Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support

Herwitz, Stanley R.; Johnson, Lee F.; Dunagan, Stephen E.; Higgins, Robert G.; Sullivan, Donald V.; Zheng, Jian Ping; Lobitz, B.; Leung, Joseph; Gallmeyer, B.; Aoyagi, Michio; Slye, Robert E.; Brass, James A.

doi:10.1016/j.compag.2004.02.006

Cited by 350 publications

(198 citation statements)

References 13 publications

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“…Remote-control helicopters were used to generate maps of crop status (Sugiura, et al 2005) and model aircraft were used as the platform to remotely sense crop biomass and nitrogen status (Hunt Jr., et al 2005). Additionally, a high altitude unmanned aerial vehicle was used to monitor crop ripeness and weeds in a coffee plantation (Herwitz, et al 2004). …”

Section: Introductionmentioning

confidence: 99%

Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform

Jensen

Apan

Young

et al. 2007

Computers and Electronics in Agriculture

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A low-altitude platform utilising a 1.8-m diameter tethered helium balloon was used to position a multispectral sensor, consisting of two digital cameras, above a fertiliser trial plot where wheat (Triticum spp.) was being grown. Located in Cecil Plains, Queensland, Australia, the plot was a long-term fertiliser trial being conducted by a fertiliser company to monitor the response of crops to various levels of nutrition. The different levels of nutrition were achieved by varying nitrogen application rates between 0-120 units of N at 40 unit increments. Each plot had received the same application rate for 10 years. Colour and nearinfrared images were acquired that captured the whole 2 ha plot. These images were examined and relationships sought between the captured digital information and the crop parameters imaged at anthesis and the at-harvest quality and quantity parameters. The statistical analysis techniques used were correlation analysis, discriminant analysis, and partial least squares regression. A high correlation was found between the image and yield (R 2 = 0.91) and a moderate correlation between the image and grain protein content (R 2 = 0.66). The utility of the system could be extended by choosing a more mobile platform. This would increase the potential for the system to be used to diagnose the causes of the variability and allow remediation, and/or to segregate the crop at harvest to meet certain quality parameters.Keywords: grain protein, grain yield, low altitude, digital camera This is the authors' final pre-print version of: Jensen, Troy and Apan, Armando and Young, Frank and Zeller, Lez (2007) Detecting the attributes of a wheat crop using digital 3D imagery acquired from a low-altitude platform.

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Section: Introductionmentioning

confidence: 99%

Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform

Jensen

Apan

Young

et al. 2007

Computers and Electronics in Agriculture

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“…Limitations associated with traditional aerial imagery platforms can be overcome by using UAV (Unmanned Aerial Vehicle); in recent years, UAV have been developed into a new aerial platform for image acquisition with a tremendous potential for mapping vegetation cover for detailed vegetation studies with environmental and agricultural objectives (Bryson et al, 2010;García-Ruiz et al, 2013;Herwitz et al, 2004;Laliberte et al, 2006;Torres-Sánchez et al, 2013).…”

Section: Fvc (Fractional Vegetationmentioning

confidence: 99%

Estimation of Fractional Vegetation Cover in Sand Dunes Using Multi-spectral Images from Fixed-wing UAV

Choi¹,

Lee²,

Jung³

et al. 2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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Since the use of UAV (Unmanned Aerial Vehicle) is convenient for the acquisition of data on broad or inaccessible regions, it is nowadays used to establish spatial information for various fields, such as the environment, ecosystem, forest, or for military purposes. In this study, the process of estimating FVC (Fractional Vegetation Cover), based on multi-spectral UAV, to overcome the limitations of conventional methods is suggested. Hence, we propose that the FVC map is generated by using multi-spectral imaging. First, two types of result classifications were obtained based on RF (Random Forest) using RGB images and NDVI (Normalized Difference Vegetation Index) with RGB images. Then, the result map was reclassified into vegetation and nonvegetation. Finally, an FVC map-based RF were generated by using pixel calculation and FVC map-based GI (Gutman and Ignatov) model were indirectly made by fixed parameters. The method of adding NDVI shows a relatively higher accuracy compared to that of adding only RGB, and in particular, the GI model shows a lower RMSE (Root Mean Square Error) with 0.182 than RF. In this regard, the availability of the GI model which uses only the values of NDVI is higher than that of RF whose accuracy varies according to the results of classification. Our results showed that the GI mode ensures the quality of the FVC if the NDVI maintained at a uniform level. This can be easily achieved by using a UAV, which can provide vegetation data to improve the estimation of FVC. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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“…Unmanned Aerial Vehicle (UAV) [1,2], as new modern military equipment, with its unique advantage, is fully compatible with the modern war concepts of "no contact" and "zero casualties". UAVs can carry out tasks such as reconnaissance and surveillance, laser guidance, electronic jamming, communication relay, target location, combat assessment, and precision strike by carrying different equipment.…”

Section: Introductionmentioning

confidence: 99%

A new UAV PTZ Controlling System with Target Localization

Qingsheng

Liang

et al. 2017

MATEC Web Conf.

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Abstract. In recent years, with the continuous exposure of unmanned aerial vehicles in the local battlefield, as well as the rapid development of UAV technology, UAV's military application values, especially the advantages in the low-altitude reconnaissance have been the world's attention. Unmanned reconnaissance aircraft is becoming very promising new equipment in technology battlefield and an important information combat front. However, most of the current UAV controlling systems only provide manually PTZ controlling functions, which is inconvenient for the user to control the sensor to monitor targets on demand. This paper propose a new UAV PTZ controlling system, which enables user to keep target in the view filed by simply selecting a position from the digital map. The proposed system is also able to do real-time target localization for any pixel of the video. Experimental results show that our system is easy for PTZ controlling and the localization error is small.

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Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support

Cited by 350 publications

References 13 publications

Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform

Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform

Estimation of Fractional Vegetation Cover in Sand Dunes Using Multi-spectral Images from Fixed-wing UAV

A new UAV PTZ Controlling System with Target Localization

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