MODELING OF DRAFT AND ENERGY REQUIREMENTS OF A MOLDBOARD PLOW USING ARTIFICIAL NEURAL NETWORKS BASED ON TWO NOVEL VARIABLES

• Autor(a) principal: Al-Janobi,Abdulrahman
• Data de Publicação: 2020
• Outros Autores: Al-Hamed,Saad, Aboukarima,Abdulwahed, Almajhadi,Yousef
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Engenharia Agrícola
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162020000300363
• topic: soil texture index; field working index; artificial neural network; prediction; tillage

ABSTRACT Draft and energy requirements are the most important factors in the activities of farm machinery management owing to their role in matching the tractor with implements for different tillage operations. This study's aim was to model the draft and energy requirements of a moldboard plow based on two novel variables. The first was the soil texture index (STI), which was formed from the clay, sand, and silt contents with a range of 0.03–0.84. The second variable was the field working index (FWI), formed by combining the plow width, plowing speed, soil bulk density, soil moisture content, plowing depth, and tractor power into one dimensionless variable, which had a range of 7.17–82.45. The coefficient of determination (R2) values obtained using a testing dataset were found out to be 0.9134 for energy and 0.8602 for draft requirements. For the draft and energy requirements of the testing data points, the mean absolute errors between the measured values and the values predicted using the artificial neural networks (ANN) model were 0.99 kN and 2.39 kW·h/ha, respectively. Based on comparisons with other results reported using multiple linear regression, it was clear that the predictions by the proposed ANN model were very satisfactory.