Multicriteria Prediction and Simulation of Winter Wheat Yield Using Extended Qualitative and Quantitative Data Based on Artificial Neural Networks

Wheat is one of the main grain species as well as one of the most important crops, being the basic food ingredient of people and livestock. Due to the importance of wheat production scale, it is advisable to predict its yield before harvesting. However, the current models are built solely on the basis of quantitative data. Therefore, the aim of the work was to create three multicriteria models for the prediction and simulation of winter wheat yield, which were made on the basis of extended quantitative and qualitative variables from field research in the year period 2008–2015. Neural networks with MLP (multi-layer perceptron) topology were used to build the following models, which can predict and simulate the yield on three dates: 15 April, 31 May, and 30 June. For this reason, they were designated as follows: QQWW15_4, QQWW31_5, and QQWW30_6. Each model is based on a different number of independent features, which ranges from 19 to 25. As a result of the conducted analyses, a MAPE (mean absolute percentage error) forecast error from 6.63% to 6.92% was achieved. This is equivalent of an error ranging from 0.521 to 0.547 t·ha−1, with an average yield of 6.57 ton per hectare of cultivated area. In addition, the most important quantitative and qualitative factors influencing the yield were also indicated. In the first predictive range (15 April), it is the average air temperature from 1 September to 31 December of the previous year (T9-12_PY). In the second predictive range (31 May) it is the sum of precipitation from 1 May to 31 May, and in the third (30 June) is the average air temperature from 1 January to 15 April of the year (T1-4_CY). In addition, one of the qualitative factors had a significant impact on the yield in the first phase-the type of forecrop in the previous year (TF_PY). The presented neural modeling method is a specific extension of the previously used predicting methods. An element of innovation of the presented concept of yield modeling is the possibility of performing a simulation before harvest, in the current agrotechnical season. The presented models can be used in large-area agriculture, especially in precision agriculture as an important element of decision-making support systems.

[1]  R. Kroebel,et al.  Prospects of Forage Production in Northern Regions under Climate and Land-Use Changes: A Case-Study of a Dairy Farm in Newfoundland, Canada , 2019, Agronomy.

[2]  Kesten C. Green,et al.  Golden Rule of Forecasting: Be Conservative , 2015 .

[3]  Ajai Singh,et al.  Comparison of soil and water assessment tool (SWAT) and multilayer perceptron (MLP) artificial neural network for predicting sediment yield in the Nagwa agricultural watershed in Jharkhand, India , 2012 .

[4]  Reimund P. Rötter,et al.  Multi-wheat-model ensemble responses to interannual climate variability , 2016, Environ. Model. Softw..

[5]  Mohammad Reza Pahlavan Rad,et al.  Application of Artificial Neural Networks to predict the final fruit weight and random forest to select important variables in native population of melon (Cucumis melo L.) , 2015 .

[6]  P. Vlek,et al.  Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions , 2005 .

[7]  J. Kumhálová,et al.  Winter oilseed rape and winter wheat growth prediction using remote sensing methods. , 2016 .

[8]  P. Griffin,et al.  Yield and Price Forecasting for Stochastic Crop Decision Planning , 2010 .

[9]  M. Niazian,et al.  Modeling the seed yield of Ajowan ( Trachyspermum ammi L.) using artificial neural network and multiple linear regression models , 2018, Industrial Crops and Products.

[10]  M. Omid,et al.  A Comparative Study between Artificial Neural Networks and Adaptive Neuro-fuzzy Inference Systems for Modeling Energy Consumption in Greenhouse Tomato Production- A Case Study in Isfahan Province , 2015 .

[11]  G. Niedbała,et al.  An IT system for the simultaneous management of vector and raster images , 2016, International Conference on Digital Image Processing.

[12]  Weixing Cao,et al.  Predicting grain yield and protein content in wheat by fusing multi-sensor and multi-temporal remote-sensing images , 2014 .

[13]  A. Kayad,et al.  Prediction of Potato Crop Yield Using Precision Agriculture Techniques , 2016, PloS one.

[14]  Isabelle Piccard,et al.  Towards Predictive Modeling of Sorghum Biomass Yields Using Fraction of Absorbed Photosynthetically Active Radiation Derived from Sentinel-2 Satellite Imagery and Supervised Machine Learning Techniques , 2019, Agronomy.

[15]  Spyros Makridakis,et al.  The M3-Competition: results, conclusions and implications , 2000 .

[16]  Yi Peng,et al.  Remote Estimation of Biomass in Winter Oilseed Rape (Brassica napus L.) Using Canopy Hyperspectral Data at Different Growth Stages , 2019, Applied Sciences.

[17]  A. Liese,et al.  Explaining Varying Degrees of Openness in the Food and Agriculture Organization of the United Nations (FAO) , 2010 .

[18]  M. Trnka,et al.  Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization , 2015, The Journal of Agricultural Science.

[19]  Alberto González-Sanchez,et al.  Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction , 2014, TheScientificWorldJournal.

[20]  J. Porter,et al.  Temperatures and the growth and development of wheat: a review , 1999 .

[21]  P. Kyle,et al.  Climate change effects on agriculture: Economic responses to biophysical shocks , 2013, Proceedings of the National Academy of Sciences.

[22]  M. Abdipour,et al.  Artificial neural networks and multiple linear regression as potential methods for modeling seed yield of safflower (Carthamus tinctorius L.) , 2019, Industrial Crops and Products.

[23]  G. Cai,et al.  Low Redundancy Feature Selection of Short Term Solar Irradiance Prediction Using Conditional Mutual Information and Gauss Process Regression , 2018, Sustainability.

[24]  Henrique Boriolo Dias,et al.  Evaluation of three sugarcane simulation models and their ensemble for yield estimation in commercially managed fields , 2017 .

[25]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[26]  Rui Zhang,et al.  Derivative Parameters of Hyperspectral NDVI and Its Application in the Inversion of Rapeseed Leaf Area Index , 2018, Applied Sciences.

[27]  G. Niedbała,et al.  Use of computer image analysis methods to evaluate the quality topping sugar beets with using artificial neural networks , 2016, International Conference on Digital Image Processing.

[28]  Tomasz Wojciechowski,et al.  Rapeseed seeds quality classification with usage of VIS-NIR fiber optic probe and artificial neural networks , 2016, 2016 International Conference on Optoelectronics and Image Processing (ICOIP).

[29]  M. van der Velde,et al.  Performance of the MARS-crop yield forecasting system for the European Union: Assessing accuracy, in-season, and year-to-year improvements from 1993 to 2015 , 2019, Agricultural systems.

[30]  G. Niedbała,et al.  Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Wheat , 2019 .

[31]  S. Al-hamed,et al.  Prediction of Potato Yield Based on Energy Inputs using Artificial Neural Networks and C-sharp under Saudi Arabia Conditions , 2016 .

[32]  Andrew J. Challinor,et al.  Assessing uncertainty and complexity in regional-scale crop model simulations , 2017 .

[33]  S. Emamgholizadeh,et al.  Seed yield prediction of sesame using artificial neural network , 2015 .

[34]  Biodiversity of weed communities in common wheat and spelt following various forecrops , 2018, Acta Agrobotanica.

[35]  M. Omid,et al.  A Neural Network Based Modeling and Sensitivity Analysis of Energy Inputs for Predicting Seed and Grain Corn Yields , 2014 .

[36]  Olgierd Hryniewicz,et al.  Application of linguistic summarization methods in time series forecasting , 2019, Inf. Sci..

[37]  Mohsen Niazian,et al.  Modeling Oil Content of Sesame (Sesamum indicum L.) Using Artificial Neural Network and Multiple Linear Regression Approaches , 2018 .

[38]  Juan Frausto-Solís,et al.  Predictive ability of machine learning methods for massive crop yield prediction , 2014 .

[39]  G. Niedbała.  Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed , 2019, Sustainability.

[40]  Robert L. Winkler,et al.  Combining Probability Distributions From Experts in Risk Analysis , 1999 .

[41]  H. Feng,et al.  Within-season yield prediction with different nitrogen inputs under rain-fed condition using CERES-Wheat model in the northwest of China. , 2016, Journal of the science of food and agriculture.

[42]  G. Niedbała.  Simple model based on artificial neural network for early prediction and simulation winter rapeseed yield , 2019, Journal of Integrative Agriculture.

[43]  A. S. Brar,et al.  Simulating response of wheat to timing and depth of irrigation water in drip irrigation system using CERES-Wheat model , 2017 .

[44]  Saeed Samadianfard,et al.  M5 Model Tree and Gene Expression Programming Based Modeling of Sandy Soil Water Movement under Surface Drip Irrigation - TI Journals , 2014 .