Sensor technology, which benefits from high temporal measuring resolution, real-time data transfer and high spatial resolution of sensor data that shows in-field variations, has the potential to provide added value for crop production. The present paper explores how sensors and sensor networks have been utilised in the crop production process and what their added-value and the main bottlenecks are from the perspective of users. The focus is on sensor based applications and on requirements that users pose for them. Literature and two use cases were reviewed and applications were classified according to the crop production process: sensing of growth conditions, fertilising, irrigation, plant protection, harvesting and fleet control. The potential of sensor technology was widely acknowledged along the crop production chain. Users of the sensors require easy-to-use and reliable applications that are actionable in crop production at reasonable costs. The challenges are to develop sensor technology, data interoperability and management tools as well as data and measurement services in a way that requirements can be met, and potential benefits and added value can be realized in the farms in terms of higher yields, improved quality of yields, decreased input costs and production risks, and less work time and load.
References
[1]
Stefanidis, A; Nittel, S. GeoSensor Networks; CRC Press: Boca Raton, FL, USA, 2005.
[2]
Hart, J; Martinez, K. Environmental sensor networks: A revolution in the earth system science? Earth-Sci. Rev 2006, 78, 177–191, doi:10.1016/j.earscirev.2006.05.001.
[3]
Botts, M; Percivall, G; Reed, C; Davidson, J. OGC sensor web enablemant: Overview and high level architecture. In Geo-Sensor Networks; Springer-Verlag: Berlin, Germany, 2008.
[4]
Delin, K. The sensor web: A macro-instrument for coordinated sensing. Sensors 2002, 2, 270–285, doi:10.3390/s20700270.
[5]
Teillet, PM; Chichagov, A; Fedosejevs, G; Gauthier, R; Ainsley, G; Maloley, M; Guimond, M; Nadeau, C; Wehn, H; Shankaie, A; et al. An integrated earth sensing sensorweb for improved crop and rangeland yield predictions. Can. J. Remote Sens 2007, 33, 88–98, doi:10.5589/m07-012.
[6]
Nittel, S; Labrinidis, A; Stefanidis, A. Introduction. In Advances in Geosensor Networks; Springer-Verlag: Berlin, Germany, 2008.
[7]
Tilman, D; Cassman, KG; Matson, PA; Naylor, R; Polasky, S. Agricultural sustainability and intensive production practices. Nature 2002, 418, 671–677, doi:10.1038/nature01014. 12167873
[8]
Thysen, I. Agriculture in the information society. J. Agric. Eng. Res 2000, 76, 297–303, doi:10.1006/jaer.2000.0580.
[9]
Cao, X; Chen, J; Zhang, Y; Sun, Y. Development of an integrated wireless sensor network micro-environmental monitoring system. ISA Trans 2008, 47, 247–255, doi:10.1016/j.isatra.2008.02.001. 18355827
[10]
Pierce, F; Elliott, T. Regional and on-farm wireless sensor networks for agricultural systems in Eastern Washington. Comput. Electon. Agric 2008, 61, 32–43, doi:10.1016/j.compag.2007.05.007.
[11]
Akyildiz, IF; Su, W; Sankarasubramaniam, Y; Cayirci, E. Wireless sensor networks: A survey. Comput. Networks 2002, 38, 393–422, doi:10.1016/S1389-1286(01)00302-4.
[12]
Butler, D. Everything, everywhere. Nature 2006, 7083, 402–405.
[13]
Wang, N; Zhang, N; Wang, M. Wireless sensors in agriculture and food industry-recent development and future perspective. Comput. Electron. Agric 2006, 50, 1–14, doi:10.1016/j.compag.2005.09.003.
[14]
Delin, KA; Jackson, SP; Johnson, DW; Burleigh, SC; Woodrow, RR; McAuley, JM; Dohm, JM; Ip, F; Ferre, TPA; Rucker, DF; et al. Environmental studies with the sensor web: Principles and practice. Sensors 2005, 5, 103–117, doi:10.3390/s5010103.
[15]
Lee, WS; Alchanatis, V; Yang, C; Hirafuji, M; Moshou, D; Li, C; Lee, WS; Alchanatis, V; Yang, C; Hirafuji, M; et al. Sensing technologies for precision specialty crop production. Comput. Electron. Agric 2010, 74, 2–33, doi:10.1016/j.compag.2010.08.005.
[16]
Liang, SHL; Croitoru, A; Vincent Tao, C. A distributed geospatial infrastructure for sensor web. Comput. Geosci 2005, 31, 221–231, doi:10.1016/j.cageo.2004.06.014.
Ruiz-Garcia, L; Lunadei, L; Barreiro, P; Robla, JI. A review of wireless sensor technologies and applications in agriculture and food industry: State of the art and current trends. Sensors 2009, 9, 4728–4750, doi:10.3390/s90604728. 22408551
[19]
Motha, RP; Sivakumar, MVK; Bernardi, M. Strengthening operational agrometeorological services at the national level. Proceedings of the Inter-Regional Workshop, Manila, Philippines, 22–26 March 2004; pp. 237–238.
[20]
Hubbard, KG; Rosenberg, NJ; Nielsen, DC. Automated weather data network for agriculture. J. Water Res 1983, 109, 213–222, doi:10.1061/(ASCE)0733-9496(1983)109:3(213).
[21]
Sivertsen, TH. Weather information, site information and a system for dissemination of information on the worldwide web from a network of 52 automatic agrometeorological stations. EPPO Bull 2000, 30, 77–81, doi:10.1111/j.1365-2338.2000.tb00855.x.
[22]
Hoogenboom, G; Coker, DD; Edenfield, JM; Evans, DM; Fang, C. The Georgia automated environmental monitoring network: Ten years of weather information for water resources management. Proceedings of the 2003 Georgia Water Resources Conference, Athens, GA, USA, April 2003.
[23]
Hemmat, A; Adamchuk, VI. Sensor systems for measuring soil compaction: Review and analysis. Comput. Electron. Agric 2008, 63, 89–103, doi:10.1016/j.compag.2008.03.001.
[24]
Zerger, A; Viscarra Rossel, RA; Swain, DL; Wark, T; Handcock, RN; Doerr, VAJ; Bishop-Hurley, GJ; Doerr, ED; Gibbons, PG; Lobsey, C. Environmental sensor networks for vegetation, animal and soil sciences. Int. J. Appl. Earth. Obs 2010, 12, 303–316, doi:10.1016/j.jag.2010.05.001.
[25]
Bartholomeus, H; Kooistra, L; Stevens, A; van Leeuwen, M; van Wesemael, B; Ben-Dor, E; Tychon, B. Soil organic carbon mapping of partially vegetated agricultural fields with imaging spectroscopy. Int. J. Appl. Earth. Obs 2011, 13, 81–88, doi:10.1016/j.jag.2010.06.009.
[26]
Kooistra, L; Wanders, J; Epema, GF; Leuven, RSEW; Wehrens, R; Buydens, LMC. The potential of field spectroscopy for the assessment of sediment properties in river floodplains. Anal. Chim. Acta 2003, 484, 189–200, doi:10.1016/S0003-2670(03)00331-3.
[27]
Viscarra Rossel, RA; Walvoort, DJJ; McBratney, AB; Janik, LJ; Skjemstad, JO. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 2006, 131, 59–75, doi:10.1016/j.geoderma.2005.03.007.
[28]
Robinson, NJ; Rampant, PC; Callinan, APL; Rab, MA; Fisher, PD. Advances in precision agriculture agriculture in south-eastern Australia. II. Spatio-temporal prediction of crop yield using terrain derivatives and proximally sensed data. Crop Pasture Sci 2009, 60, 859–869, doi:10.1071/CP08348.
[29]
Lambot, SA; Weihermüller, L; Huisman, JA; Vereecken, H; Vanclooster, M; Slob, EC. Analysis of air-launched ground-penetrating radar techniques to measure the soil surface water content. Water Resour. Res 2006, 42, W11403, doi:10.1029/2006WR005097.
[30]
Mertens, MF; P?tzold, S; Welp, G. Spatial heterogeneity of soil properties and its mapping with apparent electrical conductivity. J. Plant Nutr. Soil Sci 2008, 171, 146–154, doi:10.1002/jpln.200625130.
[31]
Sudduth, KA; Kitchen, NR; Wiebold, WJ; Batchelor, WD; Bollero, GA; Bullock, DG; Clay, DE; Palm, HL; Pierce, FJ; Schuler, RT; et al. Relating apparent electrical conductivity to soil properties across the north-central USA. Comput. Electron. Agric 2005, 46, 263–283, doi:10.1016/j.compag.2004.11.010.
[32]
Brown, DJ; Shepherd, KD; Walsh, MG; Dewayne Mays, M; Reinsch, TG. Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma 2006, 132, 273–290, doi:10.1016/j.geoderma.2005.04.025.
[33]
Sinfield, JV; Fagerman, D; Colic, O. Evaluation of sensing technologies for on-the-go detection of macro-nutrients in cultivated soils. Comput. Electron. Agric 2010, 70, 1–18, doi:10.1016/j.compag.2009.09.017.
Moghaddam, M; Entekhabi, D; Goykhman, Y; Ke, L; Mingyan, L; Mahajan, A; Nayyar, A; Shuman, D; Teneketzis, D. A wireless soil moisture smart sensor web using physics-based optimal control: Concept and initial demonstrations. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens 2010, 3, 522–535, doi:10.1109/JSTARS.2010.2052918.
[36]
Ritsema, CJ; Kuipers, H; Kleiboer, L; van den Elsen, E; Oostindie, K; Wesseling, JG; Wolthuis, J; Havinga, P. A new wireless underground network system for continuous monitoring of soil water contents. Water Resour Res 2009, 45, W00D36, doi:10.1029/2008WR007071.
[37]
Sun, Y; Li, L; Schulze Lammers, P; Zeng, Q; Lin, J; Schumann, H. A solar-powered wireless cell for dynamically monitoring soil water content. Comput. Electron. Agric 2009, 69, 19–23, doi:10.1016/j.compag.2009.06.009.
[38]
Stevens, A; Udelhoven, T; Denis, A; Tychon, B; Lioy, R; Hoffmann, L; van Wesemael, B. Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy. Geoderma 2010, 158, 32–45, doi:10.1016/j.geoderma.2009.11.032.
[39]
Gomez, C; Viscarra Rossel, RA; McBratney, AB. Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study. Geoderma 2008, 146, 403–411, doi:10.1016/j.geoderma.2008.06.011.
[40]
Greenwood, DJ; Zhang, K; Hilton, HW; Thompson, AJ. Opportunities for improving irrigation efficiency with quantitative models, soil water sensors and wireless technology. J. Agric. Sci 2010, 148, 1–16, doi:10.1017/S0021859609990487.
[41]
Silva, AR; Vuran, MC. Develoment of a testbed for wireless underground sensor networks. Eurasip J Wireless Commun Networking 2010, doi:10.1155/2010/620307..
[42]
Huang, J; Kumar, R; El-Sayed Kamal, A; Eber, RJ. Development of a wireless soil sensor network. Proceeding of the ASABE annual meeting 2008, Providence, RI, USA, June 2008.
[43]
Tiusanen, J. Wireless soil scout prototype radio signal reception compared to the attenuation model. Precis. Agric 2009, 10, 372–381, doi:10.1007/s11119-008-9096-7.
[44]
Pardossi, A; Incrocci, L; Incrocci, G; Malorgio, F; Battista, P; Bacci, L; Rapi, B; Marzialetti, P; Hemming, J; Balendonck, J; et al. Root zone sensors for irrigation management in intensive agriculture. Sensors 2009, 9, 2809–2835, doi:10.3390/s90402809. 22574047
[45]
Wang, N; Zhang, N; Dowell, FE; Sun, Y; Peterson, DE. Design of an optical weed sensor using plant spectral characteristics. Trans. ASAE 2001, 44, 409–419.
[46]
Gerhards, R; Oebel, H. Practical experiences with a system for site-specific weed control in arable crops using real-time image analysis and GPS-controlled patch spraying. Weed Res 2006, 46, 185–193, doi:10.1111/j.1365-3180.2006.00504.x.
[47]
Tian, LF; Reid, JF; Hummel, JW. Development of a precision sprayer for site-specific weed management. Trans.ASAE 1999, 42, 893–900.
[48]
Burks, TF; Shearer, SA; Payne, FA. Classification of weed species using color texture features and discriminant analysis. Trans. ASAE 2000, 43, 441–448.
[49]
Lamm, RD; Slaughter, DC; Giles, DK. Precision weed control system for cotton. Trans. ASAE 2002, 45, 231–238.
[50]
Slaughter, D; Giles, D; Downey, D. Autonomous robotic weed control systems: A review. Comput. Electron. Agric 2008, 61, 63–78, doi:10.1016/j.compag.2007.05.008.
[51]
Melander, B. Optimization of the adjustment of a vertical axis rotary brush weeder for intra-row weed control in row crops. J. Agric. Eng. Res 1997, 68, 39–50, doi:10.1006/jaer.1997.0178.
[52]
?strand, B; Baerveldt, A. An agricultural mobile robot with vision-based perception for mechanical weed control. Autonom. Robot 2002, 13, 21–35, doi:10.1023/A:1015674004201.
[53]
Peruzzi, A; Raffaelli, M; Ginanni, M; Borelli, M. Physical weed control in organic carrot in the Fucino Valley (Italy). Proceedings of the 7th EWRS Mediterranean Symposium, Adana, Turkey, May 2003; pp. 37–38.
[54]
van Der Schans, D; Bleeker, P; Molendijk, L. Practical Weed Control in Arable Farming and Outdoor Vegetable Cultivation Without Chemicals; Wageningen UR: Lelystad, The Netherlands, 2006.
[55]
McCarthy, C; Rees, S; Baillie, C. Machine vision-based weed spot spraying: A review and where next for sugarcane? Proceeding of the 32nd Annual Conference of the Australian Society of Sugar Cane Technologists, Bundaberg, Australia, May 2010; p. 7.
[56]
Paap, A; Askraba, S; Alameh, K; Rowe, J. Evaluation of an optical image sensor for use in the microphotonic real-time vegetation discrimination system. Proceedings of the Opto-Electronics and Communications Conference and Australian Conference on Optical Fibre Technology, Sydney, Australia, July 2008.
[57]
Weedseeker Automatic Spot Spray System. Available online: http://www.ntechindustries.com (accessed on 16 May 2011).
Dammer, K-H; Ehlert, D. Variable-rate fungicide spraying in cereals using a plant cover sensor. Precis. Agric 2006, 7, 137–148, doi:10.1007/s11119-006-9005-x.
[60]
Gerhards, R; Christensen, S. Real-time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley. Weed Res 2003, 43, 385–392, doi:10.1046/j.1365-3180.2003.00349.x.
[61]
Luck, JD; Pitla, SK; Shearer, SA; Mueller, TG; Dillon, CR; Fulton, JP; Higgins, SF. Potential for pesticide and nutrient savings via map-based automatic boom section control of spray nozzles. Comput. Electron. Agric 2010, 70, 19–26, doi:10.1016/j.compag.2009.08.003.
[62]
Heisel, T. Weeds in sugar beet rows—I. Influence of neighbour plant on the beet yield—II. Investigations of a CO2 laser for in-row weed control. Available online: http://agris.fao.org/agris-search/search/display.do?f=2003/DK/DK03011.xml;DK2003000580 (accessed on 16 May 2011).
[63]
Samborski, SM; Tremblay, N; Fallon, E. Strategies to make use of plant sensors-based diagnostic information for nitrogen recommendations. Agron. J 2009, 101, 800–816, doi:10.2134/agronj2008.0162Rx.
[64]
Goffart, JP; Olivier, M; Frankinet, M. Potato crop nitrogen status assessment to improve N fertilization management and efficiency: Past-present-future. Potato Res 2008, 51, 355–383, doi:10.1007/s11540-008-9118-x.
[65]
Tremblay, N; Wang, Z; Ma, B; Belec, C; Vigneault, P. A comparison of crop data measured by two commercial sensors for variable-rate nitrogen application. Precis. Agric 2009, 10, 145–161, doi:10.1007/s11119-008-9080-2.
Cugati, WM; Schueller, J. Automation concepts for the variable rate fertilizer applicator for tree farming. Proceedings of the 4th European Conference in Precision, Agriculture, Berlin, Germany, June 2003.
[71]
Delegido, J; Alonso, L; Gonzalez, G; Moreno, J. Estimating chlorophyll content of crops from hyperspectral data using a normalized area over reflectance curve (NAOC). Int. J. Appl. Earth Obs. Geoinf 2010, 12, 165–174, doi:10.1016/j.jag.2010.02.003.
[72]
Chen, P; Haboudane, D; Tremblay, N; Wang, J; Vigneault, P; Li, B; Chen, P; Haboudane, D; Tremblay, N; Wang, J; et al. New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sens. Environ 2010, 114, 1987–1997, doi:10.1016/j.rse.2010.04.006.
[73]
Clevers, J. A simplified approach for yield prediction of sugar beet based on optical remote sensing data. Remote Sens. Environ 1997, 61, 221–228, doi:10.1016/S0034-4257(97)00004-7.
[74]
Hatfield, JL; Prueger, JH. Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices. Remote Sens 2010, 2, 562–578, doi:10.3390/rs2020562.
[75]
Hedley, CB; Yule, IJ; Hedley, CB; Yule, IJ. A method for spatial prediction of daily soil water status for precise irrigation scheduling. Agric. Water Manage 2009, 96, 1737–1745, doi:10.1016/j.agwat.2009.07.009.
[76]
Grismer, ME. Field sensor networks and automated monitoring of soil water sensors. Soil Sci 1992, 154, 482–489, doi:10.1097/00010694-199212000-00007.
[77]
Vellidis, G; Smajstrla, AG; Zazueta, FS. Continuous soil water potential measurement with a microcomputer-based data acquisition system. Appl. Eng. Agric 1990, 6, 733–738.
Holler, M. High density multiple depth soil moisture tension measurements for irrigation management. Proceeding of the 59th Annual Meeting of American Society for Enology and Viticulture (ASEV), Portland, OR, USA, June 2008.
[80]
King, BA; Stark, JC; Wall, RW. Comparison of site-specific and conventional uniform irrigation management for potato. Appl. Eng. Agric 2006, 22, 677–688.
[81]
Vellidis, G; Tucker, M; Perry, C; Kvien, C; Bednarz, C. A real-time wireless smart sensor array for scheduling irrigation. Comput. Electron. Agric 2008, 61, 44–50, doi:10.1016/j.compag.2007.05.009.
[82]
Perry, C; Pocknee, S; Hansen, O. A variable rate pivot irrigatio control system. Proceedings of the Fourth European Conference in Presicion Agriculture, Berlin, Germany, 15–19 June 2003; pp. 539–544.
[83]
Chavez, JL; Pierce, FJ; Elliott, TV; Evans, RG. A remote irrigation monitoring and control system for continuous move systems. Part A: Description and development. Precis. Agric 2010, 11, 1–10, doi:10.1007/s11119-009-9109-1.
[84]
Kim, Y; Evans, RG. Software design for wireless sensor-based site-specific irrigation. Comput. Electron. Agric 2009, 66, 159–165, doi:10.1016/j.compag.2009.01.007.
[85]
Chavez, JL; Pierce, FJ; Elliott, TV; Evans, RG; Kim, Y; Iversen, WM. A remote irrigation monitoring and control system (RIMCS) for continuous move systems. Part B: Field testing and results. Precis. Agric 2010, 11, 11–26, doi:10.1007/s11119-009-9110-8.
Reyns, P; Missotten, B; Ramon, H; De Baerdemaeker, J. A review of combine sensors for precision farming. Precis. Agric 2002, 3, 169–182, doi:10.1023/A:1013823603735.
Schmidhalter, U; Maidl, F-X; Heuwinkel, H; Demmel, M; Auernhammer, A; Noack, PO; Rothmund, R. Precision farming-adaptation of land use management to small scale heterogeneity. Perspectives for Agroecosystem Management: Balancing Environmental and Socio-Economic Demands; Schr?der, P, Pfadenhauer, J, Munch, JC, Eds.; Elsevier: San Diego, CA, USA, 2008; pp. 121–199.
[90]
Whelan, BM; Taylor, JA; Hassall, JA. Site-specific variation in wheat grain protein concentration and wheat grain yield measured on an Australian farm using harvester-mounted on-the-go sensors. Crop Pasture Sci 2009, 60, 808–817, doi:10.1071/CP08343.
[91]
Taylor, J; Whelan, B; Thylén, L; Gilbertsson, M; Hassall, J. Monitoring wheat protein content on-harvester: Australian experiences. Precis. Agric 2005, 5, 8–17.
[92]
Long, DS; Engel, RE; Siemens, MC. Measuring grain protein concentration with in-line near infrared reflectance spectroscopy. Agron. J 2008, 100, 247–252, doi:10.2134/agrojnl2007.0052.
[93]
Stewart, CM; McBratney, AB; Skerritt, JH. Site-specific durum wheat quality and its relationship to soil properties in a single field in northern New South Wales. Precis. Agric 2002, 3, 155–168, doi:10.1023/A:1013871519665.
Stafford, J. Implementing precision agriculture in the 21st century. J. Agric. Eng. Res 2000, 76, 267–275, doi:10.1006/jaer.2000.0577.
[96]
Kumhala, F; Kroulik, M; Prosek, V. Development and evaluation of forage yield measure sensors in a mowing-conditioning machine. Comput .Electron. Agric 2007, 58, 154–163, doi:10.1016/j.compag.2007.03.013.
Lee, WS; Burks, T. Silage Yield Monitoring System; ASAE-Society for Engineering in Agricultural, Food, and Biological Systems: St. Joseph, MI, USA, 2002. Paper No. 021165.
[99]
Zhang, N; Wang, M; Wang, N. Precision agriculture—a worldwide overview. Comput. Electron. Agric 2002, 36, 113–132, doi:10.1016/S0168-1699(02)00096-0.
[100]
Griffin, TW; Dobbins, CL; Vyn, TJ; Florax, RJGM; Lowenberg-DeBoer, JM. Spatial analysis of yield monitor data: Case studies of on-farm trials and farm management decision making. Precis. Agric 2008, 9, 269–283, doi:10.1007/s11119-008-9072-2.
[101]
Arnó, J; Martínez-Casasnovas, J; Ribes-Dasi, M; Rosell, J. Precision viticulture. Research topics, challenges and opportunities in site-specific vineyard management. Span. J. Agric. Res 2009, 7, 779–790.
[102]
Matese, A; Di Gennaro, SF; Zaldei, A; Genesio, L; Vaccari, FP. A wireless sensor network for precision viticulture: The NAV system. Comput. Electron. Agric 2009, 69, 51–58, doi:10.1016/j.compag.2009.06.016.
[103]
Rosa, M; Genesio, R; Gozzini, B; Maracchi, G; Orlandini, S. Plasmo: A computer program for grapevine downy mildew development forecasting. Comput. Electron. Agric 1993, 9, 205–215, doi:10.1016/0168-1699(93)90039-4.
[104]
Burrell, J; Brooke, T; Beckwith, R. Sensor and actuator networks-Vineyard computing: Sensor networks in agricultural production. IEEE Pervas. Comput 2004, 3, 38–45, doi:10.1109/MPRV.2004.1269130.
[105]
Crainic, TG; Laporte, G. Fleet Management and Logistics; Kluwer Academic Publishers: Boston, MA, USA, 1998.
[106]
der van Heijden, R; Marchau, V. Innovating road traffic management by ITS: A future perspective. Int. J. Technol. Policy. Manag 2002, 2, 20–39, doi:10.1504/IJTPM.2002.001756.
[107]
Pesonen, L; Koskinen, H; Rydberg, A. Recommendations and Guidelines for a Novel, Intelligent, Integrated Information and Decision Support Framework for Planning and Control of Mobile Working Units; Nordic Innovation Centre: Oslo, Norway, 2008.
[108]
Kelly, B; Hatfield, G. Fleet management in the electronic age. Util. Fleet Manag 2003, 22, 20–26.
[109]
Borirug, S; Fung, C; Philuek, W. A study on the requirements and tools for real time fleet management e-business systems in Thailand. Proceeding of the 8th International Conference on e-Business (iNCEB2009), Bangkok, Thailand, October 2009; pp. 92–97.
[110]
Guo, LS; Zhang, Q. Wireless data fusion system for agricultural vehicle positioning. Biosyst. Eng 2005, 91, 261–269, doi:10.1016/j.biosystemseng.2005.04.001.
[111]
Jung, D. Fleet management with AGRO-COMBINE online. Landtechnik 2004, 59, 200–201.
[112]
Techy, L; Schmale, DG; Woolsey, CA. Coordinated aerobiological sampling of a plant pathogen in the lower atmosphere using two autonomous unmanned aerial vehicles. J. Field Rob 2010, 27, 335–343.
[113]
G?ktogan, A; Sukkarieh, S; Bryson, M; Randle, J; Lupton, T; Hung, C. A rotary-wing unmanned air vehicle for aquatic weed surveillance and management. J Intell Rob Syst 2010, 467–484.
[114]
Hunt, E, Jr; Hively, W; Fujikawa, S; Linden, D; Daughtry, C; McCarty, G. Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sens 2010, 2, 290–305, doi:10.3390/rs2010290.
[115]
Huang, Y; Hoffmann, WC; Lan, Y; Thomson, SJ; Fritz, BK. Development of unmanned aerial vehicles for site-specific crop production management. Proceedings of 10th International Conference on precision agriculture, Denver, CO, USA, 18–21 July 2010.
[116]
Lelong, C; Burger, P; Jubelin, G; Roux, B; Labbé, S; Baret, F. Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors 2008, 8, 3557–3585, doi:10.3390/s8053557.
[117]
Kotam?ki, N; Thessler, S; Koskiaho, J; Hannukkala, AO; Huitu, H; Huttula, T; Havento, J; J?rvenp??, M. Wireless in-situ sensor network for agriculture and water monitoring on a river basin scale in southern finland: Evaluation from a data user's perspective. Sensors 2009, 9, 2862–2883, doi:10.3390/s90402862. 22574050
[118]
Andrade-Piedra, JL; Forbes, GA; Shtienberg, D; Grunwald, NJ; Chacon, MG; Taipe, MV; Hijmans, RJ; Fry, WE. Qualification of a plant disease simulation model: Performance of the LATEBLIGHT model across a broad range of environments. Phytopathology 2005, 95, 1412–1422, doi:10.1094/PHYTO-95-1412. 18943552
[119]
Hansen, JG; Nielsen, BJ; B?dger, L; Andersson, B; Yuen, J; Wiik, L; Hermansen, A; N?rstad, R; Le, VH; Brurberg, MB; et al. Blight management in the Nordic countries. Proceedings of the 9th Workshop of an European Network for Development of an Integrated Control sStrategy of Potato Late Blight, Tallinn, Estonia, October 2007.
[120]
Linjama, J; Puustinen, M; Koskiaho, J; Tattari, S; Kotilainen, H; Granlund, K. Implementation of automatic sensors for continuous monitoring of runoff quantity and quality in small catchments. Agric. Food Sci 2009, 18, 417–427.
[121]
Langendoen, K; Baggio, A; Visser, O. Murphy loves potatoes: Experiences from a pilot sensor network deployment in precision agriculture. Proceeding of the 20th International Parallel and Distributed Processing Symposium, Rhodes, Greece, April 2006.
[122]
Kooistra, L; Thessler, S; Bregt, A. User requirements and future expectations for geosensor networks—An assessment. Lect. Notes Comput. Sci 2009, 5659, 149–157.
[123]
Balzano, L; Novak, R. Blind calibration of sensor networks. Proceedings of the 6th International Conference on Information Processing in Sensor Networks (IPSN’07), Cambridge, MA, USA, 25–27 April 2007.
[124]
Nash, E; Korduan, P; Bill, R. Applications of open geospatial web services in precision agriculture: A review. Precis. Agric 2009, 10, 546–560, doi:10.1007/s11119-009-9134-0.
[125]
ISO. ISO 11783: Tractors and Machinery for Agriculture and Forestry-Serial Control and Communications Data Network; International Organization for Standardization: Geneva, Switzerland, 2011.
[126]
INSPIRE Directive. Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing an infrastructure for spatial information in the European community (INSPIRE). Available online: http://inspire.jrc.ec.europa.eu/ (accessed on 16 May 2011).
[127]
Kresse, W; Fadaie, K. ISO Standards for Geographic Information; Springer Verlag: Berlin, Germany, 2004.
[128]
Steinberger, G; Rothmund, M; Auernhammer, H. Mobile farm equipment as a data source in an agricultural service architecture. Comput. Electron. Agric 2009, 65, 238–246, doi:10.1016/j.compag.2008.10.005.