It is estimated that half to two-thirds of the increases in food production needed in the future will have to come from irrigated lands, which are currently contributing about 40 percent of the world's food requirement (Serageldin, 1995). The global irrigation scenario, however, is characterized by poor performance, increased demand for higher agricultural productivity, decreased availability of water for agriculture, increasing soil salinity and possible effects of global climate change. A major challenge is to sustain or increase the yield of irrigated agriculture while reducing water consumption (Lenton, 1992).

In the Sultanate of Oman, most cultivated areas received only 100 to 200 mm of rainfall annually (Norman et al., 1998).  As a result, all cultivation relies on irrigation.  Water conservation and water use efficiency has now become a major priority in the government (George, 1996; Norman et al., 1998). There is a pressing need to improve irrigation performance in Oman.

One solution is to automate the irrigation process.  Automation encompasses capabilities such as monitoring soil and climatic conditions and applying water at a given time based on an informed and “intelligent” decision. Abernethy and Pearce (1987) pointed out that performance assessment is the most critical element for improving irrigation management.  An automated irrigation system not only allows a better water use efficiency but it also provides all the necessary information to generate detailed water usage reports which are critical to assess and improve irrigation performance. 

Automated irrigation systems have been developed and used for several years. Fangmeier et al. (1990), for example, have developed an automated irrigation system using plant and soil sensors.  Their system consisted of two infrared thermometers, an aspirated psychrometer, four soil resistance blocks, a data logger, a solar panel, and a 12 V-DC battery. The data logger was programmed to collect measurements from the sensors and determine if irrigation is needed. Their study indicated that the hardware performed well but that inadequate criteria for determining the crop water stress index prevented the system from automatically starting irrigation. Araya et al. (1991) have designed an automated drip irrigation system for Chilean conditions based on the use of a low-cost personal computer. Wanjura et al. (1991) have also developed and tested an automated irrigation system for cotton.  It consisted of sensors located within irrigation scheduling treatments and a PC which controlled individual irrigation lines through MS-DOS operations. Heinemann et al. (1992) evaluated an automated irrigation system for frost protection on a strawberry plantation during autumn frosts.  Their system used a microcomputer to monitor environmental conditions to determine when and how much water to apply.  Their results showed that the system was effective for frost protection and in reducing water usage.  More precisely, their automated system used 76% less water than a conventional system during a mild frost event. Gonzalez et al. (1992) designed and tested a computer-controlled drip irrigation system to estimate evapotranspiration of container-grown plants by monitoring randomly selected plants within a container block and watering on an "as needed" basis. They reported a reduction of 95% or more in total irrigation rate with the computer-controlled treatment compared with a manually-controlled treatment, without reducing plant growth.  More recently, Testezlaf et al. (1997) developed an automated irrigation computer control system for management of greenhouse container plants. The system consisted of soil moisture sensors, a hardware input/output interface, a computer with a software interface, and actuators.  Testezlaf et al. (1997) reported that the control system was reliable in applying water responding to the plant demands.

Using Fuzzy Logic, Koc et al. (1997) have evaluated an automated irrigation system to protect apple buds from cold while reducing water usage.  Their automated irrigation system used up to 75% less water than conventional system.  Fuzzy Logic controls were simulated to simplify the automated intermittent irrigation algorithms.  The simulation results indicated that the use of Fuzzy Logic controllers provide a very useful approach to simplify the automation process.  More recently, Ribeiro et al. (1998) developed a Fuzzy Logic based irrigation control system optimized via neural networks.  They have used artificial neural networks to process the actual input and the expected output data for redefinition of membership functions, to optimize the control system.  Their acquisition system recorded climatic conditions and soil moisture from sensors. This system was implemented and tested using micro-irrigation under plastic mulch with peppers. The authors reported that the system performed satisfactorily in a fully automated manner in response to the climatic and soil moisture variations.  

As described above, much effort has been made to develop automated irrigation systems.  Based on a recent literature survey, only two attempts have been made to incorporate Fuzzy Logic concepts in automated irrigation systems. Moreover, both sensor technology and control logic of irrigation systems need improvements for the widespread use of this new approach (Clemmens, 1990). 

This project was undertaken to develop an automatic irrigation control system using Fuzzy Logic to determine when to irrigate and how much to apply.  The primary objective of this study is to develop an automated irrigation system that integrates: 1) a PC-based monitoring system of ambient factors such as soil moisture, temperature, relative humidity, solar radiation and water availability using a modular distributed I/O system, 2) a decision support system using fuzzy controllers and 3) an automated electrical relay which controls irrigation. The bottom line objective is to develop a robust and intelligent automation system to irrigate crop producing areas in Oman.

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References:

• Abernethy, C. L., and G. R. Pearce. 1987. Research needs in third world irrigation. Wallingford, U.K.: Hydraulics Research Limited.

• Araya, A., Ortiz, H., Torres, A., Van der Meer, E. 1991. Automation of a drip irrigation system.  In Y. Hashimoto and W. Day (ed.):  Mathematical and control applications in agriculture and horticulture. Proceedings of the IFAC-ISHS workshop, Matsuyama, Japan, pp. 433-437.

• Clemmens, A.J. 1990. Feedback Control for Surface Irrigation Management. In: Visions of the Future. ASAE Publication 04-90. American Society of Agricultural Engineers, St. Joseph, Michigan, pp. 255-260.

• Fangmeier, D.D., Garrot, D.J.,  Mancino,C.F. and S.H. Husman. 1990. Automated Irrigation Systems Using Plant and Soil Sensors. In: Visions of the Future. ASAE Publication 04-90. American Society of Agricultural Engineers, St. Joseph, Michigan, pp. 533-537.

• George, E. C. 1996. Desalinization research center. In: Oman Daily Observer, 12 August, p.2. Ministry of Information. 1992. Oman 1992. Sultanate of Oman.

• Gonzalez,R.A., Struve,D.K. and L.C. Brown. 1992. A computer-controlled drip irrigation system for container plant production.  HortTechnology. 2(3):402-407.

• Heinemann,P.H., Morrow,C.T., Stombaugh, T.S., Goulart, B.L. and J. Schlegel. 1992. Evaluation of an automated irrigation system for frost protection. Applied Engineering in Agriculture, 8(6):779-785.

• Koc, A.B., Heinemann, P.H., Morrow, C.T., and R. M. Crassweller. 1997. Evaluation of an automated irrigation system for frost protection of apple buds. ASAE Annual International Meeting, Paper No. 97-3052, Minneapolis, Minnesota, USA.

• Lenton, R. 1992. Irrigation management strategies for the 21st century. Canadian Journal of Development Studies (Special Issue): 121-130.

• Norman, W. R., W. S. Shayya, A. S. Al-Ghafri and I. R. McCann. 1998.  Aflaj irrigation and on-farm water management in northern Oman.  Irrigation and Drainage Systems 12:35-48.

• Ribeiro, R.S., Yoder, R.E., Wilkerson, J.B., and B.D. Russell. 1998.  A fuzzy logic based irrigation control system optimized via neural networks. ASAE Annual International Meeting, Paper No: 98-2169, Orlando, Florida, USA.

• Serageldin, I. 1995. Water resources management: A new policy for sustainable future. Water International 21:15-21.

• Testezlaf, R., Zazueta, F.S. and T.H. Yeager. 1997.  A real-time irrigation control system for greenhouses.  Applied Engineering in Agriculture. 13(3):329-332.

• Wanjura, D.F., Upchurch,D.R. and W. M.  Webb. 1991.  An automated control system for studying microirrigation. ASAE Annual International Meeting, Paper No. 91-2157.