Skip to main content

Precision Agriculture: An Overview of the Field and Women’s Contributions to It

  • Chapter
  • First Online:
Women in Precision Agriculture

Part of the book series: Women in Engineering and Science ((WES))

  • 470 Accesses

Abstract

Precision agriculture is about viewing and treating the agricultural process as a system and incorporating information available from all its parts to improve its performance. In order to do so, new enabling processes, tools, and technologies had to be developed to enable the observation and measurement of important variables, facilitate the study and assessment of these variables to extract relevant information and knowledge, and use this knowledge to control the agricultural process and its inputs/outputs. This book is a compilation of contributions, breakthroughs, and impactful research done by leading female researchers and scholars from various fields and from around the world toward making precision agriculture a reality. These researchers are creating new technological advances that are revolutionizing agriculture and providing innovative solutions to some of today’s most challenging global food problems, paving the way for a smarter, more precise, more efficient, and more profitable agriculture for the twenty-first century. This is the only known book focused on advances in precision agriculture for both land and livestock, led by women researchers and scholars, hence providing a unique woman’s perspective in a field primarily dominated by men. This chapter presents a holistic overview of the field, highlighting relevant technologies, decision-making strategies, practices, applications, economics, opportunities, and challenges for both land and livestock applications.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Adamchuk, V. I., Morgan, M. T., & Ess, D. R. (1999). An automated sampling system for measuring soil pH. Transactions of ASAE, 42, 885–892. https://doi.org/10.13031/2013.13268.

    Article  Google Scholar 

  • Adrion, F., Kapun, A., Eckert, F., Holland, E.-M., Staiger, M., Götz, S., & Gallmann, E. (2018). Monitoring trough visits of growing-finishing pigs with UHF-RFID. Computers and Electronics in Agriculture, 144, 144–153. https://doi-org.proxy-remote.galib.uga.edu/10.1016/j.compag.2017.11.036.

    Article  Google Scholar 

  • Aguilar-Rivera, N., Algara-Siller, M., Olvera-Vargas, L. A., & Michel-Cuello, C. (2018). Land management in Mexican sugarcane crop fields. Land Use Policy, 78, 763–780. https://doi.org/10.1016/j.landusepol.2018.07.034.

    Article  Google Scholar 

  • Alsaaod, M., Schaefer, A., Büscher, W., & Steiner, A. (2015). The role of infrared thermography as a non-invasive tool for the detection of lameness in cattle. Sensors, 15, 14513–14525. https://doi.org/10.3390/s150614513.

    Article  Google Scholar 

  • Arslan, S., & Colvin, T. S. (2002). Grain yield mapping: Yield sensing, yield reconstruction, and errors. Precision Agriculture, 3, 135–154.

    Article  Google Scholar 

  • Aydin, A., Bahr, C., & Berckmans, D. (2013). An innovative monitoring system to measure the feed intake of broiler chickens using pecking sounds. Precision livestock farming 2013 – Papers presented at the 6th European Conference on Precision Livestock Farming, ECPLF 2013, pp. 926–936.

    Google Scholar 

  • Banhazi, T. M., Babinszky, L., Halas, V., & Tscharke, M. (2012). Precision livestock farming: Precision feeding technologies and sustainable livestock production. International Journal of Agricultural & Biological Engineering, 5(4), 54–61. https://doi-org.proxy-remote.galib.uga.edu/10.3965/j.ijabe.20120504.006.

    Google Scholar 

  • Baptista, E. S., Baptista, F. J., & Castro, J. A. (2013). Environmental and endocrine assessment of sheep welfare in a climate-controlled room. 6th European conference on precision livestock farming, pp. 397–406.

    Google Scholar 

  • Boghossian, A., Linsky, S., & Brown, A. (2018). Threats to precision agriculture. 2018 Public-private analytic exchange program.

    Google Scholar 

  • Brown, R. M., Dillon, C. R., Schieffer, J., & Shockley, J. M. (2015). The carbon footprint and economic impact of precision agriculture technology on a corn and soybean farm. Journal of Environmental Economics and Policy, 5, 335–348. https://doi.org/10.1080/21606544.2015.1090932.

    Article  Google Scholar 

  • Bullock, D. S., Kitchen, N., & Bullock, D. G. (2007). Multidisciplinary teams: A necessity for research in precision agriculture systems. Crop Science, 47(5), 1765–1769. https://doi.org/10.2135/cropsci2007.05.0280

  • Cameron, K., & Hunter, P. (2002). Using spatial models and kriging techniques to optimize long-term ground-water monitoring networks: A case study. Environmetrics, 13, 629–656. https://doi.org/10.1002/env.582.

    Article  Google Scholar 

  • Caria, M., Sara, G., Todde, G., Polese, M., & Pazzona, A. (2019). Exploring smart glasses for augmented reality: A valuable and integrative tool in precision livestock farming. Animals, 9(11), pii: E903. https://doi-org.proxy-remote.galib.uga.edu/10.3390/ani9110903.

    Article  Google Scholar 

  • Castle, M., Lubben, B & Luck, J. (2015). Precision agriculture usage and big agriculture data. https://agecon.unl.edu/cornhusker-economics/2015/precision-agriculture-usage-and-big-agriculture-data. Accessed 21 June 2019.

  • Coble, K. H., Mishra, A., Ferrell, S., & Griffin, T. (2018). Big data in agriculture: A challenge for the future. Applied Economic Perspectives and Policy, 40, 79–96.

    Article  Google Scholar 

  • D’Eath, R. B., Jack, M., & Futro, A. (2018). Automatic early warning of tail biting in pigs: 3D cameras can detect lowered tail posture before an outbreak. PLoS One, 13(4), e0194524. https://doi.org/10.1371/journal.pone.0194524.

  • Dalezios, N. R., Dercas, N., Spyropoulos, N. V., & Psomiadis, E. (2017). Water availability and requirements for precision agriculture in vulnerable agroecosystems. European Water, 59, 387–394.

    Google Scholar 

  • Dalezios, N. R., Dercas, N., Spyropoulos, N. V., & Psomiadis, E. (2019). Remotely sensed methodologies for crop water availability and requirements in precision farming of vulnerable agriculture. Water Resources Management, 33, 1499–1519. https://doi.org/10.1007/s11269-018-2161-8.

    Article  Google Scholar 

  • Dicks, L. V., Rose, D. C., & Ang, F. (2018). What agricultural practices are most likely to deliver ‘sustainable intensification’ in the UK? Food and Energy Security, 8(1), e00148. https://doi.org/10.1002/fes3.148.

  • Díez, M., Moclan, C., Romo, A., & Pirondini, F. (2014). High-resolution super-multitemporal monitoring: Two-day time series for precision agriculture applications. https://iafastro.directory/iac/archive/browse/IAC-14/B1/5/26742/

  • Duhan, J. S., Kumar, R., & Kumar, N. (2017). Nanotechnology: The new perspective in precision agriculture. Biotechnology Reports, 15, 11–23. https://doi.org/10.1016/j.btre.2017.03.002.

  • Elarab, M., Ticlavilca, A. M., & Torres-Rua, A. F. (2015). Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture. International Journal of Applied Earth Observation and Geoinformation, 43, 32–42. https://doi.org/10.1016/j.jag.2015.03.017.

  • Fontana, I., Tullo, E., & Fernandez, A. (2015). Frequency analysis of vocalisation in relation to growth in broiler chicken. 7th European conference on precision livestock farming, At Milan, Italy.

    Google Scholar 

  • Fort, H., Dieguez, F., Halty, V., & Lima, J. M. S. (2017). Two examples of application of ecological modeling to agricultural production: Extensive livestock farming and overyielding in grassland mixtures. Ecological Modelling, 357, 23–34. https://doi.org/10.1016/j.ecolmodel.2017.03.023.

    Article  Google Scholar 

  • Gago, J., Douthe, C., & Coopman, R. (2015). UAVs challenge to assess water stress for sustainable agriculture. Agricultural Water Management, 153, 9–19. https://doi.org/10.1016/j.agwat.2015.01.020.

  • Gómez-Candón, D., Castro, A. I. D., & López-Granados, F. (2013). Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision Agriculture, 15, 44–56. https://doi.org/10.1007/s11119-013-9335-4.

    Article  Google Scholar 

  • Haboudane, D., Miller, J. R., & Tremblay, N. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81, 416–426. https://doi.org/10.1016/s0034-4257(02)00018-4.

  • Halachmi, I. (2015). Precision livestock farming applications: Making sense of sensors to support farm management. Wageningen: Wageningen Academic.

    Book  Google Scholar 

  • Hamrita, T., & Paulishen, M. (2011). Advances in management of poultry production using biotelemetry. In Modern telemetry. Rijeka, Croatia: InTech. https://doi.org/10.5772/24691.

    Chapter  Google Scholar 

  • Hamrita, T., Tollner, E., & Schafer, R. (1996). Towards a robotic farming vision: Advances in sensors and controllers for agricultural system applications. IAS 96 conference record of the 1996 IEEE industry applications conference thirty-first IAS annual meeting. https://doi.org/10.1109/ias.1996.559293.

  • Hamrita, T., Tollner, E., & Schafer, R. (2000). Toward fulfilling the robotic farming vision: Advances in sensors and controllers for agricultural applications. IEEE Transactions on Industry Applications, 36, 1026–1032. https://doi.org/10.1109/28.855956.

    Article  Google Scholar 

  • Hamrita, T., Kaluskar, N., & Wolfe, K. (2005). Advances in smart sensor technology. Fortieth IAS annual meeting conference record of the 2005 industry applications conference. https://doi.org/10.1109/ias.2005.1518731.

  • Havránková, J., Godwin, R. J., & Wood, G. A. (2007). The evaluation of ground based remote sensing systems for canopy nitrogen management in winter wheat. Silsoe: Cranfield University.

    Google Scholar 

  • Heiniger, R. W., Havlin, J. L., Crouse, D. A., & Knowles, T. (2002). Seeing is believing: The role of field days and tours in precision agriculture education. Precision Agriculture, 3, 309–318.

    Article  Google Scholar 

  • Important tools to succeed in precision farming. (2018). Precision agriculture. https://precisionagricultu.re/important-tools-to-succeed-in-precision-farming/. Accessed 20 Mar 2020.

  • Ivanov, S., Bhargava, K., & Donnelly, W. (2015). Precision farming: Sensor analytics. IEEE Intelligent Systems, 30, 76–80. https://doi.org/10.1109/mis.2015.67.

    Article  Google Scholar 

  • Jensen H. G., Jacobsen L. B., Pedersen S. M., & Tavella E. (2012). Socioeconomic impact of widespread adoption of precision farming and controlled traffic systems in Denmark. Precision Agriculture, 13(6):661–677.

    Google Scholar 

  • Jørgensen, R. N., Sørensen, C. G., & Jensen, H. F. (2007, June 17–20). FeederAnt – An autonomous mobile unit feeding outdoor pigs. 2007 Minneapolis, Minnesota. https://doi.org/10.13031/2013.22864.

  • Kah, M., & Hofmann, T. (2014). Nanopesticide research: Current trends and future priorities. Environment International, 63, 224–235. https://doi.org/10.1016/j.envint.2013.11.015.

    Article  Google Scholar 

  • Kapurkar, P. M., Kurchania, A. K., & Kharpude, S. N. (2013). GPS and remote sensing adoption in precision agriculture. International Journal of Agricultural Engineering, 6, 221–226.

    Google Scholar 

  • Kerry, R., Oliver, M. A., & Frogbrook, Z. L. (2010). Sampling in precision agriculture. In Geostatistical applications for precision agriculture (pp. 35–63). Dordrecht: Springer. https://doi.org/10.1007/978-90-481-9133-8_2.

    Chapter  Google Scholar 

  • Kitchen, N. R., Snyder, C. J., Franzen, D. W., & Wiebold, W. J. (2002). Educational needs of precision agriculture. Precision Agriculture, 3, 341–351.

    Article  Google Scholar 

  • Kühn, J., Brenning, A., & Wehrhan, M. (2008). Interpretation of electrical conductivity patterns by soil properties and geological maps for precision agriculture. Precision Agriculture, 10, 490–507. https://doi.org/10.1007/s11119-008-9103-z.

  • Kutz, L. J., Miles, G. E., Hammer, P. A., & Krutz, G. W. (1987). Robotic transplanting of bedding plants. Transactions of ASAE, 30, 0586–0590. https://doi.org/10.13031/2013.30443.

    Article  Google Scholar 

  • Lamb, D. W., Frazier, P., & Adams, P. (2008). Improving pathways to adoption: Putting the right Ps in precision agriculture. Computers and Electronics in Agriculture, 61, 4–9. https://doi.org/10.1016/j.compag.2007.04.009.

    Article  Google Scholar 

  • Lasley, P. (1998). Perceived risks and decisions to adopt precision farming methods. 4c Precision Ag Edition 9–9.

    Google Scholar 

  • Lima, E., Hopkins, T., & Gurney, E. (2018). Drivers for precision livestock technology adoption: A study of factors associated with adoption of electronic identification technology by commercial sheep farmers in England and Wales. PLoS One, 13(1), e0190489. https://doi.org/10.1371/journal.pone.0190489.

  • Ma, Y., Wu, H., & Wang, L. (2014). Remote sensing big data computing: Challenges and opportunities. Future Generation Computer Systems, 51, 47–60. https://doi.org/10.1016/j.future.2014.10.029.

  • Marek, T., Almas, L., Amosson, S., & Cox, E. (2001). The feasibility of variable rate irrigation with center pivot systems in the Northern Texas High Plains. 2001 Sacramento, CA, 29 July–1 August 2001. https://doi.org/10.13031/2013.3443.

  • Martelloni, L., Fontanelli, M., Frasconi, C., et al. (2016). Cross-flaming application for intra-row weed control in maize. Applied Engineering in Agriculture, 32, 569–578. https://doi.org/10.13031/aea.32.11114.

    Article  Google Scholar 

  • Maselyne, J., Saeys, W., & Nuffel, A. V. (2013). A health monitoring system for growing-finishing pigs based on the individual feeding pattern using radio frequency identification and synergistic control. Papers presented at the 6th European conference on precision livestock farming, Leuven, pp. 825–833.

    Google Scholar 

  • Matthews, S. G., Miller, A. L., & Clapp, J. (2016). Early detection of health and welfare compromises through automated detection of behavioural changes in pigs. The Veterinary Journal, 217, 43–51. https://doi.org/10.1016/j.tvjl.2016.09.005.

  • Morris, J. E., Cronin, G. M., & Bush, R. D. (2012). Improving sheep production and welfare in extensive systems through precision sheep management. Animal Production Science, 52, 665. https://doi.org/10.1071/an11097.

    Article  Google Scholar 

  • Mulla, D., & Khosla, R. (2016). Historical evolution and recent advances in precision farming. In R. Lal & B. A. Stewart (Eds.), Soil-specific farming precision agriculture (pp. 1–35). Boca Raton: CRC Press. https://doi.org/10.1201/b18759-2.

    Chapter  Google Scholar 

  • National Museum of American History. (2018). Precision farming. In National Museum of American History. Smithsonian. https://americanhistory.si.edu/american-enterprise-exhibition/new-perspectives/precision-farming. Accessed 20 Mar 2020.

  • North Dakota State University. (2018). New precision ag major offered at NDSU – College of Agriculture, Food Systems, and Natural Resources. https://www.ag.ndsu.edu/academics/new-precision-ag-major-offered-at-ndsu. Accessed 27 Jan 2020.

  • O’Shaughnessy, S. A., Evett, S. R., & Colaizzi, P. D. (2019). Identifying advantages and disadvantages of variable rate irrigation: An updated review. Applied Engineering in Agriculture, 35, 837–852. https://doi.org/10.13031/aea.13128.

  • Ozguven, M. M. (2018). The newest agricultural technologies. Current Investigations in Agriculture and Current Research, 5(1), 573–580. https://doi.org/10.32474/ciacr.2018.05.000201.

    Article  Google Scholar 

  • Pandey, G. (2018). Challenges and future prospects of agri-nanotechnology for sustainable agriculture in India. Environmental Technology and Innovation, 11, 299–307. https://doi.org/10.1016/j.eti.2018.06.012.

    Article  Google Scholar 

  • Pastell, M., Hietaoja, J., & Yun, J. (2013). Predicting farrowing based on accelerometer data. The 6th European Conference on Precision Livestock Farming (EC-PLF 2013), Leuven, Belgium.

    Google Scholar 

  • Paustian, M., & Theuvsen, L. (2016). Adoption of precision agriculture technologies by German crop farmers. Precision Agriculture, 18, 701–716. https://doi.org/10.1007/s11119-016-9482-5.

    Article  Google Scholar 

  • Paxton, K. W., Mishra, A. K., & Chintawar, S. (2010, February 6–9). Precision agriculture technology adoption for cotton production. Selected Paper prepared for presentation at the Southern Agricultural Economics Association annual meeting, Orlando, FL.

    Google Scholar 

  • Peña, J. M., Torres-Sánchez, J., & Castro, A. I. D. (2013). Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS One, 8(10), e77151. https://doi.org/10.1371/journal.pone.0077151.

  • Phadikar, S., Das, A. K., & Sil, J. (2012, January). Misclassification and cluster validation techniques for feature selection of diseased rice plant images. Advances in intelligent and soft computing proceedings of the international conference on information systems design and intelligent applications 2012 (INDIA 2012) held in Visakhapatnam, India, pp. 137–144. https://doi.org/10.1007/978-3-642-27443-5_16.

  • Poulopoulou, I., & Chatzipapadopoulos, F. (2015). Saving resources using a cloud livestock farm management tool. 7th European conference on precision livestock farming, pp. 276–283.

    Google Scholar 

  • Pudumalar, S., Ramanujam, E., & Rajashree, R. H. (2017). Crop recommendation system for precision agriculture. 2016 eighth International Conference on Advanced Computing (ICoAC). https://doi.org/10.1109/icoac.2017.7951740.

  • Richard, M.-M., Sloth, K. H., & Veissier, I. (2015). Real time positioning to detect early signs of welfare problems in cows. European conference on precision livestock farming, Milan, Italy, 4pp.

    Google Scholar 

  • Rodríguez, S., Gualotuña, T., & Grilo, C. (2017). A system for the monitoring and predicting of data in precision agriculture in a rose greenhouse based on wireless sensor networks. Procedia Computer Science, 121, 306–313. https://doi.org/10.1016/j.procs.2017.11.042.

    Article  Google Scholar 

  • Rovira-Más, F., Millot, C., & Sáiz-Rubio, V. (2015). Navigation strategies for a Vineyard Robot. 2015 ASABE international meeting. https://doi.org/10.13031/aim.20152189750.

  • Ruiz-Garcia, L., Lunadei, L., Barreiro, P., & Robla, I. (2009). A review of wireless sensor technologies and applications in agriculture and food industry: State of the art and current trends. Sensors, 9, 4728–4750. https://doi.org/10.3390/s90604728.

    Article  Google Scholar 

  • Sáiz-Rubio, V. A., Rovira-Más, F. A., Broseta-Sancho, P. A., & Aguilera-Hernández, R. A. (2015). Robot-generated crop maps for decision-making in Vineyards. 2015 ASABE international meeting. https://doi.org/10.13031/aim.20152189909.

  • Sassi, N. B., Averós, X., & Estevez, I. (2016). Technology and poultry welfare. Animals, 6, 62. https://doi.org/10.3390/ani6100062.

    Article  Google Scholar 

  • Schimmelpfennig, D. (2011). On the doorstep of the information age: Recent adoption of precision agriculture. Washington, DC: U.S. Department of Agriculture, Economic Research Service.

    Google Scholar 

  • Schrøder-Petersen, D. I., & Simonsen, H. B. (2001). Tail biting in pigs. The Veterinary Journal, 162, 196–210. https://doi.org/10.1053/tvjl.2001.0605.

    Article  Google Scholar 

  • Shobha, S., Everitt, J. H., & Fletcher, R. (2008). Geographic information system (GIS) and remote sensing (RS): Undergraduate academic curriculum and precollege training program. IGARSS 2008 – 2008 IEEE International Geoscience and Remote Sensing Symposium. https://doi.org/10.1109/igarss.2008.4779628.

  • Silva, C. B., Do Vale, S. M. L. R., & Pinto, F. A. C. (2007). The economic feasibility of precision agriculture in Mato Grosso do Sul State, Brazil: A case study. Precision Agriculture, 8, 255–265. https://doi.org/10.1007/s11119-007-9040-2.

  • Silva, C. B., De Moraes, M. A. F. D., & Molin, J. P. (2010). Adoption and use of precision agriculture technologies in the sugarcane industry of São Paulo state, Brazil. Precision Agriculture, 12, 67–81. https://doi.org/10.1007/s11119-009-9155-8.

    Article  Google Scholar 

  • Skouby, D. (2017). A content review of precision agriculture courses across the United States. International conference on precision agriculture, July 31–August 4, 2016, St. Louis, Missouri. Available: https://www.ispag.org/proceedings/?action=abstract&id=2186

  • Srinivasan, A. (2006). Handbook of precision agriculture. New York: Food Products Press. https://doi.org/10.1201/9781482277968.

    Book  Google Scholar 

  • Tarrío, P. M., Bernardos, A. M., Casar, J. R., & Besada, J. A. (2006, July 23–25). A harvesting robot for small fruit in bunches based on 3-D stereoscopic vision. Computers in agriculture and natural resources, Orlando, Florida. https://doi.org/10.13031/2013.21885.

  • Technology Quarterly. (2016). The future of agriculture. The Economist. https://www.economist.com/technology-quarterly/2016-06-09/factory-fresh. Accessed 20 Mar 2020.

  • Thornton, P. K. (2010). Livestock production: Recent trends, future prospects. Philosophical Transactions of the Royal Society, B: Biological Sciences, 365, 2853–2867. https://doi.org/10.1098/rstb.2010.0134.

    Article  Google Scholar 

  • Turner, L. W., Udal, M. C., Larson, B. T., & Shearer, S. A. (2000). Monitoring cattle behavior and pasture use with GPS and GIS. Canadian Journal of Animal Science, 80, 405–413. https://doi.org/10.4141/a99-093.

    Article  Google Scholar 

  • Vasconez, J. P., Cantor, G. A., & Cheein, F. A. A. (2019). Human–robot interaction in agriculture: A survey and current challenges. Biosystems Engineering, 179, 35–48.

    Article  Google Scholar 

  • Vellidis, G., Perry, C. D., & Durrence, J. S. (2001). The peanut yield monitoring system. Transactions of ASAE, 44(4), 775–785.

    Google Scholar 

  • Villeneuve, É., Akle, A. A., & Merlo, C. (2019). Decision support in precision sheep farming. IFAC-Papers OnLine, 51, 236–241. https://doi.org/10.1016/j.ifacol.2019.01.048.

  • Wishart, H., Morgan-Davies, C., & Waterhouse, A. (2015). A PLF approach for allocating supplementary feed to pregnant ewes in an extensive hill sheep system. Precision Livestock Farming, 15, 256–265.

    Google Scholar 

  • Yost, M. A., Kitchen, N. R., & Sudduth, K. A. (2016). Long-term impact of a precision agriculture system on grain crop production. Precision Agriculture, 18, 823–842.

    Google Scholar 

  • Yousefi, M. R., & Razdari, A. M. (2015). Application of Gis and Gps in precision agriculture (a review). International Journal of Advanced Biological and Biomedical Research, 3, 7–9.

    Google Scholar 

  • Yu, P., Li, C., Rains, G., & Hamrita, T. (2011a). Development of the berry impact recording device sensing system: Hardware design and calibration. Computers and Electronics in Agriculture, 79, 103–111. https://doi.org/10.1016/j.compag.2011.08.013.

    Article  Google Scholar 

  • Yu, P., Li, C., Rains, G., & Hamrita, T. (2011b). Development of the berry impact recording device sensing system. Software Computers and Electronics in Agriculture, 77, 195–203. https://doi.org/10.1016/j.compag.2011.05.003.

    Article  Google Scholar 

  • Yun, G., Mazur, M., & Pederii, Y. (2017). Role of unmanned aerial vehicles in precision farming. Proceedings of the National Aviation University, 1(70), 106–112. https://doi.org/10.18372/2306-1472.70.11430.

    Article  Google Scholar 

  • Zehner, N., Niederhauser, J. J., Schick, M., & Umstatter, C. (2019). Development and validation of a predictive model for calving time based on sensor measurements of ingestive behavior in dairy cows. Computers and Electronics in Agriculture, 161, 62–71. https://doi.org/10.1016/j.compag.2018.08.037.

    Article  Google Scholar 

  • Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13, 693–712. https://doi.org/10.1007/s11119-012-9274-5.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Takoi Khemais Hamrita .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Hamrita, T.K., Deal, K., Gant, S., Selsor, H. (2021). Precision Agriculture: An Overview of the Field and Women’s Contributions to It. In: Hamrita, T. (eds) Women in Precision Agriculture. Women in Engineering and Science. Springer, Cham. https://doi.org/10.1007/978-3-030-49244-1_1

Download citation

Publish with us

Policies and ethics