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Irrigation Scheduling using WSN
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 88 - Number 2 |
| Year of Publication: 2014 |
| Authors: Mamoun Hussein Mamoun, Said A. Shokry |
10.5120/15326-3643
|
Mamoun Hussein Mamoun, Said A. Shokry . Irrigation Scheduling using WSN. International Journal of Computer Applications. 88, 2 ( February 2014), 37-40. DOI=10.5120/15326-3643
Abstract
Irrigation scheduling is a planning and decision making process, the primary decision being: how much water to apply and when to apply it. Agricultural field figures can be obtained either by direct measurements with sensors placed close to the soil, or by remote sensing with sensors placed in aircrafts or satellites. In remote sensing data are obtained from the electromagnetic wave reflected by soil and vegetation, especially in the bands of visible light, infra-red and microwaves. Microwaves reflection and absorption are strongly affected by soil and vegetation water content, making it possible to estimate these characteristics with the help of radiometric sensors. However, conducting the measurements at land level is advantageous over remote sensing, since it is not affected neither by weather nor field surface conditions. Besides, larger amount of data with better resolution in space and time can be obtained at land level if a large number of special purpose sensors is used. This paper proposes a newly method based on direct measurements using wireless sensor network (WSN) to map the vegetation water content in order to estimate the soil moisture in agricultural fields in coincident with RADARSAT-2 multipolarized information The soil moisture is evaluated over six wheat fields. Results show that, the relative error in retrieving the soil moisture can be degraded through direct measurements by using WSN.
References
- Heidemann, J. & Govindan, R. Embedded Sensor Networks. In Handbook of Networked and Embedded Control Systems. D. Hristu-Varsakelis and W. S. Levine, editors. Springer Verlag, 18p. , 2004.
- Magri, A. Van Es, H. M, Glos, M. A. and Cox, W. J. "Soil Test, Aerial Image and Yield Data as Inputs for Site-specific Fertility and Hybrid Management Under Revista Maize. Precision Agriculture", Springer Science. pp. 87- 110, 2005
- National Research Council - Committee On Assessing Crop Yield: Site-specific Farming. Precision Agriculture In The 21st Century: Geospatial And Information Technologies In Crop Management. National Academy Press, Washington, USA. 149p. , 1997.
- Mallarino, A. P. & Wittry D. J. "Efficacy of Grid and Zone Soil Sampling Approaches for Site-Specific Assessment of Phosphorus, Potassium, pH, and Organic Matter", Precision Agriculture, Kluwer Academic Publishers, Netherlands, vol. 5, pp. 131-144, 2004.
- Hatfield, J. L. Precision Agriculture and Environmental Quality: Challenges for Research and Educational. Agricultural Research Service, USDA, Ames, Iowa. Obtained in http://www. arborday. org/PROGRAMS/papers/PrecisionAg. html. .
- Leon, C. T. , Shaw, D. R. , Cox, M. S. , Abshire, M. J. , Ward, B. , Ward law III, M. C. and Watson, C. "Utility of Remote Sensing in Predicting Crop and Soil Characteristics", Precision Agriculture, Kluwer Academic Publishers, vol. 4, pp. 359-384, 2003.
- Hornbuckle, B. K. Radiometric Sensitivity to Soil Moisture Relative to Vegetation Canopy Anisotropy, Canopy Temperature, and Canopy Water Content at 1. 4 GHz; PhD tesis on Electrical Engineering and Atmospheric, Oceanic & Space Sciences. University of Michgan, 135 p. , 2003.
- Adamchuk, V. I. , Hummel, J. W. , Morgan, M. T. , Upadhyaya, S. K. "On-the-go soil sensors for precision agriculture", Computers and Electronics in Agriculture, vol. 44, pp. 71-91, 2004.
- Gherboudj I. . , Magagi R. , Berg A. , Toth B. , Aaron A. , "soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data", REMOTE Sensing of Environment vol. 115, pp. 33-43, 2011.
- Gross bow Technology. Inc(2005). Wireless Sensor Networks. Manual do fabricante, 30p. Disponível em: http://www. xbow. com.
- Chipcon AS. CC1000-MSingle Chip 'Very Low Power RF Transceiver (rev. 2. 1). Manual do fabricante, pp. 48, Disponivel em: http://www. chipcon. com.
- Atmel Corporation. Atmega128L – 8-bit AVR Microcontroller with 128K Bytes – In-System Programmable Flash. Manual do fabricante, 328 p. Disponível: em http://www. atmel. com. .
- Carlos J. , Henrique F. , Jose E. , "Estimating Vegetation Water Content with Wireless Sensor Communication Signals", Instrumentation and Measurement Technology Conference, IMTC 2007
- Wigneron, J-P. , Pardé, M. , Waldteufel, P. , Chanzy, A. , Kerr, Y. , Schmidl, S. and Skou, N. "Characterizing the Dependence of Vegetation Model Parameters on Crop Structure, Incidence Angle, and Polarization at L-Band", IEEE Transactions on Geoscience and Remote Sensing, vol. 42, pp. 416-424, 2004.
- Khabazan S. , Motagh M. , Hosseini M. , "Evaluation of Radar Backscattering Models IEM, OH, and Dubois using L and C-Bands SAR Data over different vegetation canopy covers and soil depths", International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W3, SMPR 2013, pp. 225-230, October, 2013
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