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

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 generat...