Prediction of sweet corn yield depending on cultivation technology parameters by using linear regression and artificial neural network methods

Abstract

Artificial neural networks and linear regression are widely used in particularly all branches of science for modeling and prediction. Linear regression is an old data processing tool, and artificial neural networks are a comparatively new one. The goal of the study was to determine whether artificial neural networks are more accurate than linear regression in sweet corn yield prediction. In the study we used a dataset obtained from field experiments on the technological improvement of sweet corn cultivation. The field experiments were conducted during the period from 2014 to 2016 on dark-chestnut soil under drip irrigated conditions in the Steppe Zone of Ukraine. We studied the impact of the moldboard plowing depths, mineral fertilizer application rates and plant densities on the crop yield. A significant impact of all the studied factors on the sweet corn productivity was proved by using the analysis of variance. The highest yield of sweet corn ears without husks (10.93 t ha–1) was under the moldboard plowing at the depth of 20–22 cm, mineral fertilizers application rate of N120P120, plant density of 65,000 plants ha–1. Data processing by using the linear regression and artificial neural network methods showed that the latter is a great deal better than linear regression in sweet corn yield prediction. Higher accuracy of the artificial neural network prediction was proved by the higher value of the coefficient of determination (R2) – 0.978, in comparison to 0.897 for the linear regression prediction model. We conclude that artificial neural networks are a much better data processing tool, especially, in the life sciences and for prediction of the non-linear natural processes and phenomena. The main disadvantage of the neural network models is their “black box” nature. However, linear regression will not lose its popularity among scientists in the nearest future. Linear regression is a much simpler data analysis tool, it is easier to perform the prediction, but it still provides a sufficiently high level of accuracy.

A Life Factor Approach to the Yield Prediction: a Comparison with a Technological Approach in Reliability and Accuracy

“…The results of the study proved that neural network models provide considerably better accuracy of predictions [Kaul et al 2005;Choubin et al 2016;Lykhovyd 2018]. Besides, these models can operate with non-numeric information that is very helpful in some cases.…”

Feature selection, artificial neural network prediction and experimental testing for predicting breakage rate of maize kernels based on mechanical properties

“…Moreover, the distribution of the errors presents a normal curve. Hence, it can be concluded that the error of the ANN model was systematic error and they were not biased (Lykhovyd, 2018).…”

“…Moreover, the model is adaptive to different inputs via its learning process, which make it powerful for dealing with complex problems in practice (Shanmuganathan, 2016). Moreover, the ANN was widely used in cereal kernel studies and illustrated to have a satisfying performance in prediction problems (Khazaei, Shahbazi, Massah, Nikravesh, & Kianmehr, 2008; Lykhovyd, 2018; Zhou, Kimbeng, Tew, Gravois, & Pontif, 2011). In this research, there were numerous predictors and the relationship between mechanical properties and BR was not clarified by previous studies.…”

An apta-aggregation based machine learning assay for rapid quantification of lysozyme through texture parameters

“…Triplicates were made and their statistical texture values along with the area of the aggregates were loaded as the training data for the neural network with a 6–1 architecture. Neural networking was performed by NeuroXL Predictor, a neural network-based add-in for Microsoft Excel developed by OLSOFT and is capable of advanced data forecasting, classification and clustering [ 46 , 47 ]. The trained neural network was then tested with the data from a new set of Lys samples of pre-determined concentrations.…”