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| Marey,Samy; Aboukarima,Abdulwahed; Almajhadi,Yousef. |
| ABSTRACT This study examines the capability of an artificial neural network (ANN) approach using a backpropagation-learning algorithm to predict performance parameters for a chisel plow at three field sites with differing soils. The draft force, effective field capacity (EFC), fuel consumption rate (FC), overall energy efficiency (OEE), and rate of plowed soil volume (SVR) were predicted at varying plowing speeds, plowing depths, soil moisture contents, soil bulk densities, soil texture indexes, and tractor powers. Collected field data was divided into a training set (for predicting the required parameters) and testing set (for model validation). For the ANN algorithm, the number of hidden layers, neurons, and transfer functions were varied to construct... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Backpropagation-learning algorithm; Draft; Fuel consumption; Overall energy efficiency. |
| Ano: 2020 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162020000600719 |
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| Al-Janobi,Abdulrahman; Al-Hamed,Saad; Aboukarima,Abdulwahed; Almajhadi,Yousef. |
| 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... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Soil texture index; Field working index; Artificial neural network; Prediction; Tillage. |
| Ano: 2020 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162020000300363 |
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| Aboukarima,Abdulwahed; El-Marazky,Mohamed; Elsoury,Hussien; Zayed,Moamen; Minyawi,Mamdouh. |
| ABSTRACT One of the new crop varieties that have been adopted for high yield is the Egyptian faba bean. However, poor-quality faba bean has reduced economic value. Quality evaluation is thus important and can be performed using computational intelligence. We developed a robust method based on morphological features and artificial neural network for quality grading and classification of Egyptian faba-bean seeds, covering five varieties: Giza3, Giza461, Misr1, Nobarya1, and Sakha1. Fifteen seed morphological features were then calculated, and artificial neural networks classified faba beans into different varieties. The results indicated an overall classification accuracy of 77.5% was achieved in training phase and it was 100% when testing dataset was used.... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Faba bean; Quality; Classification; Artificial neural network; Features. |
| Ano: 2020 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162020000600791 |
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