APPLICATION OF ARTIFICIAL NEURAL NETWORK (ANN) AND MULTIPLE REGRESSIONS FOR ESTIMATING ASSESSING THE PERFORMANCE OF DRY FARMING WHEAT YIELD IN GHORVEH REGION, KURDISTAN PROVINCE

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HOSSEINI S.M.T.; SIOSEH MARDEH A.; FATHI PARVIZ; SIOSEH MARDEH M.

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Journal: AGRICULTURAL RESEARCH Year:2007 | Volume:7 | Issue:1 Page(s): 41-54

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Title

APPLICATION OF ARTIFICIAL NEURAL NETWORK (ANN) AND MULTIPLE REGRESSIONS FOR ESTIMATING ASSESSING THE PERFORMANCE OF DRY FARMING WHEAT YIELD IN GHORVEH REGION, KURDISTAN PROVINCE

Author(s)

HOSSEINI S.M.T. | SIOSEH MARDEH A. | FATHI PARVIZ | SIOSEH MARDEH M. | Issue Writer Certificate

Keywords

ARTIFICIAL NEURAL NETWORKQ2

WHEAT YIELDQ3

METEOROLOGICAL DATAQ3

REGRESSION MEHODQ3

Abstract

Prediction of crop yield such as wheat has always been an interesting topic for researchers because of its importance in economic planning. The main purpose of such studies is to estimate the crop production before harvesting. Recently, the application of ARTIFICIAL NEURAL NETWORK (ANN) has been developed as a powerful tool which enables to solve accurately the most complicated equations and to perform appropriate numerical analysis. The goal of this study is to develop and evaluate an ANN to predict the rate of dry farming WHEAT YIELD based on METEOROLOGICAL DATA in to Ghorveh Region, Kurdistan province. The METEOROLOGICAL DATA used in this study were: both minimum and maximum average of annual temperatures, mean dew point temperature, relative humidity, monthly and annual precipitation, the average of annual temperature, wind speed, number of frozen, rainy and cloudy days, maximum of daily precipitation and etc for an index period of 1989 - 2000. Different ANN models were developed and the optimum values of network parameters were determined to predict dryland WHEAT YIELD by trial and error procedures. Result of this study showed that WHEAT YIELD in Ghorveh plain has been affected by the amount and distribution of precipitation and the average maximum daily temperature, especially in the middle and ending months during the growth period. The performance of WHEAT YIELD was greatly affected by a slight change in the mentioned parameters. In addition the results showed that ANN model could be used to predict and evaluate the yield before harvesting with a good degree of accuracy. In dry farming as illustrated by results from multiple regression method with a regression coefficient of 94.3%; two factors namely annual precipitation and minimum relative humidity in June have an important bearing on the variation of performance of WHEAT YIELD production in Ghorveh plain.

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APA: Copy

HOSSEINI, S.M.T., SIOSEH MARDEH, A., FATHI, PARVIZ, & SIOSEH MARDEH, M.. (2007). APPLICATION OF ARTIFICIAL NEURAL NETWORK (ANN) AND MULTIPLE REGRESSIONS FOR ESTIMATING ASSESSING THE PERFORMANCE OF DRY FARMING WHEAT YIELD IN GHORVEH REGION, KURDISTAN PROVINCE. AGRICULTURAL RESEARCH, 7(1), 41-54. SID. https://sid.ir/paper/84708/en

Vancouver: Copy

HOSSEINI S.M.T., SIOSEH MARDEH A., FATHI PARVIZ, SIOSEH MARDEH M.. APPLICATION OF ARTIFICIAL NEURAL NETWORK (ANN) AND MULTIPLE REGRESSIONS FOR ESTIMATING ASSESSING THE PERFORMANCE OF DRY FARMING WHEAT YIELD IN GHORVEH REGION, KURDISTAN PROVINCE. AGRICULTURAL RESEARCH[Internet]. 2007;7(1):41-54. Available from: https://sid.ir/paper/84708/en

IEEE: Copy

S.M.T. HOSSEINI, A. SIOSEH MARDEH, PARVIZ FATHI, and M. SIOSEH MARDEH, “APPLICATION OF ARTIFICIAL NEURAL NETWORK (ANN) AND MULTIPLE REGRESSIONS FOR ESTIMATING ASSESSING THE PERFORMANCE OF DRY FARMING WHEAT YIELD IN GHORVEH REGION, KURDISTAN PROVINCE,” AGRICULTURAL RESEARCH, vol. 7, no. 1, pp. 41–54, 2007, [Online]. Available: https://sid.ir/paper/84708/en

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