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Smart Sensing System for Precision Agriculture

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Technological and Modern Irrigation Environment in Egypt

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Abstract

This chapter presented the state-of-the-art survey of the research literature on how emerging technology used to solve agricultural problems specifically related to precision agriculture (PA). Proximal sensing allows measuring many soil and plant properties in situ. These include portable X-ray, spectroscopy, digital camera, smartphone, multistripe laser triangulation scanning, ground-penetrating radar, and electromagnetic induction sensor. Smart (soil, water, and crop) sensors are utilizing new technology to increase the efficiency of agriculture, enabling agricultural users, reducing and saving the input farming cost, managing the agricultural resources in smart ways, and getting higher profit and productivity. Field estimation of soil–plant analysis is possible and can be evaluated with accuracy levels suitable for soil and plant monitoring requirements. This chapter also proposed a smart-based PA system based on the key technologies: Internet of Things (IoT), cloud computing, smartphone computing, and proximal sensors. Environmental sensors have been utilized in applications according to the need to construct smart PA. The cloud is a gathering of platforms and infrastructures on which data are stored and processed, enabling farmers to recover and transfer their data for a particular mobile application, at any site with Internet access. Joining the cloud, IoT, and sensors is fundamental, with the goal that the sensing data can be stored or handled. The proposed system comprises the sensor layer, the transmission layer, the cloud services layer, and the application layer. At last, the advantages and the possible limitations of the system are talked about.

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References

  1. Omran ESE (2005) Spatial data infrastructure to support land evaluation applications in Egypt. MSc Thesis GIRS-2005–016, Centre for Geo-Information, Wageningen University, The Netherlands

    Google Scholar 

  2. Gore A (1999) The digital Earth: understanding our planet in the 21st century. Photogr Eng Remote Sens 65:528

    Google Scholar 

  3. Bian F, Xie T, Cui X, Zeng Y  (2013) Geo-informatics in resource management and sustainable ecosystem. In: (eds) International symposium, GRMSE 2013, Proceedings, Part 2. Wuhan, China, 8–10

    Google Scholar 

  4. The Economist (2009) Cloud computing: clash of the clouds. http://www.economist.com/node/14637206. Retrieved 09 Oct 2016

  5. Prasad S, Peddoju S, Ghosh D (2013) AgroMobile: a cloud-based framework for agriculturists on mobile platform. Int J Adv Sci Technol 59:41–52

    Google Scholar 

  6. Channe H, Kothari S, Kadam D (2015) Multidisciplinary model for smart agriculture using internet-of-things (IoT), sensors, cloud-computing, mobile-computing & big-data analysis. Int J Comput Technol Appl 6(3):374–382

    Google Scholar 

  7. Mosa ASM, Yoo I, Sheets L (2012) A systematic review of healthcare applications for smartphones. BMC Med Inf Decis Mak 12(1):67

    Google Scholar 

  8. Habib MA, Mohktar MS, Kamaruzzaman SB, Lim KS, Pin TM,  Ibrahim F (2014) Smartphone-based solutions for fall detection and prevention: challenges and open issues. Sensors 14(4):7181–7208

    Google Scholar 

  9. Duan, YE (2011) Design of intelligent agriculture management information system based on IOT.In: International conference on intelligent computation technology and automation (ICICTA), vol 1, pp 1045–1049. 28–29 Mar 2011

    Google Scholar 

  10. Omran ESE (2008) Is soil science dead and buried? Future image in the world of 10 billion people. CATRINA 3(2):59–68

    Google Scholar 

  11. Moran MS, Inoue Y, Barnes EM (1997) Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens Environ 61:319–346

    Google Scholar 

  12. Hartemink AE, Minasny B (2016) Digital soil morphometrics. In: (eds) Progress in soil science

    Google Scholar 

  13. Weindorf D, Zhu Y, Chakraborty S, Bakr N, Huang B (2012) Use of portable X-ray fluorescence spectrometry for environmental quality assessment of peri-urban agriculture. Environ Monit Assess 184:217–227

    Google Scholar 

  14. Ben-Dor E, Taylor RG, Hill J, Demattê JAM, Whiting ML, Chabrillat S, Sommer S (2008) Imaging spectrometry for soil applications. In: Sparks DL (ed) Advances in agronomy, Academic Press, Elsevier 97:321–392

    Google Scholar 

  15. Roudier P, Hedley C, Ross C (2015) Prediction of volumetric soil organic carbon from field-moist intact soil cores. Eur J Soil Sci 66(4):651–660

    Google Scholar 

  16. Omran ESE (2016) Inference model to predict heavy metals of Bahr El Baqar soils, Egypt using spectroscopy and chemometrics technique. Model Earth Syst Environ 3:2: 200

    Google Scholar 

  17. Steffens M, Buddenbaum H (2013) Laboratory imaging spectroscopy of a stagnic luvisol profile—high resolution soil characterisation, classification and mapping of elemental concentrations. Geoderma 195:122–132

    Google Scholar 

  18. Aitkenhead MJ, Coull M, Towers W, Hudson G, Black H I J  (2013) Prediction of soil characteristics and colour using data from the national soils inventory of Scotland. Geoderma 200:99–107

    Google Scholar 

  19. Liles GC, Beaudette D E, O'Geen A T, Horwath W R (2013) Developing predictive soil C models for soils using quantitative color measurements. Soil Sci Soc Am J 77(6):2173–2181

    Google Scholar 

  20. O’Donnell TK, Goyne K W, Miles R J, Baffaut C, Anderson S H, Sudduth K A  (2011) Determination of representative elementary areas for soil redoximorphic features identified by digital image processing. Geoderma 161:138–146

    Google Scholar 

  21. Gong H, Chen C, Bialostozky E, Lawson C T (2012) A GPS/GIS method for travel mode detection in New York City. Comput Environ Urban Syst 36(2):131–139

    Google Scholar 

  22. Anjum A, Ilyas MU (2013) Activity recognition using smartphone sensors. In: Proceedings of the IEEE 10th consumer communications and networking conference (CCNC’13), pp 914–919

    Google Scholar 

  23. Chaovalit P, Saiprasert C, Pholprasit T (2014) A method for driving event detection using sax with resource usage exploration on smartphone platform. EURASIP J Wirel Commun Netw 2014(135)

    Google Scholar 

  24. Werner M, Kessel M, Marouane C (2011) Indoor positioning using smartphone camera. In: Proceedings of the international conference on indoor positioning and indoor navigation (IPIN’11), 6(1)

    Google Scholar 

  25. IUSS Working Group WRB, World reference base for soil resources World Soil Resources Reports, 2006. No. 103. FAO, Rome

    Google Scholar 

  26. Ibanez-Asensio S, Marques-Mateu A, Moreno-Ramon H, Balasch S  (2013) Statistical relationships between soil colour and soil attributes in semiarid areas. Biosys Eng 116(2):120–129

    Google Scholar 

  27. Humphrey C, O’Driscoll M (2011) Evaluation of soil colors as indicators of the seasonal high water table in coastal North Carolina. Int J Soil Sci 6(2):103–113

    Google Scholar 

  28. Gunal H, Ersahin S, Yetgin B, Kutlu T  (2008) Use of chromameter-measured color parameters in estimating color-related soil variables. Commun Soil Sci Plant Anal 39(5–6):726–740

    Google Scholar 

  29. Soil Survey Staff (2014) Keys to soil taxonomy, 12th edn. USDA-Natural Resources Conservation Service, Washington, DC

    Google Scholar 

  30. Billmeyer F, Saltzman M (1981) Principles of color technology. Wiley, New York, NY

    Google Scholar 

  31. Sánchez-Marañón M, Huertas R, Melgosa M (2005) Colour variation in standard soil-colour charts. Soil Res 43(7):827–837

    Google Scholar 

  32. Viscarra Rossel RA (2008) The soil spectroscopy group and the development of a global spectral library. In: 3rd global workshop on digital soil mapping. Utah State University, Logan, Utah, USA, 30 Sept–3 Oct 2008

    Google Scholar 

  33. Aydemir S, Keskin S, Drees LR (2004) Quantification of soil features using digital image processing (DIP) techniques. Geoderma 119(1–2):1–8

    Article  CAS  Google Scholar 

  34. Pongnumkul S, Chaovalit P, Surasvadi N (2015) Applications of smartphone-based sensors in agriculture: a systematic review of research. J Sens 2015:18 (ID 195308)

    Google Scholar 

  35. Han P, Dong D, Zhao X, Jiao L, Lang Y  (2016) A smartphone-based soil color sensor: for soil type classification. Comput Electron Agric 123:232–241

    Article  Google Scholar 

  36. Gomez-Robledo L, Lopez-Ruiz N, Melgosa M, Palma A, Capitan-Vallvey L, Sanchez-Maranon M  (2013) Using the mobile phone as Munsell soil-colour sensor: an experiment under controlled illumination conditions. Comput Electron Agric 99:200–208

    Article  Google Scholar 

  37. Levin N, Ben-Dor E, Singer A (2005) A digital camera as a tool to measure colour indices and related properties of sandy soils in semi-arid environments. Int J Remote Sens 26(24):5475–5492

    Article  Google Scholar 

  38. Gregory S, Lauzon J, O’Halloran I, Heck R  (2006) Predicting soil organic matter content in southwestern Ontario fields using imagery from high-resolution digital cameras. Can J Soil Sci 86(3):573–584

    Article  CAS  Google Scholar 

  39. Aitkenhead M, Donnelly D, Coull M, Black H  (2013) E-smart: environmental sensing for monitoring and advising in real-time. IFIP Adv Inf Commun Technol 413:129–142

    Article  Google Scholar 

  40. Murphy CP, Bullock P, Turner RH (1977) The measurement and characterisation of voids in soil thin sections by image analysis. Part I. Principles and techniques. Eur J Soil Sci 28(3): 498–508

    Google Scholar 

  41. Bouma J, Jongerius A, Boersma O, Jager A, Schoonderbeek D  (1977) The function of different types of macropores during saturated flow through four swelling soil horizons. Soil Sci Soc Am J 41:945–950

    Article  Google Scholar 

  42. Koppi A, McBratney A (1991) A basis for soil mesomorphological analysis. J Soil Sci 42(1):139–146

    Article  Google Scholar 

  43. Guber A, Pachepsky Y, van Genuchten M, Rawls W, Simunek J, Jacques D, Nicholson T, Cady R  (2006) Field-scale water flow simulations using ensembles of pedotransfer functions for soil water retention. Vadose Zone J 5:234–247

    Article  Google Scholar 

  44. Berger K, Muckenhirn R (1945) Soil profiles of natural appearance mounted with vinylite resin. Proc Soil Sci Soc Am 10:368–370

    Article  Google Scholar 

  45. Brown L (1963) Lacquer cement method of making soil monoliths. University of California, Division of Agricultural Sciences, California Agricultural Experiment Station

    Google Scholar 

  46. Haddad N, Lawrie R, Eldridge S (2009) Improved method of making soil monoliths using an acrylic bonding agent and proline auger. Geoderma 151:395–400

    Article  CAS  Google Scholar 

  47. Hussain I, Das M, Ahamad K, Nath P (2017) Water salinity detection using a smartphone. Sens Actuators B: Chem 239:1042–1050

    Article  CAS  Google Scholar 

  48. Levin S, Krishnan S, Rajkumar S, Halery N, Balkunde P (2016) Monitoring of fluoride in water samples using a smartphone. Sci Total Environ 551–552:101–107

    Article  CAS  Google Scholar 

  49. Gunda N, Naicker S, Shinde S, Kimbahune S, Shrivastava S, Mitra S  (2014) Mobile water kit (MWK): a smartphone compatible low-cost water monitoring system for rapid detection of total coliform and E. coli. Anal Methods 6(16 21):6139–6590

    Google Scholar 

  50. Garcıa A, Erenas M, Marinetto E (2011) Mobile phone platform as portable chemical analyze. Sens Actuators B Chem 156:350–359

    Article  CAS  Google Scholar 

  51. Moonrungsee N, Pencharee S, Peamaroon N (2016) Determination of iron in zeolite catalysts by a smartphone camera-based colorimetric analyzer. Instrum Sci Technol 44(4)

    Google Scholar 

  52. Lopez-Ruiz N, Curto V, Erenas M, Benito-Lopez F, Diamond D, Palma A,  Capitan-Vallvey L  (2014) Smartphone-based simultaneous pH and nitrite colorimetric determination for paper microfluidic devices anal. Chem 86(19):9554–9562

    CAS  Google Scholar 

  53. Prasad S, Peddoju SK, Ghosh D (2014) Energy efficient mobile vision system for plant leaf disease identification. In: Proceedings of the IEEE wireless communications and networking conference (WCNC’14), pp 3314–3319

    Google Scholar 

  54. Rafoss T, Sælid K, Sletten A, Gyland L F, Engravslia L  (2010) Open geospatial technology standards and their potential in plant pest risk management-GPS-enabled mobile phones utilising open geospatial technology standards web feature service transactions support the fighting of fire blight in norway. Comput Electron Agric 74(2):336–340

    Article  Google Scholar 

  55. Saha B, Ali K, Basak P, Chaudhuri A  (2012) Developmentof m-sahayak-the innovative android based application for real-time assistance in Indian agriculture and health sectors. In: Proceedings of the 6th international conference on mobile ubiquitous computing, systems, services and technologies (UBICOMM’12), pp 133–137

    Google Scholar 

  56. Mesas-Carrascosa FJ, Castillejo-Gonz´alez I L, de la Orden M S, Garc´ıa-Ferrer A  (2012) Real-time mobile phone application to support land policy. Comput Electron Agric 85:109–111

    Article  Google Scholar 

  57. Confalonieri R, Foi M, Casa R, and et al (2013) Development of an app for estimating leaf area index using a smartphone. Trueness and precision determination and comparison with other indirect methods. Comput Electron Agric 96:67–74

    Google Scholar 

  58. Frommberger L, Schmid F, Cai C (2013) Micro-mapping with smartphones for monitoring agricultural development. In: Proceedings of the 3rd ACM symposium on computing for development (DEV’13)

    Google Scholar 

  59. Raza S-e-A, Prince G, Clarkson J, Rajpoot N  (2015) Automatic detection of diseased tomato plants using thermal and stereo visible light images. PLoS ONE 10(4):e0123262

    Google Scholar 

  60. Duveiller G, Baret F, Defourny P (2012) Remotely sensed green area index for winter wheat crop monitoring: 10-year assessment at regional scale over a fragmented landscape. Agric Meteorol 166–167:156–168

    Article  Google Scholar 

  61. Gianquinto G, Orsini F, Fecondini M, Mezzetti M, Sambo P, Bona S  (2011) A methodological approach for defining spectral indices for assessing tomato nitrogen status and yield. Eur J Agron 35:135–143

    Article  Google Scholar 

  62. Bagheri N, Ahmad H, Alavipanah S K, Omid M (2013) Multispectral remote sensing for site-specific nitrogen fertilizer management. Pesqui Agropecuária Bras 48(10)

    Google Scholar 

  63. Sumriddetchkajorn S (2013) How optics and photonics is simply applied in agriculture? In: International conference on photonics solutions of Proceedings of SPIE, vol 8883

    Google Scholar 

  64. Intaravanne Y, Sumriddetchkajorn S (2012) Baikhao (rice leaf) app: a mobile device-based application in analyzing the color level of the rice leaf for nitrogen estimation. In: Optoelectronic imaging and multimedia technology II, Proceedings of SPIE, vol 8558. The International Society for Optical Engineering, Washington

    Google Scholar 

  65. Omran E, El-Masry G, Rashad A (2012) A new approach to assess wetting front map by image analysis technique for precision irrigation farming. In: International conference of agricultural engineering CIGR-AgEng2012, Papers Book, Valencia 8–12 July 2012. ISBN: 10-84-615-9928-4

    Google Scholar 

  66. Aroca RV, Gomes R B, Dantas R R, Calbo A G (2013) A wearable mobile sensor platform to assist fruit grading. Sens (Basel) 13(5):6109–6140

    Article  Google Scholar 

  67. Hettipathirana T (2004) Simultaneous determination of parts-per-million level Cr, As, Cd and Pb, and major elements in low level contaminated soils using borate fusion and energy dispersive X-ray fluorescence spectrometry with polarized excitation. Spectrochim Acta Part B 59:223–229

    Google Scholar 

  68. Gianoncelli A,  Castaing J, Ortega L, Dooryhee E, Salomon J, Walter P, Hodeau J, Bordet P  (2008) A portable instrument for in situ determination of the chemical and phase compositions of cultural heritage objects. X-Ray Spectrom 37(4):418–423

    Article  CAS  Google Scholar 

  69. Downs R (2015) Determining mineralogy on mars with the CheMin X-ray diffractometer. Elements 11(1):45–50

    Article  CAS  Google Scholar 

  70. Cannon K, Mustard J, Salvatore M (2015) Alteration of immature sedimentary rocks on Earth and Mars: recording aqueous and surface–atmosphere processes. Earth Planet Sci Lett 417:78–86

    Article  CAS  Google Scholar 

  71. Eck D, Hirmas D, Giménez D (2013) Quantifying soil structure from field excavation walls using multistripe laser triangulation scanning. Soil Sci Soc Am J 77:1319–1328

    Article  CAS  Google Scholar 

  72. Usamentiaga R, Molleda J, Garcia D, Bulnes F  (2014) Removing vibrations in 3D reconstruction using multiple laser stripes. Opt Lasers Eng 53:51–59

    Article  Google Scholar 

  73. Hirmas D et al (2016) Quantifying soil structure and porosity using three-dimensional laser scanning. In: Hartemink AE, Minasny B (eds) Digital soil morphometrics. Springer, Dordrecht

    Google Scholar 

  74. Rossi A, Hirmas D, Graham R, Sternberg P  (2008) Bulk density determination by automated three-dimensional laser scanning. Soil Sci Soc Am J 72:1591–1593

    Article  CAS  Google Scholar 

  75. Subroy V, Giménez D, Hirmas D, Takhistov P  (2012) On determining soil aggregate bulk density by displacement in two immiscible liquids. Soil Sci Soc Am J 76:1212–1216

    Article  CAS  Google Scholar 

  76. Zielinski M, Sánchez M, Romero E, Atique A  (2014) Precise observation of soil surface curling. Geoderma 226–227:85–93

    Article  Google Scholar 

  77. Sanchez M, Atique A, Kim S, Romero E, Zielinski M (2013) Exploring desiccation cracks in soils using a 2D profile laser device. Acta Geotech 8:583–596

    Article  Google Scholar 

  78. Viscarra Rossel R, Webster R (2011) Discrimination of Australian soil horizons and classes from their visible-near infrared spectra. Eur J Soil Sci 62(4):637–647

    Google Scholar 

  79. Waiser T, Morgan C, Brown D, Hallmark C (2007) In situ characterization of soil clay content with visible near-infrared diffuse reflectance spectroscopy. Soil Sci Soc Am J 71(2):389–396

    Article  CAS  Google Scholar 

  80. Viscarra Rossel RA, Cattle S R, Ortega A, Fouad Y (2009) In situ measurements of soil colour, mineral composition and clay content by vis–NIR spectroscopy. Geoderma 150:253–266

    Article  CAS  Google Scholar 

  81. Lagacherie P, Baret F, Feret J, Madeira Netto J, Robbez-Masson J  (2008) Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements. Remote Sens Environ 112:825–835

    Article  Google Scholar 

  82. Steffens M, Kohlpaintner M, Buddenbaum H (2014) Fine spatial resolution mapping of soil organic matter quality in a histosol profile. Eur J Soil Sci 65:827–839

    Article  CAS  Google Scholar 

  83. Viscarra Rossel R, Hicks W (2015) Estimates of soil organic carbon and its fractions with small uncertainty using visible–near infrared transfer functions. Eur J Soil Sci 66:438–450

    Google Scholar 

  84. Van Maarschalkerweerd M, Husted S (2015) Recent developments in fast spectroscopy for plant mineral analysis. Front Plant Sci 6:169

    Article  Google Scholar 

  85. Tremblay N, Wang Z J, Ma B L, Belec C, Vigneault P  (2009) A comparison of crop data measured by two commercial sensors for variable-rate nitrogen application. Precis Agric 10:145–161

    Google Scholar 

  86. Samborski SM, Tremblay N, Fallon E (2009) Strategies to make use of plant sensors-based diagnostic information for nitrogen recommendations. Agron J 101:800–816

    Google Scholar 

  87. Schmidt SB, Pedas P, Laursen K H, Schjoerring J K, Husted S  (2013) Latent manganese deficiency in barley can be diagnosed and remediated on the basis of chlorophyll a fluorescence measurements. Plant Soil Environ 372:417–429

    Google Scholar 

  88. Castro ACM, Meixedo J P, Santos J M, Góis J, Bento- Gonçalves A, Vieira A, Lourenço L  (2015) On sampling collection procedure effectiveness for forest soil characterization. Flamma 6:98–100

    Google Scholar 

  89. Liu X, Xuejun D, Daniel IL (2016) Ground penetrating radar for underground sensing in agriculture: a review. Int Agrophys 30:533–543

    Article  Google Scholar 

  90. Cheng N, Conrad Tang H, Chan C (2013) Identification and positioning of underground utilities using ground penetrating radar (GPR). Sustain Environ Res 23(2):141–152

    Google Scholar 

  91. Doolittle J, Butnor J (2008) Chapter 6: Soils, peatlands, and biomonitoring. In: Jol HM (ed) Ground penetrating radar: theory and applications. Elsevier, Amsterdam, The Netherlands, pp 179–202

    Google Scholar 

  92. Guo L, Chen J, Cui X, Fan B,  Lin H (2013) Application of ground penetrating radar for coarse root detection and quantification: a review. Plant Soil 362:1–23

    Article  CAS  Google Scholar 

  93. Qin Y, Chen X, Zhou K, Klenk P, Roth K,  Sun L (2013) Ground-penetrating radar for monitoring the distribution of near-surface soil water content in the Gurbantünggüt Desert. Environ Earth Sci 70:2883–2893

    Article  Google Scholar 

  94. Van Dam RL (2014) Calibration functions for estimating soil moisture from GPR dielectric constant measurements. Comm Soil Sci Plant Anal 45:392–413

    Article  CAS  Google Scholar 

  95. Mahmoudzadeh M, Francés A, Lubczynski M, Lambot S (2012) Using ground penetrating radar to investigate the water table depth in weathered granites-Sardon case study. Spain J Appl Geophys 79:17–26

    Article  Google Scholar 

  96. Tosti F, Patriarca C, Slob E, Benedetto A, Lambot S  (2013) Clay content evaluation in soils through GPR signal processing. J Appl Geophys 97:69–80

    Article  Google Scholar 

  97. Raper RL, Asmussen L, Powell JB (1990) Sensing hard pan depth with ground-penetrating radar. Trans ASAE 33:41–46

    Article  Google Scholar 

  98. Schmelzbach C, Tronicke J, Dietrich P (2012) Highresolution water content estimation from surface-based ground-penetrating radar reflection data by impedance inversion. Wat Resour Res 48:W08505

    Article  Google Scholar 

  99. Barton CV, Montagu KD (2004) Detection of tree roots and determination of root diameters by ground penetrating radar under optimal conditions. Tree Physiol 24:1323–1331

    Article  Google Scholar 

  100. Guo L, Lin H, Fan B, Cui X, Chen J  (2013) Impact of root water content on root biomass estimation using ground penetrating radar: evidence from forward simulations and field controlled experiments. Plant Soil 371:503–520

    Article  CAS  Google Scholar 

  101. Zhu S, Huang C, Su Y, Sato M  (2014) 3D ground penetrating radar to detect tree roots and estimate root biomass in the field. Remote Sens 6:5754–5773

    Article  Google Scholar 

  102. De Benedetto D, Castrignano A, Rinaldi M, Ruggieri S, Santoro F, Figorito B, Gualano S, Diacono M, Tamborrino  R (2013) An approach for delineating homogeneous zones by using multi-sensor data. Geoderma 199:117–127

    Article  Google Scholar 

  103. Tromp-van Meerveld HJ, McDonnell JJ (2009) Assessment of multi-frequency electromagnetic induction for determining soil moisture patterns at the hillslope scale. J Hydrol 368:56–67

    Google Scholar 

  104. Heil K, Schmidhalter U (2012) Characterisation of soil texture variability using apparent electrical conductivity at a highly variable site. Comput Geosci 39:98–110

    Article  Google Scholar 

  105. White ML, Michele L, Shaw JN, Raper R L, Rodekohr D, Wood C (2012) A multivariate approach for high-resolution soil survey development. Soil Sci Aoc Am J 177(5):345–354

    Article  CAS  Google Scholar 

  106. Cockx L, Van Meirvenne M, Vitharana U W A, Verbeke L P C, Simpson D, Saey T, Van Coille F M B  (2009) Extracting topsoil information from EM38DD sensor data using neural network approach. Soil Sci Soc Am J 73(6):1–8

    Article  CAS  Google Scholar 

  107. Harvey OR, Morgan CLS (2009) Predicting regional-scale soil variability using single calibrated apparent soil electrical conductivity model. Soil Sci Soc Am J 73:164–169

    Article  CAS  Google Scholar 

  108. Doolittle J, Chibirka J, Muniz E, Shaw R (2013) Using EMI and P-XRF to characterize the magnetic properties and the concentration of metals in soils formed over different lithologies. Soil Horiz 54(3):1–10

    Google Scholar 

  109. Al-Gaadi K (2012) Employing electromagnetic induction techniques for the assessment of soil compaction. Am J Agric Biol Sci 4:425–434

    Google Scholar 

  110. Triantafilis J, Lesch S M, La Lau K, Buchanan S M  (2009) Field level digital mapping of cation exchange capacity using electromagnetic induction and a hierarchical spatial regression model. Aust J Soil Res 47:651–663

    Article  CAS  Google Scholar 

  111. Vitharana UWA, Van Meirvenne M, Simpson D, Cockx L,  De Baerdemaeker J  (2008) Key soil and topographic properties to delineate potential management classes for precision agriculture in the European loess area. Geoderma 143:206–215

    Article  CAS  Google Scholar 

  112. Van Meirvenne M, Islam M M, De Smedt P, Meerschman E, Van De Vijver E, Saey T  (2013) Key variables for the identification of soil management classes in the aeolian landscapes of North–West Europe. Geoderma 199:99–105

    Article  Google Scholar 

  113. Martinez G, Vanderlinden K, Ordóñez R, Muriel J L  (2009) Can apparent electrical conductivity improve the spatial characterization of soil organic carbon? Vadose Zone J 8(3):586–593

    Article  CAS  Google Scholar 

  114. Wienhold BJ, Doran JW (2008) Apparent electrical conductivity for delineating spatial variability in soil properties. In: Allred BJ, Daniels JJ, Ehsani MR (eds) Handbook of agricultural geophysics. CRC Press, Taylor and Francis Group, Boca Raton, Florida, pp 211–215

    Google Scholar 

  115. Shaner DL, Kosla R, Brodahl M K, Buchleiter G W,  Farahani H J  (2008) How well do zone sampling based soil electrical conductivity maps represent soil variability? Agron J 100(5):1472–1480

    Article  Google Scholar 

  116. Johnston MA, Savage M J, Moolman J H, du Plessis H M  (1997) Evaluation of calibration methods for interpreting soil salinity from electromagnetic induction measurements. Soil Sci Soc Am J 61:1627–1633

    Article  CAS  Google Scholar 

  117. Lesch SM, Herrero J, Rhoades JD (1998) Monitoring for temporal changes in soil salinity using electromagnetic induction techniques. Soil Sci Soc Am J 62:232–242

    Article  CAS  Google Scholar 

  118. Doolittle J, Brevik EC (2014) The use of electromagnetic induction techniques in soils studies. Publications from USDA-ARS/ UNL Faculty. Paper 1462. http://digitalcommons.unl.edu/usdaarsfacpub/1462

  119. Cassel F, Goorahoo D, Zoldoske D, Adhikari D (2009) Mapping soil salinity using ground-based electromagnetic induction. In: Metternicht G, Zinck JA (eds) Remote sensing of soil salinization. CRC Press, Taylor and Francis Group, Boca Raton, Florida, pp 199–233

    Google Scholar 

  120. Morris ER (2009) Height-above-ground effects on penetration depth and response of electromagnetic induction soil conductivity meters. Comput Electron Agric 68:150–156

    Article  Google Scholar 

  121. Omran ESE (2016) Early sensing of peanut leaf spot using spectroscopy and thermal imaging. Arch Agron Soil Sci 1–14

    Google Scholar 

  122. Sawaya WN (2000) Proposal for the establishment of a regional network for date-palm in the near East and North Africa. A Draft Discuss FAO/RNE

    Google Scholar 

  123. Dembilio Ó, Jacas JA, Llácer E (2009) Are the palms Washingtonia filifera and chamaerops humilis suitable hosts for the red palm weevil, Rhynchophorus ferrugineus (Col. Curculionidae). J Appl Entomol 33:565–567

    Google Scholar 

  124. Mahmud AI, João F, Eleonore RAV (2015) Red palm weevil (Rhynchophorus ferrugineus Olivier, 1790): Threat of Palms. J Biol Sci 15(2):56–67

    Article  Google Scholar 

  125. Faleiro JR (2005) Insight into the management of red palm weevil Rhynchophorus ferrugineus Olivier: based on experiences on coconut in India and date palm in Saudi Arabia, Fundación Agroalimed. Jorn Int Sobre El Picudo Rojo Las Palmeras 27–29:35–57

    Google Scholar 

  126. Yones MS, Arafat SM, Abou Hadid A F, Abd Elrahman H A,  Dahi H F  (2012) Determination of the best timing for control application against cotton leaf worm using remote sensing and geographical information techniques. Egypt J Remote Sens Space Sci 15:151–160

    Google Scholar 

  127. Mozib ME, El-Shafie HA (2013) Effect of red palm weevil, Rhynchophorus ferrugineus (Olivier) infestation on temperature profiles of date palm tree. J Entomol Nematol 5(6):77–83

    Article  Google Scholar 

  128. Li D, Yao Y, Shao Z, Wang L (2014) From digital Earth to smart Earth. Chin Sci Bull 59(8):722–733

    Article  Google Scholar 

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Acknowledgements

Abdelazim Negm acknowledges the partial support of the Science and Technology Development Fund (STDF) of Egypt in the framework of the grant no. 30771 for the project titled “A Novel Standalone Solar-Driven Agriculture Greenhouse—Desalination System: That Grows Its Energy And Irrigation Water” via the Newton-Mosharafa funding scheme.

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Authors and Affiliations

  1. Soil and Water Department, Faculty of Agriculture, Suez Canal University, Ismailia, 41522, Egypt

    El-Sayed E. Omran

  2. Institute of African Research and Studies and Nile Basin Countries, Aswan University, Aswan, Egypt

    El-Sayed E. Omran

  3. Water and Water Structures Engineering Department, Faculty of Engineering, Zagazig University, Zagazig, 44519, Egypt

    Abdelazim M. Negm

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  1. El-Sayed E. Omran
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Correspondence to El-Sayed E. Omran .

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Editors and Affiliations

  1. Faculty of Agriculture, Suez Canal University, Ismailia, Egypt

    El-Sayed E. Omran

  2. Department of Water and Water Structures Engineering, Faculty of Engineering, Zagazig University, Zagazig, Egypt

    Abdelazim M. Negm

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Omran, ES.E., Negm, A.M. (2020). Smart Sensing System for Precision Agriculture. In: Omran, ES., Negm, A. (eds) Technological and Modern Irrigation Environment in Egypt. Springer Water. Springer, Cham. https://doi.org/10.1007/978-3-030-30375-4_5

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