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Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed

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  • Gniewko Niedbała

    (Institute of Biosystems Engineering, Faculty of Agronomy and Bioengineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland)

Abstract

The aim of the work was to produce three independent, multi-criteria models for the prediction of winter rapeseed yield. Each of the models was constructed in such a way that the yield prediction can be carried out on three dates: April 15th, May 31st, and June 30th. For model building, artificial neural networks with multi-layer perceptron (MLP) topology were used, on the basis of meteorological data (temperature and precipitation) and information about mineral fertilisation. The data were collected from the years, 2008–2015, from 328 production fields located in Greater Poland, Poland. An assessment of the quality of forecasts produced based on neural models was verified by determination of forecast errors using RAE (relative approximation error), RMS (root mean square error), MAE (mean absolute error) error indicators, and MAPE (mean absolute percentage error). An important feature of the produced prediction models is the ability to realize the forecast in the current agrotechnical year on the basis of the current weather and fertiliser information. The lowest MAPE error values were obtained for the neural model WR15_04 (April 15th) based on the MLP network with structure 15:15-18-11-1:1, which reached 7.51%. Other models reached MAPE errors of 7.85% for model WR31_05 (May 31st) and 8.12% for model WR30_06 (June 30th). The performed sensitivity analysis gave information about the factors that have the greatest impact on winter rapeseed yields. The highest rank of 1 was obtained by two networks for the same independent variable in the form of the sum of precipitation within a period from September 1st to December 31st of the previous year. However, in model WR15_04, the highest rank obtained a feature in the form of a sum of molybdenum fertilization in the current year (MO_CY). The models of winter rapeseed yield produced in the work will be the basis for the construction of new forecasting tools, which may be an important element of precision agriculture and the main element of decision support systems.

Suggested Citation

  • Gniewko Niedbała, 2019. "Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed," Sustainability, MDPI, vol. 11(2), pages 1-13, January.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:2:p:533-:d:199319
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    References listed on IDEAS

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    1. Nantian Huang & Ruiqing Li & Lin Lin & Zhiyong Yu & Guowei Cai, 2018. "Low Redundancy Feature Selection of Short Term Solar Irradiance Prediction Using Conditional Mutual Information and Gauss Process Regression," Sustainability, MDPI, vol. 10(8), pages 1-20, August.
    2. Grahovac, Jovana & Jokić, Aleksandar & Dodić, Jelena & Vučurović, Damjan & Dodić, Siniša, 2016. "Modelling and prediction of bioethanol production from intermediates and byproduct of sugar beet processing using neural networks," Renewable Energy, Elsevier, vol. 85(C), pages 953-958.
    3. Saluja, Rajesh Kumar & Kumar, Vineet & Sham, Radhey, 2016. "Stability of biodiesel – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 866-881.
    4. Park, S.J. & Hwang, C.S. & Vlek, P.L.G., 2005. "Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions," Agricultural Systems, Elsevier, vol. 85(1), pages 59-81, July.
    5. Yuewei Liu & Shenghui Zhang & Xuejun Chen & Jianzhou Wang, 2018. "Artificial Combined Model Based on Hybrid Nonlinear Neural Network Models and Statistics Linear Models—Research and Application for Wind Speed Forecasting," Sustainability, MDPI, vol. 10(12), pages 1-30, December.
    6. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    7. Bussay, Attila & van der Velde, Marijn & Fumagalli, Davide & Seguini, Lorenzo, 2015. "Improving operational maize yield forecasting in Hungary," Agricultural Systems, Elsevier, vol. 141(C), pages 94-106.
    8. Hsu-Yang Kung & Ting-Huan Kuo & Chi-Hua Chen & Pei-Yu Tsai, 2016. "Accuracy Analysis Mechanism for Agriculture Data Using the Ensemble Neural Network Method," Sustainability, MDPI, vol. 8(8), pages 1-11, August.
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