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Open AccessArticle

Application of Artificial Neural Networks for Yield Modeling of Winter Rapeseed Based on Combined Quantitative and Qualitative Data

1
Institute of Biosystems Engineering, Faculty of Agronomy and Bioengineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
2
Department of Agrobiotechnology, Faculty of Mechanical Engineering, Koszalin University of Technology, Racławicka 15-17, 75-620 Koszalin, Poland
3
Faculty of Information Technology and Visual Communication, Collegium Da Vinci, Tadeusza Kutrzeby 10, 61-719 Poznań, Poland
4
Department of Silviculture, Faculty of Forestry, Poznań University of Life Sciences, Wojska Polskiego 71a, 60-625 Poznań, Poland
5
Institute of Environmental Protection Engineering, Faculty of Environmental Engineering, Lublin University of Technology, Nadbystrzycka 40B, 20-618 Lublin, Poland
6
Department of Forest Engineering, Faculty of Forestry, Poznań University of Life Sciences, Wojska Polskiego 71c, 60-625 Poznań, Poland
*
Author to whom correspondence should be addressed.
Agronomy 2019, 9(12), 781; https://doi.org/10.3390/agronomy9120781
Received: 28 October 2019 / Revised: 16 November 2019 / Accepted: 17 November 2019 / Published: 20 November 2019
Rapeseed is considered as one of the most important oilseed crops in the world. Vegetable oil obtained from rapeseed is a valuable raw material for the food and energy industry as well as for industrial applications. Compared to other vegetable oils, it has a lower concentration of saturated fatty acids (5%–10%), a higher content of monounsaturated fatty acids (44%–75%), and a moderate content of alpha-linolenic acid (9%–13%). Overall, rapeseed is grown in all continents on an industrial scale, so there is a growing need to predict yield before harvest. A combination of quantitative and qualitative data were used in this work in order to build three independent prediction models, on the basis of which yield simulations were carried out. Empirical data collected during field tests carried out in 2008–2015 were used to build three models, QQWR15_4, QQWR31_5, and QQWR30_6. Each model was composed of a different number of independent variables, ranging from 21 to 27. The lowest MAPE (mean absolute percentage error) yield prediction error corresponded to QQWR31_5, it was 6.88%, and the coefficient of determination R2 was 0.69. As a result of the sensitivity analysis of the neural network, the most important independent variable influencing the final rapeseed yield was indicated, and for all the analyzed models it was “The kind of sowing date in the previous year” (KSD_PY). View Full-Text
Keywords: winter rapeseed; artificial neural networks; machine learning; yield prediction; yield simulation; MLP (multi-layer perceptron) network; sensitivity analysis; precision agriculture; sustainable agriculture; sustainability winter rapeseed; artificial neural networks; machine learning; yield prediction; yield simulation; MLP (multi-layer perceptron) network; sensitivity analysis; precision agriculture; sustainable agriculture; sustainability
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Niedbała, G.; Piekutowska, M.; Weres, J.; Korzeniewicz, R.; Witaszek, K.; Adamski, M.; Pilarski, K.; Czechowska-Kosacka, A.; Krysztofiak-Kaniewska, A. Application of Artificial Neural Networks for Yield Modeling of Winter Rapeseed Based on Combined Quantitative and Qualitative Data. Agronomy 2019, 9, 781.

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