Energy Prediction of Wheat Production Using Data Mining Technique in Iran

Abstract

Wheat is considered as one of the most important products in Iran. Concerning high cultivation area of wheat in Khuzestan, an instrument is required to process stored data in order to give information resulted from such processing to managers of agricultural sectors. Data mining technique is able to give essential information and models to producers of wheat for modelling energy consumption. One of the most practical algorithms is an artificial neural network. The main aim of this research is to predict output energy of wheat farms using a multilayer perceptron neural network. This is an analytic research and its database consists of 1240 records. Data required for the research was obtained from wheat farm during 2014-2018. Data analysis was done via IBM SPSS modeller 14.2 and standard CRISP. Concerning the model used in the research, it was found that variables of chemical fertilizers, machinery & diesel fuel with coefficients of 0.2987, 0.2064 and 0.1527 respectively had the highest effect on output variable (productive energy). Amount of prediction precision in neural network algorithm, meaning ratio of correctly predicted records to total records was 93.08%. Also, linear correlation between actual values and predicted values were 0.92 and 0.88 respectively, for training data and testing data suggesting strong correlation. The results obtained can be effective for wheat farmers in direction of evaluation and optimization of energy consumption in process of wheat production and reduction of consumption of energy inputs.

Smart Computing Techniques for Predicting Soil Compaction Criteria under Realistic Field Conditions

“…Intelligent computing technology has been used in various disciplines and fields of research in computational sciences with various software technologies such as statistics, machine learning, artificial neural networks (ANN), analysis of fuzzy data, and artificial intelligence -to solve many problems and manage technical processes in all kinds of medical and engineering sciences (Almalki et al, 2016;Kamilaris & Prenafeta-Boldú, 2018;Shafaei et al, 2018;Almaliki et al, 2019;Almaliki et al, 2021;Monjezi, 2021;Monjezi & Hosseinzadeh, 2021).…”