SILAGE MAIZE YIELD PREDICTION USING ARTIFICIAL NEURAL NETWORKS

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BAGHERI S.; GHEYSARI M.; AYOUBI SH.; LAVAEE N.

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Journal: Journal of Plant Production Research Year:2012 | Volume:19 | Issue:4 Page(s): 77-95

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Information Journal Paper

Title

SILAGE MAIZE YIELD PREDICTION USING ARTIFICIAL NEURAL NETWORKS

Author(s)

BAGHERI S. | GHEYSARI M. | AYOUBI SH. | LAVAEE N. | Issue Writer Certificate

Keywords

MAIZEQ2

IRRIGATIONQ2

NITROGEN FERTILIZERQ2

SENSITIVITY ANALYSISQ3

WEATHER PARAMETERSQ2

Abstract

The increasing demands for agricultural products and pressure on the water and land resources also the problems to generate new data specify the necessity of using suitable models to predict the performance of agricultural products. In this situation, computer models provide the possibility to investigate different management strategies. The objectives of this study were to determine the least important computer input parameters which affecting the silage MAIZE yield using artificial neural networks in different levels of water and nitrogen applications. The experiments included four IRRIGATION levels (0.7, 0.85, 1.0, and 1.13 of crop evapotranspiration, ETc) and three nitrogen fertilization levels (0, 150, and 200 kg N ha-1). The results of artificial neural network analysis showed that when at least three parameters of IRRIGATION, fertilizer and growing degree days (GDD) were introduced as the input of ANN, the model could predict the performance of silage MAIZE with high accuracy. The best validation performance of the model was at step 10 with mean square error of 0.0032. Also the results of SENSITIVITY ANALYSIS indicate that the growing degree days with the coefficient of sensitivity of 9.96 is the most important parameter for predicting of silage MAIZE performance and after that is the amount of IRRIGATION with the sensitivity coefficient of 2.07. The results showed that adding the solar radiation and average relative humidity to the input parameter cause reduction in MSE and increasing the accuracy of the model in the process network training.

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APA: Copy

BAGHERI, S., GHEYSARI, M., AYOUBI, SH., & LAVAEE, N.. (2012). SILAGE MAIZE YIELD PREDICTION USING ARTIFICIAL NEURAL NETWORKS. JOURNAL OF PLANT PRODUCTION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), 19(4), 77-95. SID. https://sid.ir/paper/155972/en

Vancouver: Copy

BAGHERI S., GHEYSARI M., AYOUBI SH., LAVAEE N.. SILAGE MAIZE YIELD PREDICTION USING ARTIFICIAL NEURAL NETWORKS. JOURNAL OF PLANT PRODUCTION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES)[Internet]. 2012;19(4):77-95. Available from: https://sid.ir/paper/155972/en

IEEE: Copy

S. BAGHERI, M. GHEYSARI, SH. AYOUBI, and N. LAVAEE, “SILAGE MAIZE YIELD PREDICTION USING ARTIFICIAL NEURAL NETWORKS,” JOURNAL OF PLANT PRODUCTION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), vol. 19, no. 4, pp. 77–95, 2012, [Online]. Available: https://sid.ir/paper/155972/en

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