Draft of Primary Tillage Implements in Sandy Loam Soil

Al-Janobi, Abdulrahman; Al-Suhaibani, S. A.

doi:10.13031/2013.19392

Cited by 48 publications

(13 citation statements)

References 4 publications

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“…The correlation coefficient and prediction accuracy for the linear regression model were 0.592 and 61%, respectively, which were significantly lower than the correlation coefficient and prediction accuracy of the ANN model, which were 0.9445 and 89%, respectively. The results are in agreement with those obtained by Al-Janobi and Al-Suhaibani (1998) and Alimardani et al (2009) [4,13]. Equation 6 where F is the draft force (Kgf); D is the working depth (cm); R is the moisture content; C is the soil cone index (MPa); and S is the forward speed (km/h).…”

Section: Comparison Of Ann Model With the Linear Regression Modelsupporting

confidence: 89%

Prediction of Draft Force of a Chisel Cultivator Using Artificial Neural Networks and Its Comparison with Regression Model

et al. 2020

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In this study, artificial neural networks (ANNs) were used to predict the draft force of a rigid tine chisel cultivator. The factorial experiment based on the randomized complete block design (RCBD) was used to obtain the required data and to determine the factors affecting the draft force. The draft force of the chisel cultivator was measured using a three-point hitch dynamometer and data were collected using a DT800 datalogger. A recurrent back-propagation multilayer network was selected to predict the draft force of the cultivator. The gradient descent algorithm with momentum, Levenberg–Marquardt algorithm, and scaled conjugate gradient descent algorithm were used for network training. The tangent sigmoid transfer function was the activation functions in the layers. The draft force was predicted based on the tillage depth, soil moisture content, soil cone index, and forward speed. The results showed that the developed ANNs with two hidden layers (24 and 26 neurons in the first and second layers, respectively) with the use of the scaled conjugate gradient descent algorithm outperformed the networks developed with other algorithms. The average simulation accuracy and the correlation coefficient for the prediction of draft force of a chisel cultivator were 99.83% and 0.9445, respectively. The linear regression model had a much lower accuracy and correlation coefficient for predicting the draft force compared to the ANNs.

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Section: Comparison Of Ann Model With the Linear Regression Modelsupporting

confidence: 89%

Prediction of Draft Force of a Chisel Cultivator Using Artificial Neural Networks and Its Comparison with Regression Model

et al. 2020

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“…The hall effect sensors that would detect magnets were fixed to the apparatus mounted on the tractor (Fig. 1) [21,22,32].…”

Section: Methodsmentioning

confidence: 99%

Toprak İşlemede Pati̇najin Otomati̇k Kontrolü

Soylu

Çarman

2020

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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In this study, a control system that automatically adjusts the working depth of the tractor tillage equipment has been developed in order to keep the wheel slip occurring in agricultural tractors during the tillage activities at the determined limit value. The developed automatic control system continuously measured the wheel slip on the tractor drive wheels and reduced the tillage depth adjusted for the wheel slip increase. While the amount of wheel sip was at the allowed level, the tillage equipment worked at the set depth value. In the study, a driver warning system is also designed to alert the driver to reduce the tillage depth when the amount of wheel slip exceeds the specified limit value. The driver warning system warns the driver visually with the help of colored LEDs and audibly with a buzzer according to the measured wheel slip value. The developed automatic control system was compared separately with the designed driver warning system and the operator's own control. The wheel slip value was calculated by comparing the forward speed of the tractor with the speed of the drive wheels. The automatic control system and driver warning system were installed on a New Holland TD110 agricultural tractor and trials were carried out in real field conditions. As a result of the trials, according to the driver warning system and operator control, it was determined that there was a 3-29% reduction in wheel slip and a 22-30% reduction in fuel consumption in soil tillage activities carried out with automatic control system. On the other hand, the draft force decreased by more than 5% compared to the operator control in the automatic control system. However, it was observed that there was almost no change in the average tillage depth between the control methods.

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“…A combined tillage tool simultaneously uses two or more types of tillage implement to accomplish primary and secondary tillage in a one-pass operation for seedbed preparation (Prem et al., 2016). The potential benefits of combining a passive implement as a primary implement and an active implement as a secondary tillage implement are:Reduction in the number of tillage operations which can reduce sub-soil compaction (Manian et al., 1999), the time of field operation, and fuel and labor costs (Al-Janobi and Al-Suhaibani, 1998; Sahu and Raheman, 2006; Kailappan et al., 2001). Reduction in the draft force due to the soil cutting of the active tool providing some thrust force (Shinners et al., 1990).…”

Section: Introductionmentioning

confidence: 99%

“…Reduction in the number of tillage operations which can reduce sub-soil compaction (Manian et al., 1999), the time of field operation, and fuel and labor costs (Al-Janobi and Al-Suhaibani, 1998; Sahu and Raheman, 2006; Kailappan et al., 2001).…”

Section: Introductionmentioning

confidence: 99%

Specific energy requirements and soil pulverization of a combined tillage implement

Usaborisut

Prasertkan

2019

Heliyon

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Reducing energy use in soil preparation has become increasingly important since it is a major cost in planting. Experiments were conducted with a combined tillage implement consisting of a subsoiler and a rotary harrow to reduce the cost due to step reduction in soil preparation. Three tillage operations, two forward speeds, and two rotational rotor speeds were determined as input factors in this study. Soil clod size, performance parameters, and the specific energy requirements of a combined tillage implement were investigated. The field experiments were using two different soil conditions. Increasing the rotor speed from 299 to 526 rpm decreased the mean soil clod diameter at a depth of 0–200 mm from 22.98 to 19.83 mm and from 31.77 to 26.57 mm for fields 1 and 2, respectively. The specific energy requirement was affected significantly by rotor speed and tillage operation. The specific energy requirements for the combined tillage implement with an on-frame pivot joint and an on-pivotable-shank joint were less by 10.4 and 21.1% and by 18.4 and 24.7%, for fields 1 and 2, respectively, compared to the total power requirement for the separate use of a subsoiler and a rotary harrow.

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Draft of Primary Tillage Implements in Sandy Loam Soil

Cited by 48 publications

References 4 publications

Prediction of Draft Force of a Chisel Cultivator Using Artificial Neural Networks and Its Comparison with Regression Model

Prediction of Draft Force of a Chisel Cultivator Using Artificial Neural Networks and Its Comparison with Regression Model

Toprak İşlemede Pati̇najin Otomati̇k Kontrolü

Specific energy requirements and soil pulverization of a combined tillage implement

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