous applications in precision agriculture and resource management.

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“…Remote-sensed images normally cover extensive areas and, when adequately processed, provide agri-environmental information at a landscape level for cropping system statistics , 2009 and at a farm level for subsidies inspection follow-up (Peña- Barragán et al 2004) and for planning precision agricultural operations at selected plots (López-Granados et al 2006). Technological advances are broadening the use of remote sensing in precision agriculture, as follows: (a) Satellites such as Quick Bird (QB) provide high spatial resolution images of around 2.0-2.4 m in multi-spectral (DIGITAL GLOBE Corp 2010); (b) Conventional airplanes images can be used to map the spatial variability of biotic and abiotic parameters on agricultural plots at spatial resolutions of 0.25-0.50 m (STEREOCARTO 2010); (c) Unmanned aerial vehicles (UAV) flying at low altitude have been developed by commercial companies (UAV 2010) to provide high spatial resolution images, with the advantage of autonomous management and ability to work in cloudy days.…”

Section: Introductionmentioning

confidence: 99%

Geo-referencing remote images for precision agriculture using artificial terrestrial targets

et al. 2011

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The aim of this paper is to assess co-registration errors in remote imagery through the AUGEO system, which consists of geo-referenced coloured tarps acting as terrestrial targets (TT), captured in the imagery and semi-automatically recognised by AUGEO2.0 Ò software. This works as an add-on of ENVI Ò for image co-registration. To validate AUGEO, TT were placed in the ground, and remote images from satellite Quick Bird (QB), airplanes and unmanned aerial vehicles (UAV) were taken at several locations in Andalusia (southern Spain) in 2008 and 2009. Any geo-referencing system tested showed some error in comparison with the Differential Global Positioning System (DGPS)-geo-referenced verification targets. Generally, the AUGEO system provided higher geo-referencing accuracy than the other systems tried. The root mean square errors (RMSE) from the panchromatic and multi-spectral QB images were around 8 and 9 m, respectively and, once co-registered by AUGEO, they were about 1.5 and 2.5 m, for the same images. Overlapping the QB-AUGEO-geo-referenced image and the National Geographic Information System (NGIS) produced a RMSE of 6.5 m, which is hardly acceptable for precision agriculture. The AUGEO system efficiently geo-referenced farm airborne images with a mean accuracy of about 0.5-1.5 m, and the UAV images showed a mean accuracy of 1.0-4.0 m. The geo-referencing accuracy of an image refers to its consistency despite changes in its spatial resolution. A higher number of TT used in the geo-referencing process leads to a lower obtained RMSE. For example, for an image of 80 ha, about 10 and 17 TT were needed to get a RMSE less than about 2 and 1 m. Similarly, with the same number of TT, accuracy was higher for smaller plots as compared to larger plots. Precision agriculture requires high spatial resolution images (i.e., \1.5 m pixel -1 ), accurately geo-referenced (errors \1-2 m). With the current DGPS technology, satellite and airplane images hardly meet this geo-referencing requirement; consequently, additional co-registration effort is needed. This can be achieved using georeferenced TT and AUGEO, mainly in areas where no notable hard points are available.

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Sunflower yield related to multi-temporal aerial photography, land elevation and weed infestation

Cited by 22 publications

References 35 publications

Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop

Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop

Near real-time high-resolution airborne camera, AEROCam, for precision agriculture

Geo-referencing remote images for precision agriculture using artificial terrestrial targets

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