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Sensor-Cloud based Precision Agriculture Approach for Intelligent Water Management

  • M. JayalakshmiEmail author
  • V. Gomathi
Research
  • 1 Downloads

Abstract

Water deficits reduce plant growth. Moisture stress affects the development of plant organs, which in turn can have very profound effects on plant growth. Initiation and differentiation of vegetative and reproductive organs, as well as cell division and cell enlargement, are very sensitive to water stress. The size of the vegetative organs in part determines the yield of grain crops, and therefore the yield is often determined before heading or flowering. Factors that determine the size of plant vegetative organs are many, and they are interrelated in a complex manner. Environmental factors rank high among those that determine vegetative growth, and among these environmental factors, nitrogen, soil temperature, and soil water play key roles. In this paper, we present a sensor-cloud based precision agriculture for intelligent water management for effective productivity in agriculture.

Keywords

Smart water management Naive Bayes Sensor cloud Precision agriculture 

References

  1. Adamchuk, V. I., Hummel, J. W., Morgan, M. T., & Upadhyaya, S. K. (2004). On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture,44(1), 71–91.CrossRefGoogle Scholar
  2. Alchanatis, V., & Cohen, Y. (2016). Spectral and spatial methods of hyperspectral image analysis for estimation of biophysical and biochemical properties of agricultural crops. Hyperspectral remote sensing of vegetation (pp. 324–343). Boca Raton: CRC Press.Google Scholar
  3. Apostol, S., Viau, A. A., Tremblay, N., Briantais, J. M., Prasher, S., Parent, L. E., et al. (2003). Laser-induced fluorescence signatures as a tool for remote monitoring of water and nitrogen stresses in plants. Canadian Journal of Remote Sensing,29(1), 57–65.CrossRefGoogle Scholar
  4. Åstrand, B., & Baerveldt, A. J. (2002). An agricultural mobile robot with vision-based perception for mechanical weed control. Autonomous Robots,13(1), 21–35.CrossRefGoogle Scholar
  5. Bakhsh, A., Jaynes, D. B., Colvin, T. S., & Kanwar, R. S. (2000). Spatio-temporal analysis of yield variability for a corn-soybean field in Iowa. Transactions of the ASAE,43, 31.CrossRefGoogle Scholar
  6. Bastiaanssen, W. G., Molden, D. J., & Makin, I. W. (2000). Remote sensing for irrigated agriculture: examples from research and possible applications. Agricultural Water Management,46(2), 137–155.CrossRefGoogle Scholar
  7. Bauer, M. E., & Cipra, J. E. (1973). Identification of agricultural crops by computer processing of ERTS MSS data. LARS Technical Reports. Paper 20. http://docs.lib.purdue.edu/larstech/20. W. Lafayette: Purdue Univ.
  8. Bausch, W. C., & Duke, H. R. (1996). Remote sensing of plant nitrogen status in corn. Transactions of the ASAE,39(5), 1869–1875.CrossRefGoogle Scholar
  9. Bhatti, A. U., Mulla, D. J., & Frazier, B. E. (1991). Estimation of soil properties and wheat yields on complex eroded hills using geostatistics and thematic mapper images. Remote Sensing of Environment,37(3), 181–191.CrossRefGoogle Scholar
  10. 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. International Journal of Computer Technology & Applications,6(3), 374–382.Google Scholar
  11. Chen, J. M. (1996). Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing,22(3), 229–242.CrossRefGoogle Scholar
  12. Corwin, D. L., & Lesch, S. M. (2003). Application of soil electrical conductivity to precision agriculture. Agronomy Journal,95(3), 455–471.CrossRefGoogle Scholar
  13. Crookston, R. K. (2006). A top 10 list of developments and issues impacting crop management and ecology during the past 50 years. Crop Science,46(5), 2253–2262.CrossRefGoogle Scholar
  14. Ferrández-Pastor, F., García-Chamizo, J., Nieto-Hidalgo, M., & Mora-Martínez, J. (2018). Precision agriculture design method using a distributed computing architecture on internet of things context. Sensors,18(6), 1731.CrossRefGoogle Scholar
  15. Karim, F., & Karim, F. (2017). Monitoring system using web of things in precision agriculture. Procedia Computer Science,110, 402–409.CrossRefGoogle Scholar
  16. Lindgren, F., Geladi, P., & Wold, S. (1994). Kernel-based PLS regression; Cross-validation and applications to spectral data. Journal of Chemometrics,8(6), 377–389.CrossRefGoogle Scholar
  17. Link, A., Panitzki, M., Reusch, S., & Robert, P. C. (2002, July). Hydro N-Sensor: Tractor-mounted remote sensing for variable nitrogen fertilization. In Proceedings of the 6th International Conference on Precision Agriculture (pp. 14–17). Madison, USA: ASA/CSSA/SSSA (published on CD).Google Scholar
  18. Link, A., & Reusch, S. (2006). Implementation of site-specific nitrogen application-Status and development of the YARA N-Sensor. In NJF seminar (Vol. 390, pp. 37–41).Google Scholar
  19. Long, D. S., Engel, R. E., & Siemens, M. C. (2008). Measuring grain protein concentration with in-line near infrared reflectance spectroscopy. Agronomy Journal,100(2), 247–252.CrossRefGoogle Scholar
  20. Mamo, M., Malzer, G. L., Mulla, D. J., Huggins, D. R., & Strock, J. (2003). Spatial and temporal variation in economically optimum nitrogen rate for corn. Agronomy Journal,95(4), 958–964.CrossRefGoogle Scholar
  21. Rao, R. N., & Sridhar, B. (2018). IoT based smart crop-field monitoring and automation irrigation system. In 2018 2nd International Conference on Inventive Systems and Control (ICISC) (pp. 478-483). IEEE.Google Scholar
  22. Reeta, R., Pushpavathi, V., Sanchana, R., & Shanmugapriya, V. (2018). A Deterministic Approach For Smart Agriculture Using Iot And Cloud. International Journal of Pure And Applied Mathematics,118(18), 2413–2424.Google Scholar
  23. Suresh, P., & Koteeswaran, S. (2019). An Effective Novel IOT Framework for Water Irrigation System in Smart Precision Agriculture. International Journal of Innovative Technology and Exploring Engineering,8(6), 558–564. (ISSN: 2278-3075).Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National Engineering CollegeTamilnaduIndia

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