The Application of Multiple Linear Regression and Artificial Neural Network Models for Yield Prediction of Very Early Potato Cultivars before Harvest

• Published in: Agronomy (Basel) Vol. 11; no. 5; p. 885
• Main Authors: Piekutowska, Magdalena, Niedbała, Gniewko, Piskier, Tomasz, Lenartowicz, Tomasz, Pilarski, Krzysztof, Wojciechowski, Tomasz, Pilarska, Agnieszka A., Czechowska-Kosacka, Aneta
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 30.04.2021
• Subjects: Agricultural production; agronomy; Artificial neural networks; Atmospheric models; Crop diseases; Crop yield; crop yield prediction; Cultivars; Decision making; Error analysis; Flowers & plants; Harvest; linear models; Meteorological data; model validation; multiple linear regression; Neural networks; Poland; Potatoes; Regression analysis; Regression models; Risk reduction; Variables; Vegetation; very early potato; yield forecasting; Poland; Europe
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy11050885

Yield forecasting is a rational and scientific way of predicting future occurrences in agriculture—the level of production effects. Its main purpose is reducing the risk in the decision-making process affecting the yield in terms of quantity and quality. The aim of the following study was to generate a linear and non-linear model to forecast the tuber yield of three very early potato cultivars: Arielle, Riviera, and Viviana. In order to achieve the set goal of the study, data from the period 2010–2017 were collected, coming from official varietal experiments carried out in northern and northwestern Poland. The linear model has been created based on multiple linear regression analysis (MLR), while the non-linear model has been built using artificial neural networks (ANN). The created models can predict the yield of very early potato varieties on 20th June. Agronomic, phytophenological, and meteorological data were used to prepare the models, and the correctness of their operation was verified on the basis of separate sets of data not participating in the construction of the models. For the proper validation of the model, six forecast error metrics were used: i.e., global relative approximation error (RAE), root mean square error (RMS), mean absolute error (MAE), and mean absolute percentage error (MAPE). As a result of the conducted analyses, the forecast error results for most models did not exceed 15% of MAPE. The predictive neural model NY1 was characterized by better values of quality measures and ex post forecast errors than the regression model RY1.