SpringerLink
Log in

Smart Farming Technologies – Description, Taxonomy and Economic Impact

  • Chapter
  • First Online:
Precision Agriculture: Technology and Economic Perspectives

Part of the book series: Progress in Precision Agriculture ((PRPRA))

Abstract

Precision Agriculture is a cyclic optimization process where data have to be collected from the field, analysed and evaluated and finally used for decision making for site-specific management of the field. Smart farming technologies (SFT ) cover all these aspects of precision agriculture and can be categorized in data acquisition, data analysis and evaluation and precision application technologies. Data acquisition technologies include GNSS technologies, map** technologies, data acquisition of environmental properties and machines and their properties. Data analysis and evaluation technologies comprise the delineation of management zones, decision support systems and farm management information system s. Finally, precision application technologies embrace variable-rate application technologies, precision irrigation and weeding and machine guidance. In this chapter, the reader can find a technical description of the technologies included in each category accompanied by a taxonomy of all SFT in terms of farming system type, crop** system, availability, level of investment and farmers’ motives to adopt them. Finally, the economic impact that each SFT has compared to conventional agricultural practices is given.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://www.navipedia.net/index.php/GLONASS_Performances

  2. 2.

    http://www.gsa.europa.eu/galileo/programme

  3. 3.

    http://www.precisionagriculture.com.au/topography-and-drainage.php

  4. 4.

    The Doppler Effect is the difference between the observed frequency and the emitted frequency of a wave for an observer moving relative to the source of the waves.

  5. 5.

    http://extensionpublications.unl.edu/assets/pdf/ec783.pdf

  6. 6.

    http://www.dickey-john.com/product/radar-ii/

  7. 7.

    http://www.dji.com

  8. 8.

    See for example the FAO Guidelines on Irrigation and Drainage, FAO Paper 56, Crop Evapotranspiration

  9. 9.

    www.nereus-regions.eu

  10. 10.

    www.esa.int/gmes

References

  • Abidine AZ, Heidman BC, Upadhyaya SK, Hills DJ (2002) Application of RTK GPS based auto-guidance system in agricultural production. Paper No. O21152. ASAE, St. Joseph, MI, USA

    Google Scholar 

  • Adamchuk VI, Viscarra Rossel RA (2014) Precision agriculture: proximal soil sensing. Encyclopedia of Agrophysics

    Google Scholar 

  • Adamchuk VI, Hummel JW, Morgan MT, Upadhyaya SK (2004) On-the-go soil sensors for precision agriculture. Comput Electron Agric 44:71–91

    Article  Google Scholar 

  • AGROBOT (2012) AGROBOT – strawberry harvesters. http://www.agrobot.com

  • Akdemir B, Sağlam N, Bellitürk K, Aydogdu B (2007) Development a variable rate controller for centrifugal fertiliser (Design the system), 4th International Symposium on Intelligent information technology in Agriculture, 26–29 October 2007, Bei**g, China

    Google Scholar 

  • Andrade-Sanchez P, Gore MA, Heun JT, Thorp KR, Carmo-Silva AE, French AN, Salvucci ME, White JW (2014) Development and evaluation of a field-based high-throughput phenoty** platform. Funct Plant Biol 41:68–79

    Article  Google Scholar 

  • Arnó J, Martinez-Casasnovas J, Ribes-Dasi M, Rosell JR (2009) Review: precision viticulture. research topics, challenges and opportunities in site-specific vineyard management. Span J Agric Res 7:779–790

    Article  Google Scholar 

  • Arnó J, Escolà A, Valle JM, Llorens J, Sanz R, Masip J, Palacın J, Rosell-Polo JR (2013) Leaf area index estimation in vineyards using a ground-based LiDAR scanner. Precis Agric 14:290–306

    Article  Google Scholar 

  • Arnó J, Escolà A, Masip J, Rosell-Polo JR (2015) Influence of the scanned side of the row in terrestrial laser sensor applications in vineyards: practical consequences. Precis Agric 16(2):119–128

    Article  Google Scholar 

  • Åstrand B, Baerveldt AJ (2002) An agricultural mobile robot with vision-based perception for mechanical weed control. Auton Robot 13(1):21–35

    Article  Google Scholar 

  • Backman J, Oksanen T, Visala A (2013) Applicability of the ISO 11783 network in a distributed combined guidance system for agricultural machines Original Research Article. Biosyst Eng 114(3):306–317

    Article  Google Scholar 

  • Baio FHR, Moratelli RF (2011) Auto guidance accuracy evaluation and contrast of the operational field capacity on the mechanized plantation system of sugar cane. Eng Agric 31:367–375

    Google Scholar 

  • Batte MT, Ehsani MR (2006) The economics of precision guidance with auto-boom control for farmer-owned agricultural sprayers. Comput Electron Agric 53(1):28–44

    Article  Google Scholar 

  • Behic Tekin A, Okyay Sındır K (2013) Variable rate control system designed for spinner disc fertilizer spreader–“Pre Fer”. Agric Eng 2:45–53

    Google Scholar 

  • Benavente E, García-Toledano L, Carrillo JM, Quemada M (2013) Thermographic imaging: assessment of drought and heat tolerance in Spanish germplasm of Brachypodium distachyon. Procedia Environ Sci 19:262–266

    Article  Google Scholar 

  • Berne D (2015) Agricultural irrigation initiative: overview of center pivot irrigation systems. https://neea.org/docs/default-source/reports/overview-of-center-pivot-irrigation-systems.pdf?sfvrsn=4. Retrieved on 10 Jan 2016

  • Berni JA, Zarco-Tejada PJ, Suarez L, Fereres E (2009) Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans Geosci Remote Sens 47:722–738

    Article  Google Scholar 

  • Blackmore BS (2007) A systems view of agricultural robots. In: Proceedings of the 6th European conference on precision agriculture, pp 23–31

    Google Scholar 

  • Booker JD, Lascano RJ, Molling CC, Zartman RE, Acosta-Martinez (2015) Temporal and spatial simulation of production-scale irrigated cotton systems. Precis Agric 16:630–653

    Article  Google Scholar 

  • Brambilla M, Calcante A, Oberti R, Bisaglia C (2015) Slurry tanker retrofitting with variable rate dosing system: a case study. In: Precision agriculture ’15. Wageningen Academic Publishers, pp 123–135

    Google Scholar 

  • Bramley RGV (2001) Progress in the development of precision viticulture-variation in yield, quality and soil properties in contrasting Australian vineyards. In: Currie LD, Loganathan P (eds) Precision tools for improving land management, Occasional report No. 14. Fertilizer and Lime Research Centre/Massey University, Palmerston North, pp 25–43

    Google Scholar 

  • Bullock DG, Bullock DS, Nafziger ED, Doerge TA, Paszkiewicz SR, Carter PR, Peterson TA (1998) Does variable rate seeding of corn pay? Agron J 90:830–836

    Article  Google Scholar 

  • Calcante A, Brambilla M, Oberti R, Bisaglia C (2015) A retrofit variable-rate control system for pressurized slurry tankers. Appl Eng Agric 31(4):569–579

    Google Scholar 

  • Calderón R, Navas-Cortés JA, Lucena C, Zarco-Tejada PJ (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

    Article  Google Scholar 

  • Camp CR (1998) Subsurface drip irrigation: a review. Applied engineering in agriculture. American Society of Agricultural and Biological Engineers 41(5):1353–1367

    Article  Google Scholar 

  • Cerovic ZG, Samson G, Morales F, Tremblay N, Moya I (1999) Ultraviolet-induced fluorescence for plant monitoring: present state and prospects. Agronomie 19:543–578

    Article  Google Scholar 

  • Chen S, Shi** Z (2011) Experimental research on variable rate fertization technology of spinning disc spreader for rice. In: International conference on agricultural and Biosystems Engineering

    Google Scholar 

  • Chene Y, Rousseau D, Lucidarme P, Bertheloot J, Caffier V, Morel P, Belin E, Chapeau-Blondeau F (2012) On the use of depth camera for 3D phenoty** of entire plants. Comput Electron Agric 82:122–127

    Article  Google Scholar 

  • Christen D, Schönmann S, Jermini M, Strasser RJ, Défago G (2007) Characterisation and early detection of grapevine stress responses to esca disease by in situ chlorophyll fluorescence and comparison with drought stress. Environ Exp Bot 60:504–514

    Article  CAS  Google Scholar 

  • Colaizzi PD, Gowda PH, Marek TH, Porter O (2009) Irrigation in the Texas high plains: a brief history and potential reductions in demand. Irrig Drain 58:257–274

    Article  Google Scholar 

  • Dammer KH, Wartenberg G (2007) Sensor-based weed detection and application of variable herbicide rates in real time. Crop Prot 26(3):270–277

    Article  Google Scholar 

  • Darr M (2012) CAN Bus technology enables advanced machinery management. Resour Eng Technol Sustainable World 19(5):10–11

    Google Scholar 

  • Dillon CR, Gandonou J, Shockley J (2009) Variable rate seeding for French wheat production: profitability and production risk management potential. In: Lokhorst C, Huijsmans JFM, de Louw RPM (eds) JIAC2009 book of abstracts. Wageningen Academic Publishers, Wageningen, p 350

    Google Scholar 

  • Doruchowski G, Swiechowski W, Holownicki R, Godyn A (2009) Environmentally-dependent application system (EDAS) for safer spray application in fruit growing. J Hortic Sci Biotechnol (special issue): 107–112

    Google Scholar 

  • Ehlert D, Heisig M, Adamek R (2010) Suitability of a laser rangefinder to characterize winter wheat. Precis Agric 11(6):650–663

    Article  Google Scholar 

  • Erdle K, Mistele B, Schmidhalter U (2011) Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crop Res 124:74–84

    Article  Google Scholar 

  • Ess DR, Parsons SD, Medlin CR (2001) Implementing site-specific management: sprayer technology – controlling application rate on the go. http://www.ces.purdue.edu/extmedia/AE/SSM-5-W.pdf

  • Evans RG, King BA (2012) Site-Specific sprinkler irrigation in a water-limited future. Applied engineering in agriculture. Am Soc Agric Biol Eng 55(2):493–504

    Google Scholar 

  • Fanourakis D, Briese C, Max JFJ, Kleinen S, Putz A, Fiorani F, Ulbrich A, Schurr U (2014) Rapid determination of leaf area and plant height by using light curtain arrays in four species with contrasting shoot architecture. Plant Methods 10:9

    Article  PubMed  PubMed Central  Google Scholar 

  • Feng W, Yao X, Zhu Y, Tian YC, Cao WX (2008) Monitoring leaf nitrogen status with hyperspectral reflectance in wheat. Eur J Agron 28:394–404

    Article  CAS  Google Scholar 

  • Forouzanmehr E, Loghavi M (2012) Design, development and field evaluation of a map-based variable rate granular fertilizer application control system. Agric Eng Int CIGR J 14(4):255–261

    Google Scholar 

  • Foth HD, Ellis BG (1988) Soil fertility. Wiley, New York

    Google Scholar 

  • Fountas S, Blackmore S, Ess D, Hawkins S, Blumhoff G, Lowenberg-Deboer J, Sorensen CG (2005) Farmer experience with precision agriculture in Denmark and the US Eastern corn belt. Precis Agric 6:121–141

    Article  Google Scholar 

  • Fountas S, Wulfsohn D, Blackmore S, Jacobsen HL, Pedersen SM (2006) A model of decision making and information flows for information-intensive agriculture. Agric Syst 87:192–210

    Article  Google Scholar 

  • Fountas S, Balafoutis A, Anastasiou E, Koundouras S, Kotseridis G, Kallithraka E, Kyraleou M (2014) Site-specific variability of grape composition and wine quality. Proceedings of International Conference on Precision Agriculture, Sacramento

    Google Scholar 

  • Fountas S, Carli C, Sørensen CG, Tsiropoulos Z, Cavalaris C, Vatsanidou A, Liakos B, Canavari M, Wiebensohn J, Tisserye B (2015) Farm management information systems: current situation and future perspectives. Comput Electron Agric 115:40–50

    Article  Google Scholar 

  • Fulton JP, Shearer SA, Chabra G, Higgins SF (2001) Performance assessment and model development of a variable-rate, spinner-disc fertilizer applicator. Trans Am Soc Agric Eng 44(5):1071–1082

    Article  Google Scholar 

  • Funk TL, Robert MJ (2003) Variable rate manure spreader: technology to validate a nutrient management plan. http://livestocktrail.illinois.edu/uploads/sowm/papers/p111-121.pdf

  • Gan-Mor S, Clark RL, Upchurch BL (2007) Implement lateral position accuracy under RTK-GPS tractor guidance. Comput Electron Agric 59(1):31–38

    Article  Google Scholar 

  • Gerhards R, Sökefeld M (2003) Precision farming in weed control – sytem components and economic benefits. In: Stafford J, Werner A (eds) Precision agriculture. Wageningen Academic Publishers, Wageningen, pp 229–234

    Google Scholar 

  • Ghozlen NB, Cerovic ZG, Germain C, Toutain S, Latouche G (2010) Non-destructive optical monitoring of grape maturation by proximal sensing. Sensors 10:10040–10068

    Article  PubMed  PubMed Central  Google Scholar 

  • Gravalos I, Tsiropoulos Z, Kateris D, Gialamas T, Xyradakis P, Avgoustis A, Fountas S (2014) Soil moisture remote monitoring from an agricultural tractor. In: Second international conference on robotics and associated high-technologies and equipment for agriculture and forestry. Madrid, 22–23

    Google Scholar 

  • Grevis-James IW, DeVoe DR, Bloome PD, Batchelder DG, Lambert BW (1983) Microcomputer-based data acquisition for tractors. Trans ASABE 26(3):692–695

    Article  Google Scholar 

  • Grisso R, Alley M, Thomason W, Holshouser D, Roberson GT (2011) Precision farming tools: variable-rate application. Va Coop Ext Publ:442–505

    Google Scholar 

  • Harter DD, Kaufman KR (1979) Microprocessor based data acquisition system for tractor tillage measurements. Paper No. 79–5026, ASAE, St Joseph, MI, USA

    Google Scholar 

  • HAU & CERETETH (2013) Dionysus Robot to Help Winegrowers. http://vimeo.com/68527042

  • Hernández-Pajares M, Juan JM, Sanz J (2000) Improving the Abel inversion by adding ground GPS data to LEO radio occultations in ionospheric sounding. Geophys Res Lett 27(16):2473–2476

    Article  Google Scholar 

  • Héroux P, Kouba J (1995) GPS precise point positioning with a difference. Natural Resources Canada, Geomatics Canada, Geodetic Survey Division

    Google Scholar 

  • Herwitz SR, Johnson LF, Dunagan SE, Higgins RG, Sullivan DV, Zheng J, Lobits BM, Leung JG, Gallmeyer BA, Aoyagi M, Slye RE, Brass JA (2004) Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support. Comput Electron Agric 44:49–61

    Article  Google Scholar 

  • Hoffmeister D, Waldhoff G, Korres W, Curdt C, Bareth G (2016) Crop height variability detection in a single field by multi-temporal terrestrial laser scanning. Precis Agric 17(3):296–312

    Article  Google Scholar 

  • Hosoi F, Omasa K (2012) Estimation of vertical plant area density profiles in a rice canopy at different growth stages by high-resolution portable scanning lidar with a lightweight mirror. ISPRS J Photogram Remote Sens 74:11–19

    Article  Google Scholar 

  • Hostens I, Anthonis J, Kennes P, Ramon H (2000) Six-degrees-of-freedom test rig design for simulation of mobile agricultural machinery vibrations. J Agric Eng Res 77(2):155–169

    Article  Google Scholar 

  • Huber DM, Sutton AL, Jones DD, Joern BC, Mitchell JK (1993) Nutrient management of manure to enhance crop production and protect the environment. In: Proceedings of the Integrated Resource Management and Landscape Modifications for Environmental Protection Conference, Chicago, IL. American Society of Agricultural Engineers, St. Joseph, MI

    Google Scholar 

  • Iftikhar N, Pedersen TB (2011) Flexible exchange of farming device data. Comput Electron Agric 75(1):52–63

    Article  Google Scholar 

  • Inman D, Khosla R, Mayfield T (2005) On-the-go active remote sensing for efficient crop nitrogen management. Sens Rev 25(3):209–214

    Article  Google Scholar 

  • Jorgensen RN, Sorensen CG, Maagaard J, Havn I, Jensen K, Sogaard HT, Sorensen LB (2007) Hortibot: a system design of a robotic tool carrier for high-tech plant nursing. CIGR J IX

    Google Scholar 

  • Karkee M, Steward B, Kruckeberg J (2013) Automation of pesticide application systems. In: Zhang G, Pierce FJ (eds) Agricultural automation: fundamentals and practices. CRC Press, Boca Raton

    Google Scholar 

  • Kelly I, Melhuish C (2001) Slugbot: a robot predator. In: Advances in artificial life. Springer, Berlin/Heidelberg, pp 519–528

    Chapter  Google Scholar 

  • Kitchen NR, Sudduth KA, Myers DB, Drummond ST, Hong SY (2005) Delineating productivity zones on claypan soil fields apparent soil electrical conductivity. Comput Electron Agric 46:285–308

    Article  Google Scholar 

  • Koch B, Khosla R, Frasier WM, Westfall DG, Inman D (2004) Economic feasibility of variable-rate nitrogen application utilizing site-specific management zones. Agron J 96(6):1572–1580

    Article  Google Scholar 

  • Kuang B, Tekin Y, Waine T, Mouazen AM (2014) Variable rate lime application based on on-line visible and near infrared (vis-NIR) spectroscopy measurement of soil properties in a Danish field. Ageng 2014

    Google Scholar 

  • Lambert D, Lowenberg-De Boer J (2000) Precision agriculture profitability review. Purdue University

    Google Scholar 

  • Landau H, Chen X, Kipka A, Vollath U (2007) Latest developments in Network RTK modeling to support GNSS modernization. Positioning 1(11)

    Google Scholar 

  • Lawson LG, Pedersen SM, Sorensen CG, Pesonen L, Fountas S, Werner A, Oudshoorn FW, Herold L, Chatzinikos T, Kirketerp IM, Blackmore S (2011) A four nation survey of farm information management and advanced farming systems: a descriptive analysis of survey responses. Comput Electron Agric 77:7–20

    Article  Google Scholar 

  • Li X, Zhang X, Ren X, Fritsche M, Wickert J, Schuh H (2015) Precise positioning with current multi-constellation global navigation satellite systems: GPS, GLONASS, Galileo and BeiDou. Sci Rep 5:8328

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Liu C, Liu W, Lu X, Ma F, Chen W, Yang J, Zheng L (2014) Application of multispectral imaging to determine quality attributes and ripeness stage in strawberry fruit. PLoS One 9(2):e87818

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Lowenberg-DeBoer JM (1998) Economics of variable rate planting for corn. In: Robert PC, Rust RH, Larson WE (eds) Proceedings of 4th international conference on precision agriculture. Purdue University, Saint Paul, pp 1643–1651

    Google Scholar 

  • Maleki MR, Mouazen AM, De Ketelaere B, Ramon H, De Baerdemaeker J (2008a) On-the-go variable-rate phosphorus fertilisation based on a visible and near-infrared soil sensor. Biosyst Eng 99(1):35–46

    Article  Google Scholar 

  • Maleki MR, Ramon H, De Baerdemaeker J, Mouazen AM (2008b) A study on the time response of a soil sensor-based variable rate granular fertiliser applicator. Biosyst Eng 100(2):160–166

    Article  Google Scholar 

  • Mamo M, Malzer GL, Mulla DJ, Huggins DR, Strock J (2003) Spatial and temporal variation in economically optimum nitrogen rate for corn. Agron J 95(4):958–964

    Article  Google Scholar 

  • Martelloni L (2014) Design and realization of an innovative automatic machine able to perform site-specific thermal weed control in maize. PhD thesis, Università degli Studi di Firenze

    Google Scholar 

  • Martínez JL, Mandow A, Morales J, Pedraza S, García-Cerezo A (2005) Approximating kinematics for tracked mobile robots. Int J Robot Res 24(10):867–878

    Article  Google Scholar 

  • McBratney AB, Whelan B, Ancev T, Bouma J (2005) Future directions of precision agriculture. Precis Agric 6:7–23

    Article  Google Scholar 

  • Montes JM, Techow F, Dhillon BS, Mauch F, Melchinger AE (2011) High-throuput non-destructive biomass determination during early plant development in maize under field conditions. Field Crop Res 121:268–273

    Article  Google Scholar 

  • Morris DK, Ess DR, Hawkins SE, Parsons SD (1999) Development of a site-specific application system for liquid animal manures. Appl Eng Agric 15(6):633–638

    Article  Google Scholar 

  • Munoz-Carpena R (2017) Field devices for monitoring soil water content. http://edis.ifas.ufl.edu/pdffiles/AE/AE26600.pdf. Retrieved 20 Feb 2017

  • Nakarmi AD, Tang L (2012) Automatic inter-plant spacing sensing at early growth stages using a 3D vision sensor. Comput Electron Agric 82:23–31

    Article  Google Scholar 

  • Nikkila R, Seilonen I, Koskinenet K (2010) Software architecture for farm management information systems in precision agriculture. Comput Electron Agric 70(2):328–336

    Article  Google Scholar 

  • Oriade CA, King RP, Forcella F, Gunsolus JL (1996) A bioeconomic analysis of site-specific management for weed control. Rev Agric Econ 18:523–535

    Google Scholar 

  • Ortiz BV, Balkcom KB, Duzy L, van Santen E, Hartzog DL (2013) Evaluation of agronomic and economic benefits of using RTK-GPS-based auto-steer guidance systems for peanut digging operations. Precis Agric 14:357–375

    Article  Google Scholar 

  • Paulus S, Schumann H, Kuhlmann H, Leon J (2014) High-precision laser scanning system for capturing 3D plant architecture and analyzing growth of cereal plants. Biosyst Eng 121:1–11

    Article  Google Scholar 

  • Pedersen SM (2003) Precision farming, technology assessment of site-specific input application in cereals. PhD dissertation, Technical University of Denmark, p 343

    Google Scholar 

  • Pedersen SM, Pedersen JL (2002) Economic and environmental impact of site-specific N-application – based on different weather conditions and arable crops. Paper presentation at the 6th international conference on precision agriculture, Jul 14–17, 2002, Minneapolis, US

    Google Scholar 

  • Pedersen SM, Fountas S, Blackmore BS, Gylling M, Pedersen JL, Pedersen HH (2003) Adoption of precision farming in Denmark. 4th European conference on precision agriculture proceedings, p 533–538

    Google Scholar 

  • Perez-Ruiz M, Slaughter DC, Gliever C, Upadhyaya SK (2013) Tractor-based Real-time Kinematic-Global Positioning System (RTK-GPS) guidance system for geospatial map** of row crop transplant. Biosyst Eng 111:64–71

    Article  Google Scholar 

  • Ramirez DA, Yactayo W, Gutierrez R, Mares V, De Mendiburu F, Posadas A, Quiroz R (2014) Chlorophyll concentration in leaves is an indicator of potato tuber yield in water-shortage conditions. Sci Hortic 168:202–209

    Article  CAS  Google Scholar 

  • Raun W, Solie J, Johnson G, Stone M, Mullen R, Freeman K, Thomason W, Lukina E (2001) Improving Nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agron J 94:815–820

    Article  Google Scholar 

  • Remondi BW (1985) Performing centimeter-level surveys in seconds with GPS carrier phase: initial results. Navigation 32(4):386–400

    Article  Google Scholar 

  • Reutebuch SE, McGaughey RJ, Andersen HE, Carson WW (2003) Accuracy of a high-resolution lidar terrain model under a conifer forest canopy. Can J Remote Sens 29(5):527–535

    Article  Google Scholar 

  • Rojo B, Fabio H (2012) Evaluation of an auto-guidance system operating on a sugar cane harvester. Precis Agric 13:141–147

    Article  Google Scholar 

  • Ramon H, Missotten B, De Baerdemaeker J (1997) Spray boom motions and spray distribution: Part 2, Experimental validation of the mathematical relation and simulation results. J Agric Eng Res 66(1):31–39

    Article  Google Scholar 

  • Rosell JR, Llorens J, Sanz R, Arno J, Ribes-Dasi M, Masip J, Escola A, Camp F, Solanelles F, Gracia F, Gil E, Val L, Planas S, Palacin J (2009) Obtaining the three-dimensional structure of tree orchards from remote 2D terrestrial LIDAR scanning. Agric For Meteorol 149:1505–1515

    Article  Google Scholar 

  • Rouse JW, Haas RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the Great Plains with ERTS, Third ERTS symposium, NASA SP351 I, pp 309–317

    Google Scholar 

  • Ruckelshausen A, Biber P, Dorna M, Gremmes H, Klose R, Linz A, Rahe R, Resch R, Thiel M, Trautz D, Weiss U (2009) BoniRob: an autonomous field robot platform for individual plant phenoty**. Precis Agric 9:841

    Google Scholar 

  • Scarlett AJ (2001) Integrated control of agricultural tractors and implements: a review of potential opportunities relating to cultivation and crop establishment machinery. Comput Electron Agric 30:167–191

    Article  Google Scholar 

  • Schellberg J, Reiner L (2009) A site-specific slurry application technique on grassland and on arable crops. Bioresour Technol 100:280–286

    Article  CAS  PubMed  Google Scholar 

  • Shanahan JF, Doerge TA, Johnson JJ, Vigil MF (2004) Feasibility of site-specific management of corn hybrids and plant densities in the great plains. Precis Agric 5:207–225

    Article  Google Scholar 

  • Shinners TJ, Digman MF, Panuska JC (2012) Overlap loss of manually and automatically guided mowers. Appl Eng Agric 28:5–8

    Article  Google Scholar 

  • Shockley JM, Dillon CR, Stombaugh T (2011) A whole farm analysis of the influence of auto-steer navigation on net returns, risk and production practices. J Agric Appl Econ 43(1):57–75

    Article  Google Scholar 

  • Society of Automotive Engineers (SAE) (1995) Surface vehicle recommended practice J 1939-7x

    Google Scholar 

  • Sogaard HT, Kierkegaard P (1994) Yield reduction resulting from uneven fertiliser distribution. Trans ASAE 37:1749–1752

    Article  Google Scholar 

  • Sørensen CG (1999) A Bayesian network based decision support system for the management of field operations. Case: harvesting operations. Ph.D. thesis, Technical University of Denmark, 193 pp

    Google Scholar 

  • Sørensen GC, Fountas S, Nash E, Pesonen L, Bochtis D, Pedersen SM, Basso B, Blackmore SB (2010) Conceptual model of a future farm management information system. Comput Electron Agric 72:37–47

    Article  Google Scholar 

  • Stafford JV (2000) Implementing precision agriculture in the 21st century. J Agric Eng Res 76(3):267–275

    Google Scholar 

  • Steffe J (2000) Evolution of the farm environment: the need to produce a general information system. In: Beers G, Poppe KJ, de Putter I (eds) Agenda 2000 and the FADN agenda, Workshop report, Project code 63403. Agricultural Economics Research Institute (LEI), The Hague, pp 88–97

    Google Scholar 

  • Swinton SM (2003) Site-specific pest management. In: den Hond F, Groenewegen P, van Straalen NM (eds) Pesticides – problems, improvements, alternatives. Blackwell Science, Oxford. 155 p

    Google Scholar 

  • Swinton SM, Lowenberg-DeBoer J (1998) Evaluating the profitability of site-specific farming. J Prod Agric 11(4):439–446

    Article  Google Scholar 

  • Tabile RA, Godoy EP, Pereira RR, Tangerino GT, Porto AJ, Inamasu RY (2011) Design and development of the architecture of an agricultural mobile robot. Engenharia Agricola 31(1):130–142

    Article  Google Scholar 

  • Tagarakis A, Liakos V, Fountas S, Koundouras S, Aggelopoulou K, Gemtos T (2011) Management zones delineation using fuzzy clustering techniques in vines. In: Stafford J (ed) Precision agriculture, proceedings of the 8th European conference on precision agriculture. Wageningen Academic Publishers, Wageningen, pp 191–200

    Google Scholar 

  • Taylor RK, Staggenborg S (2000) Using a GIS to evaluate the potential of variable rate corn seeding. ASAE meeting presentation

    Google Scholar 

  • Taylor RK, Schrock MD, Staggenborg SA (2002) Extracting machinery management information from GPS data. Paper No. 02–10008. St. Joseph, Michigan: ASABE

    Google Scholar 

  • Tekin AB, Yurdem H, Fountas S, Tsiropoulos Z, Aygun T (2013) Design and implementation of robotic platform: RoboTurk. In: Proceedings of the EFITA WCCA-CIGR conference “Sustainable Agriculture through ICT Innovation”

    Google Scholar 

  • Thoren D, Thoren P, Schmidhalter U (2010) Influence of ambient light and temperature on laser-induced chlorophyll fluorescence measurements. Eur J Agron 32(2):169–176

    Article  CAS  Google Scholar 

  • Thorp KR, Dierig DA (2011) Color image segmentation approach to monitor flowering in lesquerella. Ind Crop Prod 34:1150–1159

    Article  Google Scholar 

  • Timmermann C, Gerhards R, Kühbauch W (2003) The economic impact of site-specific weed control. Precis Agric 4:249–260

    Article  Google Scholar 

  • Tressos IAK (2011) Zeus: codename savage. http://www.savage.gr/gallery.html

  • Tsiropoulos Z, Fountas S, Gemtos T, Gravalos I, Paraforos D (2013) Management information system for spatial analysis of tractor-implement draft forces. In: Precision agriculture’13. Wageningen Academic Publishers, Wageningen, pp 349–356

    Google Scholar 

  • Tsiropoulos Z, Fountas S, Gravalos I, Augoustis A, Arslan S, Misiewicz P, Gemtos T (2015) Importance of measuring tillage implement forces for reduced fuel consumption and increased efficiency without affecting tillage depth. In: Precision agriculture’15. Wageningen Academic Publishers, Wageningen, pp 217–232

    Google Scholar 

  • Tu X (2013) Robust navigation control and headland turning optimization of agricultural vehicles. Graduate theses and dissertations, Paper 13188. Iowa State University

    Google Scholar 

  • Van Elderen E (1977) Heuristic strategy for scheduling farm operations. Centre for Agricultural Publishing and Documentation, Wageningen, .The Netherlands, 217 pp

    Google Scholar 

  • Van Liedekerke PJ, Baerdemaeker D, Ramon H (2006) Precision Agriculture: Fertilizer application control. In: Munack A (ed) CIGR handbook of agricultural engineering VOLUME VI information technology. CIGR, St. Joseph

    Google Scholar 

  • Verdu S, Ivorra E, Sanchez AJ, Giron J, Barat JM, Grau R (2013) Comparison of TOF and SL techniques for in-line measurement of food item volume using animal and vegetable tissues. Food Control 33:221–226

    Article  CAS  Google Scholar 

  • Vlugt P (2013) Ag Industry’s initiative in electronic standards implementation. http://www.clubofbologna.org/ew/documents/1.3_Van_der_Vlugt.pdf

  • Vollmann J, Walter H, Sato T, Schweiger P (2011) Digital image analysis and chlorophyll metering for phenoty** the effects of nodulation in soybean. Comput Electron Agric 75:190–195

    Article  Google Scholar 

  • Wang W, Li C (2014) Size estimation of sweet onions using consumer-grade RGB-depth sensor. J Food Eng 142:153–162

    Article  Google Scholar 

  • Weisz R, Heiniger R, White JG, Knox B, Reed L (2003) Long-term variable rate lime and phosphorus application for Piedmont no-till field crops. Precis Agric 4(3):311–330

    Article  Google Scholar 

  • Whelan B, Taylor J (2013) Precision agriculture for grain production systems. CSIRO Publishing, Collingwood

    Google Scholar 

  • Wollenhaupt NC, Mulla DJ, Gotway Crawford CA (1997) Chapter 2: Soil sampling and interpolation techniques for map** spatial variability of soil properties. In: Pierce FT, Sadler EJ (eds) The state of site-specific management for agriculture. ASA-CSSA-SSSA, Madison, pp 19–53

    Google Scholar 

  • Yahya A, Zohadiea M, Kheiralla AF, Giewa SK, Boona NE (2009) Map** system for tractor-implement performance. Comput Electron Agric 69:2–11

    Article  Google Scholar 

  • Yigit CO (2016) Experimental assessment of post-processed kinematic precise point positioning method for structural health monitoring. Geomat Nat Haz Risk 7(1):360–383

    Article  Google Scholar 

  • Zarco-Tejada PJ, Gonzales-Dugo V, Williams LE, Suarez L, Berni JAJ, Goldhamer D, Fereres E (2013a) A PRI-based water stress index combining structural and chlorophyll effects: assessment using diurnal narrow-band airborne imagery and the CSWI thermal index. Remote Sens Environ 138:38–50

    Article  Google Scholar 

  • Zarco-Tejada PJ, Guillen-Climent MI, Hernandez-Clemente R, Catalina A, Gonzalez MR, Martin P (2013b) Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agric For Meteorol 171–172:281–294

    Article  Google Scholar 

  • Zarco-Tejada P Hubbard N, Loudjani P (2014) Precision agriculture: an opportunity for EU farmers – potential support with the CAP 2014–2020. Joint Research Centre (JRC) of the European Commission. Monitoring Agriculture ResourceS (MARS) Unit H04, Brussels, Belgium

    Google Scholar 

  • Zhang C, Kovacs JM (2012) The small unmanned aerial systems for precision agriculture: a review. Precis Agric 13:693–712

    Article  CAS  Google Scholar 

  • Zumberge JF, Heflin MB, Jefferson DC, Watkins MM, Webb FH (1997) Precise point positioning for the efficient and robust analysis of GPS data from large networks. J Geophys Res Solid Earth 102(B3):5005–5017

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Agricultural University of Athens, Athens, Greece

    Athanasios T. Balafoutis, Spyros Fountas & Zisis Tsiropoulos

  2. Institute of Bio-economy & Agro-technology Centre of Research & Technology Hellas, Volos, Greece

    Athanasios T. Balafoutis

  3. Institute for Agricultural, Fisheries and Food Research (ILVO), Merelbeke, Belgium

    Bert Beck & Jürgen Vangeyte

  4. Wageningen Environmental Research (Alterra), Wageningen, The Netherlands

    Tamme van der Wal

  5. European Commission – Joint Research Centre (JRC), Seville, Spain

    I. Soto-Embodas & Manuel Gómez-Barbero

  6. Department of Food and Resource Economics, Faculty of Science, University of Copenhagen, Frederiksberg C, Denmark

    Søren Marcus Pedersen

Authors
  1. Athanasios T. Balafoutis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bert Beck
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Spyros Fountas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zisis Tsiropoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jürgen Vangeyte
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tamme van der Wal
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. I. Soto-Embodas
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Manuel Gómez-Barbero
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Søren Marcus Pedersen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Athanasios T. Balafoutis .

Editor information

Editors and Affiliations

  1. Department of Food and Resource Economics, University of Copenhagen, Frederiksberg C, Denmark

    Søren Marcus Pedersen

  2. Department of Food and Resource Economics, University of Copenhagen, Frederiksberg C, Denmark

    Kim Martin Lind

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Balafoutis, A.T. et al. (2017). Smart Farming Technologies – Description, Taxonomy and Economic Impact. In: Pedersen, S., Lind, K. (eds) Precision Agriculture: Technology and Economic Perspectives. Progress in Precision Agriculture. Springer, Cham. https://doi.org/10.1007/978-3-319-68715-5_2

Download citation

Publish with us

Policies and ethics

Search

Navigation