Journal of Arid Regions Geographic Studies

Journal of Arid Regions Geographic Studies

DETERMINATION OF SENSOR LOCATIONS FOR MONITORING OF ORCHARDS PARAMETERS USING REMOTE SENSING AND GIS

Author
Soheil Radiom
Abstract
Optimal management of the farm and increasing production efficiency can be achieved by collecting accurate and appropriate information from the fields. The aim of this study is to determine the location of soil moisture sensors in pistachio orchards. For this purpose, initial information was obtained using satellite image processing. Then, using clustering method the information was clustered to different class, representing moisture and canopy cover changes at the garden level, and at each class, the position of every sensor is selected using maximum covering location methods. Classification of garden data demonstrated nine different classes of soil moisture and vegetation. The results showed that in each garden with flood or drip irrigation systems, soil moisture classes with different conditions can be identified. Moreover, the results showed that satellite images can provide valuable information for agricultural area. And also, through proper site selection of sensors, all changes and variation of the desired parameters at the garden level can be measured. Sensor’s site selection confirms that this garden requires at least 9 soil moisture sensors for proper irrigation planning.
Keywords
Internet of Things
Pistachio
Remote Sensing
Soil moisture sensor
Precise Agriculture
Site Selection
Allen, R.G., Pereira, L.S., Howell, T.A., Jensen, M.E.; Evapotranspiration information reporting: I. Factors governing measurement accuracy. Agric. Water Manag, 2011. Bannari, A., Morin, D., Bonn, F., & Huete, A. R. (1995). A review of vegetation indices. Remote sensing reviews, 13(1-2), 95-120. Bastiaanssen, W.G.M.; SEBAL-based Sensible and Latent Heat Fluxes in the Irrigated Gediz Basin, Turkey. Journal of Hydrology, 2000, 229, 87-100. Dursun, M., & Özden, S. (2017). Optimization of soil moisture sensor placement for a PV-powered drip irrigation system using a genetic algorithm and artificial neural network. Electrical Engineering, 99(1), 407-419. Gao, B. C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote sensing of environment, 58(3), 257-266. Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote sensing of Environment, 58(3), 289-298. Goldberg, D. E., and Holland, J. H. (1988). "Genetic algorithms and machine learning." Machine learning. Vol.3, No.2, PP.95-99. Jensen, J. R. (2005). Digital image processing: a remote sensing perspective. Upper Saddle River, NJ: sPrentice Hall. Jourdan, D. B., & de Weck, O. L. (2004, May). Layout optimization for a wireless sensor network using a multi-objective genetic algorithm. In 2004 IEEE 59th Vehicular Technology Conference. VTC 2004-Spring (IEEE Cat. No. 04CH37514) (Vol. 5, pp. 2466-2470). IEEE. Roy, D.P., Wulder, M.A., Loveland, T.R., Woodcock, C.E., Allen, R.G., Anderson, M.C., et al.; Landsat-8: Science and Product Vision for Terrestrial Global Change Research. Remote Sensing of Environment, 2014, 145, 154-172. Santos, C., Lorite, I.J., Allen, R.G., and Tasumi, M.; Aerodynamic Parameterization of the Satellite-Based Energy Balance (METRIC) Model for ET Estimation in Rain fed Olive Orchards of Andalusia, Spain. Water Resources Management, 2012, 26, 3267-3283. Shafi, U., Mumtaz, R., García-Nieto, J., Hassan, S. A., Zaidi, S. A. R., & Iqbal, N. (2019). Precision Agriculture Techniques and Practices: From Considerations to Applications. Sensors, 19(17), 3796. Shen, X., Liang, J., Zeleke, K. T., Liang, Y., Wang, G., Duan, A., ... & Zhang, J. (2018). Optimizing the positioning of soil moisture monitoring sensors in winter wheat fields. Water, 10(12), 1707. Wu, X., Liu, M., & Wu, Y. (2012). In-situ soil moisture sensing: Optimal sensor placement and field estimation. ACM Transactions on Sensor Networks (TOSN), 8(4), 1-30. Zheng, Z., Saghaian, S., & Reed, M. (2012). Factors Affecting the Export Demand for U. S. Pistachios, International Food and Agribusiness Management Review, 15 (3): 99-115. Zotarelli, L., Dukes, M. D., & Paranhos, M. (2013). Minimum number of soil moisture sensors for monitoring and irrigation purposes. This document is HS1222, one of a series of the Horticultural Sciences Department, UF/IFAS Extension. Original publication date July.

  • Receive Date 23 November 2022
  • Publish Date 23 November 2022