A comparision between satellite based and drone based remote sensing technology to achieve sustainable development: a review

Babankumar Bansod, Rangoli Singh, Ritula Thakur, Gaurav Singhal


Precision agriculture is a way to manage the crop yield resources like water, fertilizers, soil, seeds in order to increase production, quality, gain and reduce squander products so that the existing system become eco-friendly. The main target of precision agriculture is to match resources and execution according to the crop and climate to ameliorate the effects of Praxis. Global Positioning System, Geographic Information System, Remote sensing technologies and various sensors are used in Precision farming for identifying the variability in field and using different methods to deal with them. Satellite based remote sensing is used to study the variability in crop and ground but suffer from various disadvantageous such as prohibited use, high price, less revisiting them, poor resolution due to great height, Unmanned Aerial Vehicle (UAV) is other alternative option for application in precision farming. UAV overcomes the drawback of the ground based system, i.e. inaccessibility to muddy and very dense regions. Hovering at a peak of 500 meter - 1000 meter is good enough to offer various advantageous in image acquisition such as high spatial and temporal resolution, full flexibility, low cost. Recent studies of application of UAV in precision farming indicate advanced designing of UAV, enhancement in georeferencing and the mosaicking of image, analysis and extraction of information required for supplying a true end product to farmers. This paper also discusses the various platforms of UAV used in farming applications, its technical constraints, seclusion rites, reliability and safety.

Full Text:



Arnold, T., De Biasio, M., Fritz, A., Leitner, R., 2013. UAV-based measurement of vegetation indices for environmental monitoring, In Proceedings of 2013 International Conference on Sensing Technology (ICST), Wellington, New Zealand, 3–5 December 2013; pp. 704–707.

Baluja, J., Diago, M.P., Balda, P., Zorer, R., Meggio, F., Morales, F., Tardaguila, J., 2012. Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV), Irrig. Sci., 30, 511–522.

Bendig, J., Bolten, A., & Bareth, G., 2012. Introducing a low-cost mini-UAV for thermal- and multispectral-imaging, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 39(B1), 345–349.

Berni, J. A. J., Zarco-Tejada, P. J., Suarez, L., Gonzalez-Dugo, V., & Fereres, E., 2009a. Remote sensing of vegetation from UAV platforms using lightweight multispectral and thermal imaging sensors, Retrieved March 12, 2012 from http,//www.ipi.uni-hannover.de/fileadmin/institut/pdf/isprs-Hannover2009/ Jimenez_Berni-155.pdf.

Baret, E., Guyot, G., & Major, D. J., 1989a. TSAVI, A vegetation index which minimizes soil brightness effects on LAI and APAR estimation, In Proceedings of the 12th Canadian Symposium on Remote Sensing, Vancouver, Canada, 1355-1358

Bauer, M. E., & Cipra, J. E., 1973. Identification of agricultural crops by computer processing of ERTS MSS data. LARS Technical Reports, Paper 20, http,//docs.lib.purdue.edu/larstech/20. W. Lafayette, IN, Purdue Univ.

Beard , Randal W., Derek Kingston, Morgan Quigley, Deryl Snyder, Reed Christiansen, Walt Johnson, Timothy McLain, & Michael Goodrich, 2005. Autonomous vehicle technologies for small fixedwing UAVs, Journal of Aerospace Computing, Information, and Communication, 2(1), 92-108.

BenDor,E., 2002. Quantitative remote sensing of soil properties. Advances in Agronomy, 75, 173-243.

Bryant, R., Susan Moran, M., McElroy, S. A., Chandra Holificld, Thome, K. J., & Tomoaki Miura, 2003. Data continuity of Earth Observing 1 (EO1) Advanced Land I satellite imager (ALI) and Landsat TM and ETM+. IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1204-1214.

Bulanon, Duke, M., Horton, Mark, Salvador, Paulo Fallahi, & Esmaeil, 2014. Apple Orchard Monitoring Using Aerial Multispectral Imaging. Americn Soceity of Biological and Engineering Soceity, 141913165. doi, 10.13031/aim.20141913165.

Calderón, R., Navas-Cortés, J., Lucena, C., Zarco-Tejada, P., 2013. High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sens. Environ., 139, 231–245.

Caroline, M. Gevaert, Juha Suomalainen, Jing Tang, & Lammert Kooistra, 2015. Generation of Spectral–Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6), 3140 – 3146.

Castillejo-Gonzalez, I. L., Lopez-Granados, F., Garcia-Ferrer, A., Pena-Barragan, J. M., Jurado-Exposito, M., Orden, M. S., 2009. Object- and pixel-based analysis for mapping crops and their agroenvironmental associated measures using QuickBird imagery. Computers and Electronics in Agriculture, 68, 207–215.

Chao, H., Baumann, M., Jensen, A., Chen, Y., Cao, Y., Ren, W., & McKee, M., 2008. Bandre configurable Multi UAV based cooperative remote sensing for real time watermanagement and distributed irrigation control, Paper presented at the IFAC World Congress, Seoul, Korea.

Chuchra, Jyotsana, 2016. Drones and Robots, Revolutionizing Farms of the Future,

https,//www.geospatialworld.net/article/drones-and- robots-future- agriculture/

Clarke, R., & Bennett Mose, L., 2014. The regulation of civilian drones' impacts on public safety. Computer Law & Security Review, 30(3), 263¬-285. doi, http,//dx.doi.org/10.1016/j.clsr.2014.03.007.

Colewell, R. N., 1956. Determining the prevalence of certain cereal crop diseases by means of aerial photography, Hilgardia 26, 223–286.

Colomina, I., & Molina, P., 2014. Unmanned aerial systems for photogrammetry and remote sensing, A review. ISPRS J. Photogramm. Remote Sensing, 92, 79–97.

Cook, S. E., & Bramley, R. G. V., 1998. Precision agriculture, Opportunities, benefits and pitfalls of site specific crop management in Australia. Australian Journal of Experimental, Agriculture 38, 753–763.

David, J.M., 2013. Twenty five years of remote sensing in precision agriculture, Key advances and remaining knowledge gaps. Biosystem. Enineering, 114,358–371.

De Tar, W. R., Chesson, J. H., Penner, J. V., & Ojala, J. C., 2008. Detection of soil properties with airborne hyperspectral measurements of bare fields. Transactions of the ASABE, 51,463–470.

Du, Q., Chang, N. B., Yang, C. H., & Srilakshmi, K. R., 2008. Combination of multispectral remote sensing, variable rate technology and environmental modeling for citrus pest management. Journal of Environmental Management, 86,14-26.

Donoghue, D., Watt, P., Cox, N.,&Wilson, J., 2006. Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data, International Workshop 3D remote sensing in Forestry, Retrieved March 12, 2012 form http,//www.rali.boku.ac.at/fileadmin/_/H857-VFL/workshops/3drsforestry/presentations/6a.5-donoghue.pdf.

Doraiswamy, P. C., Moulin, S., Cook, P. W., & Stern, A., 2003. Crop yield assessment from remote sensing. Photogrammetric Engineering and Remote Sensing 69, 665-674.

Ehsani, R., Sankaran, S., Maja, J., & Neto, J.C., 2014. Affordable multirotor Remote sensing platform for applications in precision horticulture. Paper presented at the 12 th International Conference on Precision Agriculture.

Enclona, E. A., Thenkabail, P. S., Celis, D., & Diekmann, J., 2004. Within-field wheat yield prediction from IKONOS data A new matrix approach. International Journal of Remote Sensing, 25, 377–388.

Erickson, B. J., Johannsen, C. J., Vorst, J. J., & Biehl, L. L., 2004. Using remote sensing to assess stand loss and defoliation in maize. Photogrammetric Engineering and Remote Sensing, 70,717–722.

Everaerts, J., 2008. The use of unmanned aerial vehicles (UAVs) for remote sensing andmapping. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, 37, 1187-1192.

FOODY, G.M., 1988. Crop classification from airborne synthetic aperture radar data. International Journal of Remote Sensing, 9(4), 655-668.

Fornace, K. M., Drakeley, C. J., William, T., Espino,F., & Cox, J., 2014. Mapping infectious disease landscapes, unmanned aerial vehicles and epidemiology. Trends in parasitology, 30(11), 514-519

Franke, Ulrike Esther, 2015. Civilian Drones, Fixing an Image Problem?". ISN Blog, International Relations and Security Network.

Gago, J., Martorell, S., Tomas, M., Pou, 2013. A High-resolution aerial thermal imagery for plantwater status assessment in vineyards using a multicopter-RPAS. In Proceedings of 2013 VII Congreso Ibérico de Agroingeniería y Ciencias Hortícolas, Madrid, Spain, 26–29 August 2013, 1–6.

GarciaRuiz, F., Sankaran, S., Maja, J. M., Lee, Lee, W.S., Rasmussen, J., & Ehsani, R., 2013. Comparison of two aerial imaging platforms for identification of Huanglongbinginfected citrus trees. Computers and Electronics in Agriculture, 91,106-115.

Gardner, R., Nielsen,C., & Shock, C., 1992. Infrared Thermometry and the Crop Water Stress Index, Sampling Procedures and Interpretation. J. Prod. Agric., 5, 466-475. doi,10.2134/jpa1992.0466

Godwin, R. J., Richards, T. E., Wood, G. A., Welsh, J. P., & Knight, S. M., 2003. An economic analysis of the potential for precision farming in UK cereal production. Biosystems Engineering, 84,533–545.

Gomez, C., Rossel, R. A. V., & McBratney, A. B., 2008. Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy An Australian case study. Geoderma, 146, 403–411.

Gomez-Casero, Castillejo-Gonzalez, M. T., Garcia-Ferrer, I. L., Pena-Barragan, A., Jurado-Exposito, J. M., Garcia-Torres, M., L´opez-Granados F., 2010. Spectral discrimination of wild oat and canary grass in wheat fields for less herbicide application. Agronomy for Sustainable Development, 30, 689–699.

Government Of India Office Of The Director General Of Civil Aviation, 2016. Guidelines for obtaining Unique Identification Number (UIN) & Operation of Civil Unmanned Aircraft System (UAS), http,//www.dgca.nic.in/misc/draft%20circular/AT_Circular%20-

Grenzdörffer, G., Engel, A., & Teichert, B, 2008. The photogrammetric potential of Lowcost UAVs in forestry and agriculture. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 31(B3),1207-1214.

Harwin, S., & Lucieer, A., 2012. Assessing the accuracy of georeferenced point clouds produced via multiview stereopsis from unmanned aerial vehicle (UAV) imagery. Remote Sensing, 4(6), 1573-1599.

Herwitz, S. R., Johnson., L. F. , Dunagan, S. E ., Higgins, R. G., Sullivan, D. V., Zheng, J., Lobitz, B.M, Leung, J.G., Gallmeyer, B.A., Aoyagi, M., Slye, R.E., & Brass, J.A., 2004. Imaging from an unmanned aerial vehicle, agricultural surveillance and decision support. Computers and Electronics in Agriculture, 44(1), 49-61, doi, http,//dx.doi.org/10.1016/j.compag.2004.02.006

Huang, Y., Thomson, S.J., Hoffmann, W.C., Lan, Y., & Fritz, B.K. , 2013. Development and prospect of unmanned aerial vehicle technologies for agricultural production management. International Journal of Agricultural and Biological Engineering, 6(3), 1-10.

Huete, A. R., 1988, A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295-309.

Hunt, E. R., Cavigelli, M., Daughtry, C. S. T., McMurtrey, J. E., & Walthall, C. L., 2005. Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precision Agriculture, 6, 359–378.

Jackson, R. D., 1984. Remote sensing of vegetation characteristics for farm management. In Proceedings of the Society of Photo-Optical Instrumentation Engineers, 475, 81–96.

Jewel, N., 1989. An evaluation of multi-date SPOT data for agriculture and land use mapping in the United Kingdom. International Journal of Remote Sensing, 10, 939-951.

Jianwei Yue, Tianjie Lei, Changchun Li, & Jiangqun Zhu., 2004. The Appli Cation Of Unmanned Aerial Vehicle Remote Sensing In Quickly Monitoring Crop Pests. Computers and Electronics in Agriculture, 44, 49–61.

Lumme,J., Karjalainen, M., Kaartinen, H., Kukko , A., Hyyppä , J., Hyyppä , H., Jaakkola , A., & Kleemola, J., 2008. Terrestrial Laser Scanning of Agricultural Crops. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII( B5), 563-566, Beijing.

Jose A. J. Berni , Pablo J. Zarco-Tejada,& Lola Suarez, 2009. Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle. IEEE Transactions on Geoscience and Remote Sensing, 47(3),722 – 738.

Kauth, R. J., and Thomas, G. S., 1976. The tasseled cap--a graphic description of the spectral-temporal development of agrictdtural crops as seen by Landsat. Proc. Symp. on Machine Processing of Remotely Sensed Data, Purdue University, West Lafayette, 41 -51.

Lamb, D. W., Frazier, P., & Adams, P., 2008. Improving

pathways to adoption, Putting the right P’s in precision agriculture. Computers and Electronics in Agriculture, 61,4–9.

Lan, Y., Huang, Y., Martin, D. E., & Hoffmann, W. C., 2009. Development of an airborne remote sensing system for crop pest management, System integration and verification. Transactions of the ASABE, 25,607–615.

Lelong, C. C. D., Pinet, P. C., & Poilve´, H., 1998. Hyperspectral imaging and stress mapping in agriculture,A case study on wheat in Beauce (France). Remote Sensing of Environment, 66,179–191.

Lelong, C. C. D., Burger, P., Jubelin, G., Roux, B., Labbe, S., & Barett, F., 2008. Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors, 8, 3557–3585.

Lopez-Lozano,R., Baret, F., de Cortazar-Atauri, I. N., Bertrand, N., & Casterad, M. A., 2009. Optimal geometric configuration and algorithms for LAI indirect estimates under row canopies, The case of vineyards. Agricultural and Forest Meteorology, 149, 1307–1316.

Lorenzen, B., & Jensen, A. , 1989. Changes in leaf spectral properties induced in barley by cereal powdery mildew. Remote Sensing of Environment, 27,201–209.

Lucieer, A., Malenovský, Z., Veness, T., & Wallace,L., 2014. HyperUAS—Imaging spectroscopy from a multirotor unmanned aircraft system. Journal of Field Robotics, 31(4), 571-590.

Malthus, T. J., & Maderia, A. C., 1993. High resolution spectroradiometry, Spectral reflectance of field bean leaves infected by Botrytis fabae. Remote Sensing of Environment, 45,107–116.

MarketsandMarkets, 2013. Unmanned Aerial Vehicle Market (2013–2018), Dallas, TX, USA.

McNairn, H., & Brisco, B., 2004. The application of C-band polarimetric SAR for agriculture, A review. Canadian Journal of Remote Sensing 30, 525–542.

Gitelson, Anatoly A., Kaufman, Yoram J., 1998. MODIS NDVI Optimization To Fit the AVHRR Data Series—Spectral Considerations. Remote Sensing of Environment, 66, 343-350

Monmonier, M., 2002. Aerial photography at the Agricultural Adjustment Administration, Acreage controls, conservation. Photogrammetric Engineering & Remote Sensing, 68, 1257–1261.

Moran, M. S., Inoue, Y., & Barnes, E. M., 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment, 61, 319-346.

Nagai, M., Chen, T. , Shibasaki, R., Kumagai, H., & Ahmed, A., 2009. UAVborne 3D mapping system by multisensor integration. IEEE Transactions on Geoscience and Remote Sensing, 47(3), 701-708.

Nebiker, S., Annen, A., Scherrer, M., Oesch, D., 2008. A light-weight multispectral sensor for micro UAV—Opportunities for very high resolution airborne remote sensing. In International Archives of the Photogrammetry, Remote Sensing and Spatial InformationSciences; International Society for Photogrammetry and Remote Sensing (ISPRS), Beijing, China 37, (B1).

Nex, F., & Remondino, F., 2014. UAV for 3D mapping application A review, Appl.Geomat., 6, 1–15.

Nonami, K., 2007. Prospect and recent research & development for civil use autonomous unmanned aircraft as UAV and MAV. Journal of system Design and Dynamics, 1(2), 120¬-128.

Pena-Barragan, J. M., Lopez-Granados, F., Garcia-Torres, L., Jurado-Exposito, M., de la Orden, M. S., & Garcia-Ferrer, A., 2008. Discriminating cropping systems and agro-environmental measures by remote sensing. Agronomy for Sustainable Development, 28, 355–362.

Perculija, G., Bošnjak., K., Vranic, M., Leto, J. & Kutnjak, H., 2015. Potential of aerialrobotics in crop production, high resolution NIR/VIS imagery obtained by automatedunmanned aerial vehicle (UAV) in estimation of botanical composition of alfalfagrassmixture. Paper presented at the 50th Croatian and 10th International Symposium on Agriculture.

Press Information Bureau Government of India Ministry of Agriculture, 2015, KISAN project, http,//pib.nic.in/newsite/PrintRelease.aspx?relid=128429

Price, P., 2004. Spreading the PA message. Ground Cover, Issue 51 Grains Research and Development Corporation, Canberra, Australia Capital Territory, Australia.

Primicerio, J., Di Gennaro, S., Fiorillo, E., Genesio, L., Lugato, E., Matese, A., & Vaccari, F.P., 2012. A flexible unmanned aerial vehicle for precision agriculture. Precis. Agric., 13, 517–523.

Quilter, M. C., 1997. Vegetation monitoring using low altitude, large scale imagery from radio controlled drones. PhD dissertation, Department of Botany and Range Science, Brigham Young University, Provo, UT, USA.

Quilter, M. C., & V.J.Anderson, 2001. A proposed method for determining shrub utilization using (LA/LS) imagery. Journal of Range Management, 54,378-381.

Rajvanshi, Dr. Anil K., 2016. Precision Agriculture Could Start A Green Revolution in India, www.huffingtonpost.in/dr-anil-k-rajvanshi/precision-agriculture-can_b_6845378.html,

Rango, A., Laliberte, A. S., Herrick, J. E., Winters, C., Havstad, K., Steele, C., 2009. Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management. Journal of Applied Remote Sensing, 3033542.

Rise Above Custom drone solution, 2016. Agriculture Drones. http,//www.riseabove.com.au/agriculture-drones/.

Robertson, M., Carberry, P., & Brennan, L., 2007. The economic benefits of precision agriculture, cast studies from Australia grain farms, Retrieved March 12, 2012, from http,//www.grdc.com.au/uploads/documents/Economics%20of%20Precision%20agriculture%20Report%20to%20GRDC%20final.pdf.

Rouse, J. W., Jr., Hass, R. H., Schell, J. A., & Deering, D. W., 1973. Monitoring vegetation systems in the Great Plains with ERTS, In Proceedings 3rd Earth Resources Technology Satellite (ERTS) symposium, Washington, DC, USA, NASA SP-351, NASA, 1,309-317.

Santhosh, K.S., Soizik, L., Grant, M.C. & George, A.S. , 2003. Remote sensing applications for precision agriculture, A learning community approach. Remote Sens. Environmen, 88(1-2), 157–169.

Sampaio, C. B., A.C. Hernandes, M. Becker, F.M. Catalano, F. Zanini, J.L.Nobrega, & J. Martins, 2014. Novel hybrid electric motor gliderquadrotor MAV for inflight/ VSTOL launching. Paper presented at the Aerospace Conference IEEE.

Sato, A., 2003. The rmax helicopter uav. DTIC Document.

Sebastian Candiago , Fabio Remondino ,Michaela De Giglio ,Marco Dubbini , & Mario Gattelli, 2015. Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images. Remote Sensing, 7(4), 4026-4047, doi,10.3390/rs70404026.

Seelan, S. K., Laguette, S., Casady, G. M., & Seielstad, G. A., 2003. Remote sensing applications for precision agriculture, A learning community approach. Remote Sensing of Environment, 88, 157–169.

Shenghui Fang, Wenchao Tang , Yi Peng , Yan Gong , Can Dai, Ruhui Chai and Kan Liu, 2016. Remote Estimation of Vegetation Fraction and Flower Fraction in Oilseed Rape with Unmanned Aerial Vehicle Data Remote.Sensors, 8,416-435

Spanoudakis, P., Doitsidis, L., Tsourveloudis, N. & Valavanis, K., 2003. Vertical TakeOff and Landing vehicle market overview. KOREA, 1,4.

Stafford, J. V., 2000. Implementing precision agriculture in the 21st century. Journal of Agricultural Engineering Research, 76, 267–275.

Stefanakis, D.; Hatzopoulous, J.N.; Margaris, N.S.; Danalatos, N.G., 2013. Creation of a remote sensing unmanned aerial system (UAS) for precision agriculture and related mapping applications, In Proceedings of 2013 ASPRS Annual Conference, Baltimore, MD, USA, 24–28 Marrch 2013, p. 13.

Sugiura, R., T. Fukagawa, N. Noguchi, K. Ishii, Y. Shibata, & K. Toriyama., 2003. Field information system using an agricultural helicopter towards precision farming. IEEE/ASME Proceedings.Advanced Intelligent Mechatronics AIM 2003.

Sugiura, R.; Noguchi, N.; Ishii, K., 2005. Remote-sensing technology for vegetation monitoring using an unmanned helicopter. Biosyst. Eng., 90,369–379.

Sullivan, D. G., Shaw, J. N., & Rickman, D., 2005. IKONOS imagery to estimate surface soil propertyvariability in two Alabama physiographies. Soil Science Society of America Journal, 69,1789–1798.

Sullivan D. G., J. P. Fulton, J. N. Shaw, & G. Bland, 2007. Evaluating the Sensitivity of an Unmanned Thermal Infrared Aerial System to Detect Water Stress in a CottonCanopy. American Society of Agricultural and Biological Engineers, 50(6), 1955-1962, ISSN 0001-2351.

Swain, K. C., Jayasuriya, H. P. W., & Salokhe, V. M., 2007. Suitability of low-altitude remote sensing images for estimating nitrogen treatment variations in rice cropping for precision agriculture adoption. Journal of Applied Remote Sensing, 1, 013547 , http,//dx.doi.org/10.1117/1.2824287

Swain, K.C., S. J. Thomson, & H. P. W. Jayasuriya, 2010, Adoption of an Unmanned Helicopter for Low­Altitude Remote Sensing to Estimate Yield and Total Biomass of a Rice Crop. American Society of Agricultural and Biological Engineers, 53(1), 21-27 , ISSN 2151-0032.

Tamouridou, A., T. K. Alexandridis, X. E. Pantazi, A. L. Lagopodi, J. Kashefi, & D. Moshou, 2017. Evaluation of UAV imagery for mapping Silybummarianum weed patches. International Journal of Remote Sensing, 38(8-10), 2246-2259.

Tenkorang, F., & DeBoer, L., 2007. On-farm profitability of remote sensing in agriculture. Journal of Terrestrial Observation, 1, 50–59.

Tice, Brian P., 1991. "Unmanned Aerial Vehicles – The Force Multiplier of the

s". Airpower Journal, Archived from the original on 24 July 2009, Retrieved 6

June 2013.

Tomlins, G., & Y. Lee., 1983. Remotely Piloted Aircraft—An Inexpensive Option for LargeScale Aerial Photography in Forestry Applications, Canadian journal of remote sensing, 9(2), 76-85.

Turner, D., A.Lucieer,& C.Watson, 2011. A flexible unmanned aerial vehicle for precision agriculture. In Proceedings of 34th International Symposium on Remote Sensing of Environment, Sydney, Australia.

Ugur Ozdemir, Yucel Orkut Aktas, Karaca Demirbag, Ahmet Erdem Ganime Duygu Kalaycıoglu, Ibrahim Ozkol, & Gokhan Inalhan, 2014. Design of a Commercial Hybrid VTOL UAV System. Journal of Intelligent & Robotic Systems, 74 (1), 371–393.

Unmanned Aircraft System, 2016. "ICAO's circular 328 AN/190 , Unmanned Aircraft Systems" (PDF), ICAO, www.icao.int/Meetings/UAS/Documents/Circular%20328_en.pdf.

Vega Francisco Agu ̈ era, Fernando Carvajal Ramı ́rez ,Monica P erez Saiz, & Francisco Orgaz Rosu, 2015. Multi-temporal imaging using an unmanned aerial vehicle for monitoring sunflower crop. Biosystems Engineering, 132, 19–27.

Warren, G., & Metternicht, G., 2005. Agricultural applications of high-resolution digital multispectral imagery, Evaluating within-field spatial variability of canola (Brassica napus) in Western Australia. Photogrammetric Engineering and Remote Sensing, 71, 595–602.

Wu, J. D., Wang, D., & Bauer, M. E., 2007a. Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies. Field Crops Research, 102, 33–42.

Yang, C., Everitt, J. H., Bradford, J. M., & Escobar, D. E., 2000. Mapping grain sorghum growth and yield variations using airborne multispectral digital imagery. Transactions of the ASAE, 43, 1927-1938.

Yao, H. L., Tang, L., Tian, Brown, R. L., Bhatnagar, D., & Cleveland, T. E., 2010, Using hyperspectral data in precision farming applications, Ch. 25, In P. S. Thenkabail, J. G. Lyon, & A. Huete (Eds.), Hyperspectral remote sensing of vegetation (pp. 705), Boca Raton, FL, CRC Press.

Xiang, H., & L.Tian, 2011b . Method for automatic georeferencing aerial remotensensing (RS) images from an unmanned aerial vehicle (UAV) platform. Biosystems Engineering, 108(2), 104-113.

Zarco-Tejada, P. J., Gonzalez-Dugo, V., & Berni, J. A. J., 2012. Fluorescence, temperature and narrowband indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sensing of Environment., 117, 322–337.

ZarcoTejada, P. J., R. DiazVarela, V. Angileri, & P. Loudjani, 2014. Tree height antification using very high resolution imagery acquired from an unmanned aerialVehicle (UAV) and automatic 3D photo reconstruction methods. European Journal of Agronomy, 55, 89-99, doi, http,//dx.doi.org/10.1016/j.eja.2014.01.004.

Zhang, J. H., Wang, K., Bailey, J. S., & Wang, R. C., 2006. Predicting nitrogen status of rice using multispectral data at canopy scale. Pedosphere., 16, 108–117.

Zhao, D. H., Huang, L. M., Li, J. L., & Qi, J. G. 2007. A comparative analysis of broadband and narrowband derived vegetation indices in predicting LAI and CCD of a cotton canopy. ISPRS Journal of Photogrammetry and Remote Sensing, 62, 25–33.


DOI: http://dx.doi.org/10.12895/jaeid.20172.690