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Soil and Crop Sensing for Precision Crop Production: An Introduction

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Soil and Crop Sensing for Precision Crop Production

Part of the book series: Agriculture Automation and Control ((AGAUCO))

Abstract

The development of agriculture has experienced the transformation from Agriculture 1.0 to Agriculture 4.0, or from traditional agriculture to smart agriculture. Agriculture 1.0 is the traditional agriculture using human and animal labor as its main resource. With the development of industrial revolution, agricultural machines were emerging, which directly resulted in Agriculture 2.0, featured by agricultural mechanization. With the increasing application of computers, electronics, communication technology, and automation equipment in agriculture, agriculture has stepped into the 3.0 era, characterized by digital agriculture or precision agriculture. Agriculture 4.0, also called smart agriculture or precision agriculture V2.0, is characterized by the application of IoT (Internet of Things), big data, cloud computing, and robots in agriculture. Precision agriculture or smart agriculture relies on the acquisition of field information including the environment, crops, and soil, and the accuracy of sensing data is the cornerstone of smart agriculture applications. Soil and crop sensing technology involves the exploration of sensing mechanism, spectroscopy, biology, microelectronics, remote sensing, sensors, and information processing methods. The platforms of soil and crop sensing are also constantly upgrading and improving. Multidimensional perception fusion is realized by using platforms of different scales, such as satellites, unmanned aerial vehicles (UAVs), and ground vehicles integrated with multiple sensors. Intelligent, convenient, accurate, and energy-saving information acquisition technology will continue to be the research hotspots in the field of smart agriculture.

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References

  • Adnan N, Nordin SM, Rahman I, Noor A (2018) The effects of knowledge transfer on farmers decision making toward sustainable agriculture practices, in view of green fertilizer technology. World J Sci Technol Sustain Dev 15:98–115

    Article  Google Scholar 

  • Baker J, Colvin TS, Jaynes DB (1996) Potential environmental benefits of adopting precision agriculture. In: Proceedings of the 3rd international conference. Minneapolis, Minnesota, June 23–26, 1996, pp 1051–1052

    Google Scholar 

  • Bannari A, Morin D, Bonnr F, Huete AR (1995) A review of vegetation indices. Remote Sens Rev 13(1–2):95–120

    Article  Google Scholar 

  • Blackmore BS (2000) Develo** the principles of precision farming. In: Proceeding of international conference on engineering and technological sciences. Bei**g, China. October 11, 2000, pp 133–136

    Google Scholar 

  • Braun A, Colangelo E, Steckel T (2018) Farming in the era of industrie 4.0. Proc CIRP 72:979–984

    Article  Google Scholar 

  • Brown M (2018) Smart farming – automated and connected agriculture. https://www.engineering.com/DesignerEdge/DesignerEdgeArticles/ArticleID/16653/Smart-FarmingAutomated-and-Connected-Agriculture.aspx

  • CEMA (2017) A revolution in the making: EU should lend stringent support to Digital Farming and become a ‘world-leader’. 2017 CEMA Summit – FARMING 4.0, (2017-10-12). https://www.cema-agri.org/images/publications/press_releases/Press_Release_2017_CEMA_Summit_12_October_2017.pdf

  • Chen CP, Zhang CY (2014) Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf Sci 275:314–347

    Article  Google Scholar 

  • Clevers JGPW, Büker C, van Leeuwen HJC, Bouman BAM (1994) A framework for monitoring crop growth by combining directional and spectral remote sensing information. Remote Sens Environ 50(2):161–170

    Article  Google Scholar 

  • De Clercq M, Vats A, Biel A (2018) Agriculture 4.0: the future of farming technology. World Government Summit, February, 2018. https://www.worldgovernmentsummit.org/api/publications/document?id=95df8ac4-e97c-6578-b2f8-ff0000a7ddb6

  • Doraiswamy PC, Hatfield JL, Jackson TJ, Akhmedov B, Prueger J, Stern A (2004) Crop condition and yield simulations using Landsat and MODIS. Remote Sens Environ 92:548–559

    Article  Google Scholar 

  • Erickson B, David AW (2015) 2015 precision agricultural services dealership survey results. Purdue University, West Lafayette

    Google Scholar 

  • FAO (2013) Climate-smart agriculture sourcebook. Food and Agriculture Organization of the United Nations, pp 27–30. http://www.fao.org/3/i3325e/i3325e.pdf

  • Fujitsu (2010) Cloud services for agricultural industry. 2010-04-05, Fujitsu Limited. https://www.fujitsu.com/global/about/resources/news/press-releases/2010/0405-02.html

  • Gore A (1998) The digital earth: understanding our planet in the 21st century. Los Angeles. http://www.digitalearth.net.cn

  • Grisso R, Alley M, McClellan P (2009) Precision farming tools: yield monitor. Virginia Cooperative Extension, publication, pp 442–502. https://vtechworks.lib.vt.edu/bitstream/handle/10919/51375/442-502.pdf

  • Huang T, Han P (2012) Rediscussion on the relationship between production relation and productivity as well as production mode. West Forum 22(3):47–54

    Google Scholar 

  • IBM (2008) Smarter Planet, 2008-11-06. https://www.ibm.com/ibm/history/ibm100/us/en/icons/smarterplanet/

  • Ip RHL, Ang LM, Seng KP, Broster JC, Pratley JE (2018) Big data and machine learning for crop protection. Comput Electron Agric 151:376–383

    Article  Google Scholar 

  • Jiang R, Wang P, Xu Y, Zhou ZY, Luo XW, Lan Y, Zhao GP, Sanchez-Azofeifa A, Laakso K (2020) Assessing the operation parameters of a low-altitude UAV for the collection of NDVI values over a paddy rice field. Remote Sens 12:1850. https://doi.org/10.3390/rs12111850

    Article  Google Scholar 

  • Khanna A, Kaur S (2019) Evolution of Internet of Things (IoT) and its significant impact in the field of Precision Agriculture. Comput Electron Agric 157:218–231

    Article  Google Scholar 

  • Li DL (2018) Agriculture 4.0, the approaching age of intelligent agriculture. J Agric 8(1):207–214

    Google Scholar 

  • Li DL, Yang H (2018) State-of-the-art review for Internet of Things in agriculture. Trans Chin Soc Agric Machine 49(1):1–20

    Google Scholar 

  • Li MZ, Zheng LH, An XF, Sun H (2013) Fast measurement and advanced sensors of soil parameters with NIR spectroscopy. Trans CSAM 44(3):73–87

    Google Scholar 

  • Liang Y, Lu XS, Zhang DG, Liang F (2002) The main content, technical support and enforcement strategy of digital agriculture. Geo-Spatial Inf Sci 5(1):68–73

    Article  Google Scholar 

  • Luo X, Zang Y, Zhou ZY (2006) Research progress in farming information acquisition technique for precision agriculture. Trans CSAE 22(1):167–173

    Google Scholar 

  • Marucci A, Colantoni A, Zambon I, Egidi G (2017) Precision farming in hilly areas: the use of network RTK in GNSS technology. Agriculture 7(7):60

    Article  Google Scholar 

  • McKinion JM, Lemmon HE (1985) Expert systems for agriculture. Comput Electron Agric 1:31–40

    Article  Google Scholar 

  • NCPF (2018) National Centre for Precision Farming/UK. (2018-07-11). https://www.harper-adams.ac.uk/research/ncpf/

  • Noguchi N (2017) Smart agriculture toward society 5.0: SIP “Technologies for Creating Next-Generation Agriculture, Forestry and Fisheries”. 2017-07. http://jastip.org/sites/wp-content/uploads/2017/07/prof.Noguchi.pdf

  • O’Grady MJ, O’Hare GM (2017) Modelling the smart farm. Inf Process Agric 4(3):179–187

    Google Scholar 

  • Ozdogan B, Gacar A, Aktas H (2017) Digital agriculture practices in the context of agriculture 4.0. J Econ Finance Account 4(2):186–193

    Google Scholar 

  • Pasquini C (2003) Near infrared spectroscopy: Fundamentals, practical aspects and analytical applications. J Braz Chem Soc 14(2):198–219

    Article  CAS  Google Scholar 

  • Pieruschka R, Schurr U (2019) Plant phenoty**: past, present, and future. Plant Phenomics, Article ID 7507131. https://doi.org/10.34133/2019/7507131

  • Policy Horizons Canada (2014) MetaScan 3-emerging technologies. (2014-03-01). https://horizons.gc.ca/en/2014/03/01/metascan-3-emerging-technologies/

  • Research and Markets (2017) Smart agriculture market to 2025-Global analysis and forecast. (2017-01). https://www.researchandmarkets.com/reports/4375555/smart-agriculture-market-to-2025-global

  • Robert PC, Rust RH, Larson WE (1995) Site-specific management for agricultural systems. ASA-CSSA-SSSA, Madison, pp 13–14

    Book  Google Scholar 

  • Rose DC, Chilvers J (2018) Agriculture 4.0: broadening responsible innovation in an era of smart farming. Front Sustain Food Syst 2(87). https://doi.org/10.3389/fsufs.2018.00087

  • Schueller JK, Bae YH (1987) Spatially-attributed automatic combine date acquisition. Comput Electron Agric 2:119–127

    Article  Google Scholar 

  • Searcy SW, Schueller JK, Bae YH, Borgelt SC, Stout BA (1989) Map** of spatially variable yield during grain combining. Trans Am Soc Agric Eng 32(3):826–829

    Article  Google Scholar 

  • Stafford JV, Hendrick JG (1988) Dynamic sensing of soil pans. Trans Am Soc Agric Eng 31(1):9–13

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Symeonaki E, Arvanitis K, Piromalis D (2017) Review on the trends and challenges of cloud computing technology in climate-smart agriculture. In: Proceedings of the 8th international conference on information and communication technologies in agriculture, food and environment (HAICTA 2017), Chania, Greece, 21–24 September, 2017

    Google Scholar 

  • Tian QJ, Min XJ (1998) Advances in study on vegetation indices. Adv Earth Science 13(4):327–333

    Google Scholar 

  • Trendov NM, Varas S, Zeng M (2019) Digital technologies in agriculture and rural areas, briefing paper. Food and Agriculture Organization of the United Nations, Rome, p 2019

    Google Scholar 

  • Tzounis A, Katsoulas N, Bartzanas T, Kittas C (2017) Internet of Things in agriculture, recent advances and future challenges. Biosyst Eng 164:31–48

    Article  Google Scholar 

  • Walter A, Liebisch F, Hund A (2015) Plant phenoty**: from bean weighing to image analysis. Plant Methods 11:14. https://doi.org/10.1186/s13007-015-0056-8

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang N, Zhang N, Wang MH (2006) Wireless sensors in agriculture and food industry – recent development and future perspective. Comput Electron Agric 50(1):1–14

    Article  CAS  Google Scholar 

  • Wolfert S, Ge L, Verdouw C, Bogaardt MJ (2017) Big data in smart farming – a review. Agric Syst 153:69–80

    Article  Google Scholar 

  • Wu BF, Zhang F, Liu CL, Zhang L, Luo ZM (2004) An integrated method for crop condition monitoring. J Remote Sens 8(6):498–514

    Google Scholar 

  • Yang BJ, Pei ZY (1999) Definition of crop condition and crop monitoring using remote sensing. Trans CSAE 15(3):214–218

    CAS  Google Scholar 

  • Zamora-Izquierdo MA, Santa J, Martínez JA, Martínez V, Skarmeta AF (2019) Smart farming IoT platform based on edge and cloud computing. Biosyst Eng 177:4–17

    Article  Google Scholar 

  • Zhao CJ (2019) State-of-the-art and recommended developmental strategic objectives of smart agriculture. Smart Agric 1(1):1–7

    Google Scholar 

  • Zheng LC, Yu WT, Ma Q, Wang YB (2004) Advances in the integrated evaluation of farmland fertility. Chin J Ecol 23(5):156–161

    Google Scholar 

  • Zhou J, Tardieu F, Pridmore T, Doonan J, Reynold D, Hall N, Griffiths S, Cheng T, Zhu Y, Wang X, Jiang D, Ding Y (2018) Plant phenomics: history, present status and challenges. J Nan**g Agric University 41(4):580–588

    Google Scholar 

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Author information

Authors and Affiliations

  1. China Agricultural University, Bei**g, China

    Han Li & Minzan Li

  2. Agricultural University of Athens, Athens, Greece

    Nikolaos Sygrimis

  3. Washington State University, Prosser, WA, USA

    Qin Zhang

Authors
  1. Han Li
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  2. Minzan Li
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  3. Nikolaos Sygrimis
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  4. Qin Zhang
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Corresponding author

Correspondence to Minzan Li .

Editor information

Editors and Affiliations

  1. College of Information and Electrical Engineering, China Agricultural University, Haidian, Bei**g, China

    Minzan Li

  2. Agricultural Research Service, United States Department of Agriculture, College Station, TX, USA

    Chenghai Yang

  3. Center for Precision and Automated Agricultural Systems, Washington State University, Prosser, WA, USA

    Qin Zhang

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Li, H., Li, M., Sygrimis, N., Zhang, Q. (2022). Soil and Crop Sensing for Precision Crop Production: An Introduction. In: Li, M., Yang, C., Zhang, Q. (eds) Soil and Crop Sensing for Precision Crop Production. Agriculture Automation and Control. Springer, Cham. https://doi.org/10.1007/978-3-030-70432-2_1

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