메뉴 건너뛰기




Volumn 143, Issue , 2017, Pages 23-37

A review on the practice of big data analysis in agriculture

Author keywords

Agriculture; Big data analysis; Smart Farming; Survey

Indexed keywords

BIG DATA; DATA HANDLING; DATA MINING; ECOSYSTEMS;

EID: 85030748787     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2017.09.037     Document Type: Review
Times cited : (641)

References (108)
  • 1
    • 85167071536 scopus 로고    scopus 로고
    • [Online] Available at: <> (accessed 2017).
    • AgGateway, 2005. [Online] Available at: < http://www.aggateway.org/Home.aspx> (accessed 2017).
    • (2005)
  • 2
    • 85167048610 scopus 로고    scopus 로고
    • Bringing Mobile Wallets to Nigerian Farmers. [Online] Available at: <> (accessed 2017).
    • Akinboro, B., 2016. Bringing Mobile Wallets to Nigerian Farmers. [Online] Available at: < http://www.cgap.org/blog/bringing-mobile-wallets-nigerian-farmers> (accessed 2017).
    • (2016)
    • Akinboro, B.1
  • 3
    • 85167013082 scopus 로고    scopus 로고
    • [Online] Available at: <> (accessed 2017).
    • Anon., 2016. AeroFarms. [Online] Available at: < http://aerofarms.com/> (accessed 2017).
    • (2016)
  • 4
    • 84890119176 scopus 로고    scopus 로고
    • A review of wireless sensors and networks’ applications in agriculture
    • Aqeel ur, R., Abbasi, A., Islam, N., Shaikh, Z., A review of wireless sensors and networks’ applications in agriculture. Comput. Stand. Interfaces 36:2 (2014), 263–270.
    • (2014) Comput. Stand. Interfaces , vol.36 , Issue.2 , pp. 263-270
    • Aqeel ur, R.1    Abbasi, A.2    Islam, N.3    Shaikh, Z.4
  • 5
    • 85167070172 scopus 로고    scopus 로고
    • Informatics to Support International Food Safety. s.l., s.n.
    • Armbruster, W.J., MacDonell, M.M., 2014. Informatics to Support International Food Safety. s.l., s.n., pp. 127–134.
    • (2014) , pp. 127-134
    • Armbruster, W.J.1    MacDonell, M.M.2
  • 6
    • 85167015958 scopus 로고    scopus 로고
    • The application of data mining techniques to characterize agricultural soil profiles. s.l., s.n.
    • Armstrong, L., Diepeveen, D., Maddern, R., 2007. The application of data mining techniques to characterize agricultural soil profiles. s.l., s.n.
    • (2007)
    • Armstrong, L.1    Diepeveen, D.2    Maddern, R.3
  • 7
    • 84874788283 scopus 로고    scopus 로고
    • Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs
    • Atzberger, C., Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs. Remote Sens. 5:2 (2013), 949–981.
    • (2013) Remote Sens. , vol.5 , Issue.2 , pp. 949-981
    • Atzberger, C.1
  • 8
    • 85167055606 scopus 로고    scopus 로고
    • [Online] Available at: <> (accessed 2017).
    • aWhere Inc., 2015. [Online] Available at: < http://www.awhere.com/> (accessed 2017).
    • (2015)
  • 9
    • 85167030658 scopus 로고    scopus 로고
    • The New World economy, s.l.: Report addressed to Ms Segolene Royal, Minister of Environment, Sustainable Development and Energy, working group led by Corinne Lepage.
    • Babinet, Gilles et al., 2015. The New World economy, s.l.: Report addressed to Ms Segolene Royal, Minister of Environment, Sustainable Development and Energy, working group led by Corinne Lepage.
    • (2015)
    • Gilles, B.1
  • 10
    • 84902972855 scopus 로고    scopus 로고
    • Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches
    • Barrett, B., Nitze, I., Green, S., Cawkwell, F., Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches. Remote Sens. Environ. 152:2 (2014), 109–124.
    • (2014) Remote Sens. Environ. , vol.152 , Issue.2 , pp. 109-124
    • Barrett, B.1    Nitze, I.2    Green, S.3    Cawkwell, F.4
  • 11
    • 0035047573 scopus 로고    scopus 로고
    • Spatial validation of crop models for precision agriculture
    • Basso, B., et al. Spatial validation of crop models for precision agriculture. Agric. Syst. 68:2 (2001), 97–112.
    • (2001) Agric. Syst. , vol.68 , Issue.2 , pp. 97-112
    • Basso, B.1
  • 12
    • 0033781223 scopus 로고    scopus 로고
    • Remote sensing for irrigated agriculture: examples from research and possible applications
    • Bastiaanssen, W., Molden, D., Makin, I., Remote sensing for irrigated agriculture: examples from research and possible applications. Agric. Water Manage. 46:2 (2000), 137–155.
    • (2000) Agric. Water Manage. , vol.46 , Issue.2 , pp. 137-155
    • Bastiaanssen, W.1    Molden, D.2    Makin, I.3
  • 13
    • 84860392830 scopus 로고    scopus 로고
    • Monitoring global croplands with coarse resolution earth observations: the Global Agriculture Monitoring (GLAM) project
    • Becker-Reshef, I., et al. Monitoring global croplands with coarse resolution earth observations: the Global Agriculture Monitoring (GLAM) project. Remote Sens. 2:6 (2010), 1589–1609.
    • (2010) Remote Sens. , vol.2 , Issue.6 , pp. 1589-1609
    • Becker-Reshef, I.1
  • 14
    • 85167039107 scopus 로고
    • Soil-terrain modeling for site-specific agricultural management. Site-Specific Management for Agricultural Systems, American Society of Agronomy, Crop Science Society of America, Soil Science Society of America
    • Bell, J., Butler, C., Thompson, J., 1995. Soil-terrain modeling for site-specific agricultural management. Site-Specific Management for Agricultural Systems, American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, pp. 209–227.
    • (1995) , pp. 209-227
    • Bell, J.1    Butler, C.2    Thompson, J.3
  • 15
    • 85167068112 scopus 로고    scopus 로고
    • [Online] Available at: <> (accessed 2017).
    • Blue River Technology, 2011. [Online] Available at: < http://www.bluerivert.com/> (accessed 2017).
    • (2011)
  • 16
    • 4544311272 scopus 로고    scopus 로고
    • Precision agriculture and sustainability
    • Bongiovanni, R., Lowenberg-DeBoer, J., Precision agriculture and sustainability. Precision Agric. 5:4 (2004), 359–387.
    • (2004) Precision Agric. , vol.5 , Issue.4 , pp. 359-387
    • Bongiovanni, R.1    Lowenberg-DeBoer, J.2
  • 17
    • 85166996421 scopus 로고    scopus 로고
    • Big data comes to the farm, sowing mistrust: seed makers barrel into technology business, s.l.: Wall Street Journal (Online).
    • Bunge, J., 2014. Big data comes to the farm, sowing mistrust: seed makers barrel into technology business, s.l.: Wall Street Journal (Online).
    • (2014)
    • Bunge, J.1
  • 18
    • 85167051315 scopus 로고    scopus 로고
    • Next generation data systems and knowledge products to support agricultural producers and science-based policy decision making
    • Capalbo, S.M., Antle, J.M., Seavert, C., Next generation data systems and knowledge products to support agricultural producers and science-based policy decision making. Agric. Syst., 2016.
    • (2016) Agric. Syst.
    • Capalbo, S.M.1    Antle, J.M.2    Seavert, C.3
  • 19
    • 85014058557 scopus 로고    scopus 로고
    • The ethics of big data in big agriculture
    • Carbonell, I., The ethics of big data in big agriculture. Internet Policy Rev. 5:1 (2016), 1–13.
    • (2016) Internet Policy Rev. , vol.5 , Issue.1 , pp. 1-13
    • Carbonell, I.1
  • 20
    • 85017078900 scopus 로고    scopus 로고
    • Publicising food: big data, precision agriculture, and co-experimental techniques of addition
    • Carolan, M., Publicising food: big data, precision agriculture, and co-experimental techniques of addition. Soc. Ruralis, 2016.
    • (2016) Soc. Ruralis
    • Carolan, M.1
  • 21
    • 0038054588 scopus 로고    scopus 로고
    • AP – animal production technology: recognition system for pig cough based on probabilistic neural networks
    • Chedad, A., et al. AP – animal production technology: recognition system for pig cough based on probabilistic neural networks. J. Agric. Eng. Res. 79:4 (2001), 449–457.
    • (2001) J. Agric. Eng. Res. , vol.79 , Issue.4 , pp. 449-457
    • Chedad, A.1
  • 22
    • 84994297166 scopus 로고    scopus 로고
    • Big data for remote sensing: challenges and opportunities
    • Chi, M., et al. Big data for remote sensing: challenges and opportunities. Proc. IEEE 104:11 (2016), 2207–2219.
    • (2016) Proc. IEEE , vol.104 , Issue.11 , pp. 2207-2219
    • Chi, M.1
  • 23
    • 84890315265 scopus 로고    scopus 로고
    • Big data in life cycle assessment
    • Cooper, J., et al. Big data in life cycle assessment. J. Ind. Ecol. 17:6 (2013), 796–799.
    • (2013) J. Ind. Ecol. , vol.17 , Issue.6 , pp. 796-799
    • Cooper, J.1
  • 24
    • 85166995285 scopus 로고    scopus 로고
    • [Online] Available at: <> (accessed 2017).
    • Cropster, 2007. [Online] Available at: < https://www.cropster.com/> (accessed 2017).
    • (2007)
  • 25
    • 84912097596 scopus 로고    scopus 로고
    • The potential and uptake of remote sensing in insurance: a review
    • de Leeuw, J., et al. The potential and uptake of remote sensing in insurance: a review. Remote Sens. 6:11 (2014), 10888–10912.
    • (2014) Remote Sens. , vol.6 , Issue.11 , pp. 10888-10912
    • de Leeuw, J.1
  • 26
    • 34547525049 scopus 로고    scopus 로고
    • Comparison of genetic algorithm and neural network approaches for the drying process of carrot
    • Erenturk, S., Erenturk, K., Comparison of genetic algorithm and neural network approaches for the drying process of carrot. J. Food Eng. 78:3 (2007), 905–912.
    • (2007) J. Food Eng. , vol.78 , Issue.3 , pp. 905-912
    • Erenturk, S.1    Erenturk, K.2
  • 27
    • 85167031029 scopus 로고    scopus 로고
    • Farm Hack. Farm Hack. [Online] Available at: <> (accessed January 2017).
    • Farm Hack, 2010. Farm Hack. [Online] Available at: < http://farmhack.org> (accessed January 2017).
    • (2010)
  • 28
    • 85166993954 scopus 로고    scopus 로고
    • Field to Market. Fieldprint Calculator. [Online] Available at: <> (accessed 2017).
    • Field to Market, 2015. Fieldprint Calculator. [Online] Available at: < https://www.fieldtomarket.org/fieldprint-calculator/> (accessed 2017).
    • (2015)
  • 29
    • 85166997424 scopus 로고    scopus 로고
    • Food and Agriculture Organization of the United Nations. How to Feed the World in 2050., Rome: s.n.
    • Food and Agriculture Organization of the United Nations, 2009. How to Feed the World in 2050., Rome: s.n.
    • (2009)
  • 30
    • 84954498894 scopus 로고    scopus 로고
    • Drivers of household food availability in sub-Saharan Africa based on big data from small farms
    • Frelat, R., et al. Drivers of household food availability in sub-Saharan Africa based on big data from small farms. Proc. Natl. Acad. Sci. 113:2 (2016), 458–463.
    • (2016) Proc. Natl. Acad. Sci. , vol.113 , Issue.2 , pp. 458-463
    • Frelat, R.1
  • 31
    • 85167009848 scopus 로고    scopus 로고
    • Drought and retribution: evidence from a large scale rainfall index insurance in Mexico. s.l., s.n.
    • Fuchs, A., Wolff, H., 2011. Drought and retribution: evidence from a large scale rainfall index insurance in Mexico. s.l., s.n., pp. 13–14.
    • (2011) , pp. 13-14
    • Fuchs, A.1    Wolff, H.2
  • 32
    • 83055180602 scopus 로고    scopus 로고
    • Phenomics – technologies to relieve the phenotyping bottleneck
    • Furbank, R.T., Teste, M., Phenomics – technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 16:12 (2011), 635–644.
    • (2011) Trends Plant Sci. , vol.16 , Issue.12 , pp. 635-644
    • Furbank, R.T.1    Teste, M.2
  • 33
    • 38049035787 scopus 로고    scopus 로고
    • Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil
    • Galford, G.L., et al. Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil. Remote Sens. Environ. 112:2 (2008), 576–587.
    • (2008) Remote Sens. Environ. , vol.112 , Issue.2 , pp. 576-587
    • Galford, G.L.1
  • 34
    • 76749142575 scopus 로고    scopus 로고
    • Precision agriculture and food security
    • Gebbers, R., Adamchuk, V., Precision agriculture and food security. Science 327:5967 (2010), 828–831.
    • (2010) Science , vol.327 , Issue.5967 , pp. 828-831
    • Gebbers, R.1    Adamchuk, V.2
  • 35
    • 78651244294 scopus 로고    scopus 로고
    • Communicating complexity: integrated assessment of trade-offs concerning soil fertility management within African farming systems to support innovation and development
    • Giller, K., et al. Communicating complexity: integrated assessment of trade-offs concerning soil fertility management within African farming systems to support innovation and development. Agric. Syst. 104:1 (2011), 191–203.
    • (2011) Agric. Syst. , vol.104 , Issue.1 , pp. 191-203
    • Giller, K.1
  • 36
    • 85167004235 scopus 로고    scopus 로고
    • Global Envision. Unleashing Ugandan farmers’ potential through mobile phones. [Online] Available at: <> (accessed 2017).
    • Global Envision, 2006. Unleashing Ugandan farmers’ potential through mobile phones. [Online] Available at: < https://www.mercycorps.org/research-resources/unleashing-ugandan-farmers-potential-through-mobile-phones> (accessed 2017).
    • (2006)
  • 37
    • 85166991710 scopus 로고    scopus 로고
    • Global Open Data for Agriculture and Nutrition (GODAN) initiative. [Online] Available at: <> (accessed 2017).
    • GODAN, 2015. Global Open Data for Agriculture and Nutrition (GODAN) initiative. [Online] Available at: < http://www.godan.info/> (accessed 2017).
    • (2015)
  • 38
    • 85053361262 scopus 로고    scopus 로고
    • Past, present and future of epigenetics applied to livestock breeding
    • González-Recio, O., Toro, M., Bach, A., Past, present and future of epigenetics applied to livestock breeding. Front. Genet., 6, 2015, 305.
    • (2015) Front. Genet. , vol.6 , pp. 305
    • González-Recio, O.1    Toro, M.2    Bach, A.3
  • 39
    • 85167039433 scopus 로고    scopus 로고
    • Reducing and Managing Food Scares, Washington, DC: International Food Policy Research, 2014–2015 Global Food Policy Report.
    • Grace, D., McDermot, J., 2015. Reducing and Managing Food Scares, Washington, DC: International Food Policy Research, 2014–2015 Global Food Policy Report.
    • (2015)
    • Grace, D.1    McDermot, J.2
  • 40
    • 85167051742 scopus 로고    scopus 로고
    • mAgri Programme. [Online] Available at: <> (accessed 2017).
    • GSMA, 2014. mAgri Programme. [Online] Available at: < http://www.gsma.com/mobilefordevelopment/programmes/magri/programme-overview> (accessed 2017).
    • (2014)
  • 41
    • 53549084584 scopus 로고    scopus 로고
    • Logistic regression product-unit neural networks for mapping Ridolfia segetum infestations in sunflower crop using multitemporal remote sensed data
    • Gutiérrez, P., et al. Logistic regression product-unit neural networks for mapping Ridolfia segetum infestations in sunflower crop using multitemporal remote sensed data. Comput. Electron. Agric. 64:2 (2008), 293–306.
    • (2008) Comput. Electron. Agric. , vol.64 , Issue.2 , pp. 293-306
    • Gutiérrez, P.1
  • 42
    • 84901594468 scopus 로고    scopus 로고
    • Precise plant breeding using new genome editing techniques: opportunities, safety and regulation in the EU
    • Hartung, F., Schiemann, J., Precise plant breeding using new genome editing techniques: opportunities, safety and regulation in the EU. Plant J. 78:5 (2014), 742–752.
    • (2014) Plant J. , vol.78 , Issue.5 , pp. 742-752
    • Hartung, F.1    Schiemann, J.2
  • 43
    • 84907325157 scopus 로고    scopus 로고
    • The rise of “big data” on cloud computing: review and open research issues
    • Hashem, I., et al. The rise of “big data” on cloud computing: review and open research issues. Inform. Syst. 47 (2015), 98–115.
    • (2015) Inform. Syst. , vol.47 , pp. 98-115
    • Hashem, I.1
  • 45
    • 85015255850 scopus 로고    scopus 로고
    • Agri-IoT: A Semantic Framework for Internet of Things-Enabled Smart Farming Applications
    • Reston VA, USA, s.n.
    • Kamilaris, A., Gao, F., Prenafeta-Boldú F.X., Ali, M.I., Agri-IoT: A Semantic Framework for Internet of Things-Enabled Smart Farming Applications. 2016, Reston, VA, USA, s.n.
    • (2016)
    • Kamilaris, A.1    Gao, F.2    Prenafeta-Boldú, F.X.3    Ali, M.I.4
  • 46
    • 85167002923 scopus 로고    scopus 로고
    • Online analysis of remote sensing data for agricultural applications. s.l., OSGeo's European conference on free and open source software for geospatial.
    • Karmas, A., Karantzalos, K., Athanasiou, S., 2014. Online analysis of remote sensing data for agricultural applications. s.l., OSGeo's European conference on free and open source software for geospatial.
    • (2014)
    • Karmas, A.1    Karantzalos, K.2    Athanasiou, S.3
  • 49
    • 84897562213 scopus 로고    scopus 로고
    • Big-data applications in the government sector
    • Kim, G.-H., Trimi, S., Chung, J.-H., Big-data applications in the government sector. Commun. ACM 57:3 (2014), 78–85.
    • (2014) Commun. ACM , vol.57 , Issue.3 , pp. 78-85
    • Kim, G.-H.1    Trimi, S.2    Chung, J.-H.3
  • 50
    • 40949087118 scopus 로고    scopus 로고
    • Shrink and share: humanity's present and future ecological footprint
    • Kitzes, J., et al. Shrink and share: humanity's present and future ecological footprint. Philos. Trans. Royal Soc. B: Biol. Sci. 363:1491 (2008), 467–475.
    • (2008) Philos. Trans. Royal Soc. B: Biol. Sci. , vol.363 , Issue.1491 , pp. 467-475
    • Kitzes, J.1
  • 51
    • 84983746515 scopus 로고    scopus 로고
    • The emerging role of Big Data in key development issues: opportunities, challenges, and concerns
    • Kshetri, N., The emerging role of Big Data in key development issues: opportunities, challenges, and concerns. Big Data Soc., 1(2), 2014.
    • (2014) Big Data Soc. , vol.1 , Issue.2
    • Kshetri, N.1
  • 52
    • 75249096363 scopus 로고    scopus 로고
    • A review: the role of remote sensing in precision agriculture
    • Liaghat, S., Balasundram, S.K., A review: the role of remote sensing in precision agriculture. Am. J. Agric. Biol. Sci. 5:1 (2010), 50–55.
    • (2010) Am. J. Agric. Biol. Sci. , vol.5 , Issue.1 , pp. 50-55
    • Liaghat, S.1    Balasundram, S.K.2
  • 53
    • 84982822155 scopus 로고    scopus 로고
    • Analysis of Big Data technologies for use in agro-environmental science
    • Lokers, R., et al. Analysis of Big Data technologies for use in agro-environmental science. Environ. Model. Software 84 (2016), 494–504.
    • (2016) Environ. Model. Software , vol.84 , pp. 494-504
    • Lokers, R.1
  • 54
    • 84904315355 scopus 로고    scopus 로고
    • An international survey of aquaponics practitioners
    • Love, D.C., et al. An international survey of aquaponics practitioners. PLoS ONE, 9(7), 2014.
    • (2014) PLoS ONE , vol.9 , Issue.7
    • Love, D.C.1
  • 55
    • 3042598055 scopus 로고    scopus 로고
    • Applications of location analysis in agriculture: a survey
    • Lucas, M.T., Chhajed, D., Applications of location analysis in agriculture: a survey. J. Operational Res. Soc. 55:6 (2004), 561–578.
    • (2004) J. Operational Res. Soc. , vol.55 , Issue.6 , pp. 561-578
    • Lucas, M.T.1    Chhajed, D.2
  • 56
    • 84915785146 scopus 로고    scopus 로고
    • Machine learning for Big Data analytics in plants
    • Ma, C., Zhang, H.H., Wang, X., Machine learning for Big Data analytics in plants. Trends Plant Sci. 19:12 (2014), 798–808.
    • (2014) Trends Plant Sci. , vol.19 , Issue.12 , pp. 798-808
    • Ma, C.1    Zhang, H.H.2    Wang, X.3
  • 58
    • 0035499301 scopus 로고    scopus 로고
    • Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement
    • Marcot, B.G., et al. Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement. For. Ecol. Manage. 153:1 (2001), 29–42.
    • (2001) For. Ecol. Manage. , vol.153 , Issue.1 , pp. 29-42
    • Marcot, B.G.1
  • 60
    • 1142290141 scopus 로고    scopus 로고
    • Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images
    • Meyer, G., Neto, J.C., Jones, D.D., Hindman, T.W., Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images. Comput. Electron. Agric. 42:3 (2004), 161–180.
    • (2004) Comput. Electron. Agric. , vol.42 , Issue.3 , pp. 161-180
    • Meyer, G.1    Neto, J.C.2    Jones, D.D.3    Hindman, T.W.4
  • 61
    • 67650854663 scopus 로고    scopus 로고
    • Data Mining in Agriculture, vol. 34
    • Springer Science & Business Media
    • Mucherino, A., Papajorgji, P., Pardalos, P., Data Mining in Agriculture, vol. 34. 2009, Springer Science & Business Media.
    • (2009)
    • Mucherino, A.1    Papajorgji, P.2    Pardalos, P.3
  • 62
    • 84960412878 scopus 로고    scopus 로고
    • Big and meta data management for U-agriculture mobile services
    • Nandyala, C.S., Kim, H.-K., Big and meta data management for U-agriculture mobile services. Int. J. Software Eng. Appl. (IJSEIA) 10:1 (2016), 257–270.
    • (2016) Int. J. Software Eng. Appl. (IJSEIA) , vol.10 , Issue.1 , pp. 257-270
    • Nandyala, C.S.1    Kim, H.-K.2
  • 63
    • 84923054501 scopus 로고    scopus 로고
    • Big data challenges in building the global earth observation system of systems
    • Nativi, S., et al. Big data challenges in building the global earth observation system of systems. Environ. Model. Software 68:1 (2015), 1–26.
    • (2015) Environ. Model. Software , vol.68 , Issue.1 , pp. 1-26
    • Nativi, S.1
  • 64
    • 85166995683 scopus 로고    scopus 로고
    • Open Agriculture Data Alliance. [Online] Available at: <> (accessed 2017).
    • OADA, 2014. Open Agriculture Data Alliance. [Online] Available at: < http://openag.io/> (accessed 2017).
    • (2014)
  • 65
    • 84858865606 scopus 로고    scopus 로고
    • Institutional innovations for small-holder farmers’ competitiveness in Africa
    • Oluoch-Kosura, W., Institutional innovations for small-holder farmers’ competitiveness in Africa. African J. Agric. Resource Econ. 5:1 (2010), 227–242.
    • (2010) African J. Agric. Resource Econ. , vol.5 , Issue.1 , pp. 227-242
    • Oluoch-Kosura, W.1
  • 66
    • 80051891221 scopus 로고    scopus 로고
    • Remote sensing of irrigated agriculture: opportunities and challenges
    • Ozdogan, M., Yang, Y., Allez, G., Cervantes, C., Remote sensing of irrigated agriculture: opportunities and challenges. Remote Sens. 2:9 (2010), 2274–2304.
    • (2010) Remote Sens. , vol.2 , Issue.9 , pp. 2274-2304
    • Ozdogan, M.1    Yang, Y.2    Allez, G.3    Cervantes, C.4
  • 67
    • 85166999878 scopus 로고    scopus 로고
    • Plantix. [Online] Available at: <> (accessed 2017).
    • PEAT UG, 2016. Plantix. [Online] Available at: < http://plantix.net/> (accessed 2017).
    • (2016)
    • PEAT UG1
  • 68
    • 0001772065 scopus 로고    scopus 로고
    • P. Aspects of precision agriculture.
    • Pierce, F.J., & N., P., 1999. Aspects of precision agriculture. Advances in agronomy, vol. 67, pp. 1–85.
    • (1999) Advances in agronomy , vol.67 , pp. 1-85
    • Pierce, F.J.N.1
  • 69
    • 14944382803 scopus 로고    scopus 로고
    • Combination of support vector machines (SVM) and near-infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds
    • Pierna, J.A., et al. Combination of support vector machines (SVM) and near-infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds. J. Chemom. 18:7–8 (2004), 341–349.
    • (2004) J. Chemom. , vol.18 , Issue.7-8 , pp. 341-349
    • Pierna, J.A.1
  • 70
    • 40949157501 scopus 로고    scopus 로고
    • Agricultural sustainability: concepts, principles and evidence
    • Pretty, J., Agricultural sustainability: concepts, principles and evidence. Philos. Trans. Royal Soc. London B: Biol. Sci. 363:1491 (2008), 447–465.
    • (2008) Philos. Trans. Royal Soc. London B: Biol. Sci. , vol.363 , Issue.1491 , pp. 447-465
    • Pretty, J.1
  • 71
    • 85030787558 scopus 로고    scopus 로고
    • Omics: Applications in Biomedical, Agriculture and Environmental Sciences, s.l.
    • CRC PressI Llc.
    • Rahman, H., Sudheer Pamidimarri, D., Valarmathi, R., Raveendran, M., Omics: Applications in Biomedical, Agriculture and Environmental Sciences, s.l. 2013, CRC PressI Llc.
    • (2013)
    • Rahman, H.1    Sudheer Pamidimarri, D.2    Valarmathi, R.3    Raveendran, M.4
  • 72
    • 85167028120 scopus 로고    scopus 로고
    • RIICE Partnership. Remote sensing-based Information and Insurance for Crops in Emerging economies. [Online] Available at: <> (accessed 2017).
    • RIICE Partnership, 2014. Remote sensing-based Information and Insurance for Crops in Emerging economies. [Online] Available at: < http://www.riice.org/> (accessed 2017).
    • (2014)
  • 73
    • 85010685216 scopus 로고    scopus 로고
    • To mulch or to munch? Big modelling of big data
    • Rodriguez, D., et al. To mulch or to munch? Big modelling of big data. Agric. Syst. 153 (2017), 32–42.
    • (2017) Agric. Syst. , vol.153 , pp. 32-42
    • Rodriguez, D.1
  • 74
    • 21444460280 scopus 로고    scopus 로고
    • A crop phenology detection method using time-series MODIS data
    • Sakamoto, T., et al. A crop phenology detection method using time-series MODIS data. Remote Sens. Environ. 96:3 (2005), 366–374.
    • (2005) Remote Sens. Environ. , vol.96 , Issue.3 , pp. 366-374
    • Sakamoto, T.1
  • 75
    • 85030763704 scopus 로고    scopus 로고
    • Organized data and information for efficacious agriculture using PRIDE model
    • Sawant, M., Urkude, R., Jawale, S., Organized data and information for efficacious agriculture using PRIDE model. Int. Food Agribusiness Manage. Rev., 19(A), 2016.
    • (2016) Int. Food Agribusiness Manage. Rev. , vol.19A)
    • Sawant, M.1    Urkude, R.2    Jawale, S.3
  • 76
    • 84878141258 scopus 로고    scopus 로고
    • Agricultural innovation to protect the environment
    • Sayer, J., Cassman, K., Agricultural innovation to protect the environment. Proc. Natl. Acad. Sci. U.S.A. 110:21 (2013), 8345–8348.
    • (2013) Proc. Natl. Acad. Sci. U.S.A. , vol.110 , Issue.21 , pp. 8345-8348
    • Sayer, J.1    Cassman, K.2
  • 77
    • 85061683486 scopus 로고    scopus 로고
    • MERRA analytic services: meeting the big data challenges of climate science through cloud-enabled climate analytics-as-a-service
    • Schnase, J.L., et al. MERRA analytic services: meeting the big data challenges of climate science through cloud-enabled climate analytics-as-a-service. Comput. Environ. Urban Syst., 2014.
    • (2014) Comput. Environ. Urban Syst.
    • Schnase, J.L.1
  • 78
    • 85167059053 scopus 로고    scopus 로고
    • Infrastructure for data-driven agriculture: identifying management zones for cotton using statistical modeling and machine learning techniques. s.l., IEEE.
    • Schuster, E.W. et al., 2011. Infrastructure for data-driven agriculture: identifying management zones for cotton using statistical modeling and machine learning techniques. s.l., IEEE.
    • (2011)
    • Schuster, E.W.1
  • 79
    • 85007376693 scopus 로고    scopus 로고
    • Big data ethics and the digital age of agriculture
    • Schuster, J., Big data ethics and the digital age of agriculture. Am. Soc. Agric. Biol. Eng. 24:1 (2017), 20–21.
    • (2017) Am. Soc. Agric. Biol. Eng. , vol.24 , Issue.1 , pp. 20-21
    • Schuster, J.1
  • 80
    • 0026272518 scopus 로고
    • Sustainable Agriculture: definitions and parameters for measurement
    • Senanayake, R., Sustainable Agriculture: definitions and parameters for measurement. J. Sustain. Agric. 1:4 (1991), 7–28.
    • (1991) J. Sustain. Agric. , vol.1 , Issue.4 , pp. 7-28
    • Senanayake, R.1
  • 81
    • 85167058151 scopus 로고    scopus 로고
    • SenseFly. Drone applications in agriculture. [Online] Available at: <> (accessed 2017).
    • SenseFly, 2012. Drone applications in agriculture. [Online] Available at: < https://www.sensefly.com/applications/agriculture.html> (accessed 2017).
    • (2012)
  • 82
    • 84912544750 scopus 로고    scopus 로고
    • Ecological views of big data: perspectives and issues
    • Shin, D.-H., Choi, M.J., Ecological views of big data: perspectives and issues. Telematics Inform. 32:2 (2015), 311–320.
    • (2015) Telematics Inform. , vol.32 , Issue.2 , pp. 311-320
    • Shin, D.-H.1    Choi, M.J.2
  • 83
    • 84963935784 scopus 로고    scopus 로고
    • Climate and famines: a historical reassessment
    • Slavin, P., Climate and famines: a historical reassessment. Wiley Interdiscipl. Rev.: Clim. Change 7:3 (2016), 433–447.
    • (2016) Wiley Interdiscipl. Rev.: Clim. Change , vol.7 , Issue.3 , pp. 433-447
    • Slavin, P.1
  • 84
    • 85053946874 scopus 로고    scopus 로고
    • Environmental performance evaluation with big data: theories and methods
    • Song, M.-L., Fisher, R., Wang, J.-L., Cui, L.-B., Environmental performance evaluation with big data: theories and methods. Ann. Oper. Res., 2016, 1–14.
    • (2016) Ann. Oper. Res. , pp. 1-14
    • Song, M.-L.1    Fisher, R.2    Wang, J.-L.3    Cui, L.-B.4
  • 85
    • 85018338553 scopus 로고    scopus 로고
    • Big data: fueling the next evolution of agricultural innovation
    • Sonka, S., Big data: fueling the next evolution of agricultural innovation. J. Innovation Manage. 4:1 (2016), 114–136.
    • (2016) J. Innovation Manage. , vol.4 , Issue.1 , pp. 114-136
    • Sonka, S.1
  • 86
    • 85030776911 scopus 로고    scopus 로고
    • Big data in agriculture: property rights, privacy and competition in Ag data services
    • Sykuta, M.E., Big data in agriculture: property rights, privacy and competition in Ag data services. Int. Food Agribusiness Manage. Rev. Special Issue, 19(A), 2016.
    • (2016) Int. Food Agribusiness Manage. Rev. Special Issue , vol.19A)
    • Sykuta, M.E.1
  • 87
    • 85166999558 scopus 로고    scopus 로고
    • Syngenta Foundation for Sustainable Agriculture. FarmForce. [Online] Available at: <> [accessed 2017].
    • Syngenta Foundation for Sustainable Agriculture, 2016. FarmForce. [Online] Available at: < http://www.farmforce.com/> [accessed 2017].
    • (2016)
  • 88
    • 85167020071 scopus 로고    scopus 로고
    • Syngenta. Syngenta Foundation for Sustainable Agriculture: Kilimo Salama – An agricultural insurance initiative. [Online] Available at: <> (accessed 2017).
    • Syngenta, 2010. Syngenta Foundation for Sustainable Agriculture: Kilimo Salama – An agricultural insurance initiative. [Online] Available at: < https://kilimosalama.wordpress.com/about/> (accessed 2017).
    • (2010)
  • 90
    • 85021318536 scopus 로고    scopus 로고
    • Targeting drought-tolerant maize varieties in southern Africa: a geospatial crop modeling approach using big data
    • Tesfaye, K., et al. Targeting drought-tolerant maize varieties in southern Africa: a geospatial crop modeling approach using big data. Int. Food Agribusiness Manage. Rev. 19(A) (2016), 1–18.
    • (2016) Int. Food Agribusiness Manage. Rev. , vol.19A) , pp. 1-18
    • Tesfaye, K.1
  • 91
    • 70449436485 scopus 로고    scopus 로고
    • Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium
    • Thenkabail, P., et al. Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium. Int. J. Remote Sens. 30:14 (2009), 3679–3733.
    • (2009) Int. J. Remote Sens. , vol.30 , Issue.14 , pp. 3679-3733
    • Thenkabail, P.1
  • 92
    • 36649033114 scopus 로고    scopus 로고
    • Spectral matching techniques to determine historical land-use/land-cover (LULC) and irrigated areas using time-series 0.1-degree AVHRR Pathfinder datasets
    • Thenkabail, P., et al. Spectral matching techniques to determine historical land-use/land-cover (LULC) and irrigated areas using time-series 0.1-degree AVHRR Pathfinder datasets. Photogram. Eng. Remote Sens. 73:10 (2007), 1029–1040.
    • (2007) Photogram. Eng. Remote Sens. , vol.73 , Issue.10 , pp. 1029-1040
    • Thenkabail, P.1
  • 93
    • 85167020738 scopus 로고    scopus 로고
    • Thomson Reuters. Web of Science. [Online] Available at: <> (accessed 2017).
    • Thomson Reuters, 2017. Web of Science. [Online] Available at: < http://www.webofknowledge.com/> (accessed 2017).
    • (2017)
  • 94
    • 33750018142 scopus 로고    scopus 로고
    • Downscaling of precipitation for climate change scenarios: a support vector machine approach
    • Tripathi, S., Srinivas, V.V., Nanjundiah, R.S., Downscaling of precipitation for climate change scenarios: a support vector machine approach. J. Hydrol. 330:3 (2006), 621–640.
    • (2006) J. Hydrol. , vol.330 , Issue.3 , pp. 621-640
    • Tripathi, S.1    Srinivas, V.V.2    Nanjundiah, R.S.3
  • 95
    • 84991463631 scopus 로고    scopus 로고
    • Towards a second green revolution
    • Tyagi, A.C., Towards a second green revolution. Irrigation Drainage 65:4 (2016), 388–389.
    • (2016) Irrigation Drainage , vol.65 , Issue.4 , pp. 388-389
    • Tyagi, A.C.1
  • 96
    • 34347379313 scopus 로고    scopus 로고
    • Using data mining techniques to predict industrial wine problem fermentations
    • Urtubia, A., Perez-Correa, J., Soto, A., Pszczolkowski, P., Using data mining techniques to predict industrial wine problem fermentations. Food Control 18:1 (2007), 1512–1517.
    • (2007) Food Control , vol.18 , Issue.1 , pp. 1512-1517
    • Urtubia, A.1    Perez-Correa, J.2    Soto, A.3    Pszczolkowski, P.4
  • 97
    • 84890849596 scopus 로고    scopus 로고
    • Applications of image processing in agriculture: a survey
    • Vibhute, A., Bodhe, S.K., Applications of image processing in agriculture: a survey. Int. J. Comput. Appl., 52(2), 2012.
    • (2012) Int. J. Comput. Appl. , vol.52 , Issue.2
    • Vibhute, A.1    Bodhe, S.K.2
  • 98
    • 84910036454 scopus 로고    scopus 로고
    • Web technologies for environmental Big Data
    • Vitolo, C., et al. Web technologies for environmental Big Data. Environ. Model. Software 63:1 (2015), 185–198.
    • (2015) Environ. Model. Software , vol.63 , Issue.1 , pp. 185-198
    • Vitolo, C.1
  • 99
    • 84955254844 scopus 로고    scopus 로고
    • Environmental conditions’ big data management and cloud computing analytics for sustainable agriculture
    • Waga, D., Rabah, K., Environmental conditions’ big data management and cloud computing analytics for sustainable agriculture. World J. Comput. Appl. Technol. 2:3 (2014), 73–81.
    • (2014) World J. Comput. Appl. Technol. , vol.2 , Issue.3 , pp. 73-81
    • Waga, D.1    Rabah, K.2
  • 101
    • 34247523027 scopus 로고    scopus 로고
    • Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains
    • Wardlow, B.D., Egbert, S.L., Kastens, J.H., Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains. Remote Sens. Environ. 108:3 (2007), 290–310.
    • (2007) Remote Sens. Environ. , vol.108 , Issue.3 , pp. 290-310
    • Wardlow, B.D.1    Egbert, S.L.2    Kastens, J.H.3
  • 102
    • 84971221462 scopus 로고    scopus 로고
    • A survey on metaheuristics for optimization in food manufacturing industry
    • Wari, E., Zhu, W., A survey on metaheuristics for optimization in food manufacturing industry. Appl. Soft Comput. 46 (2016), 328–343.
    • (2016) Appl. Soft Comput. , vol.46 , pp. 328-343
    • Wari, E.1    Zhu, W.2
  • 103
    • 84892050370 scopus 로고    scopus 로고
    • Internet of Things
    • Springer New York, NY
    • Weber, R.H., Weber, R., Internet of Things. 2010, Springer, New York, NY.
    • (2010)
    • Weber, R.H.1    Weber, R.2
  • 105
    • 84881406284 scopus 로고    scopus 로고
    • Big Data, Big Impact: New Possibilities for International Development
    • World Economic Forum Geneva, Switzerland
    • World Economic Forum, Big Data, Big Impact: New Possibilities for International Development. 2012, World Economic Forum, Geneva, Switzerland.
    • (2012)
    • World Economic Forum1
  • 106
    • 85167067391 scopus 로고    scopus 로고
    • Big Data Meet Green Challenges: Big Data Toward Green Applications, s.l.: s.n.
    • Wu, J., Guo, S., Li, J., Zeng, D., 2016. Big Data Meet Green Challenges: Big Data Toward Green Applications, s.l.: s.n.
    • (2016)
    • Wu, J.1    Guo, S.2    Li, J.3    Zeng, D.4
  • 107
    • 84865430209 scopus 로고    scopus 로고
    • Opening the archive: how free data has enabled the science and monitoring promise of Landsat
    • Wulder, M., et al. Opening the archive: how free data has enabled the science and monitoring promise of Landsat. Remote Sens. Environ. 122:1 (2012), 2–10.
    • (2012) Remote Sens. Environ. , vol.122 , Issue.1 , pp. 2-10
    • Wulder, M.1
  • 108
    • 84868629775 scopus 로고    scopus 로고
    • The application of small unmanned aerial systems for precision agriculture: a review
    • Zhang, C., Kovacs, J.M., The application of small unmanned aerial systems for precision agriculture: a review. Precision Agric. 13:6 (2012), 693–712.
    • (2012) Precision Agric. , vol.13 , Issue.6 , pp. 693-712
    • Zhang, C.1    Kovacs, J.M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.