Abstract
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.
Highlights
The optimization of agricultural equipment is an important strategy to improve tool performance, production efficiency, and cultivation outcomes as well as to cope with the food shortage and growing population
Most of the research on the draft force of tillage tools has been focused on measuring the draft force and developing draft prediction models using regression and artificial intelligence models
The results showed that the scaled conjugate gradient algorithm with two hidden layers and 24 neurons in the first layer and 26 neurons in the second layer had the highest simulation accuracy of 89.48% and correlation coefficient of 0.9445 compared to the other training algorithms
Summary
The optimization of agricultural equipment is an important strategy to improve tool performance, production efficiency, and cultivation outcomes as well as to cope with the food shortage and growing population. Predicting the related parameters of implements can improve the quality of field work and increase their efficiency. Cultivators, as agricultural equipment, are applied for various purposes such as weed control [1,2]. The use of cultivators is still one of the most cost-efficient and applicable method. These tools are considered by farmers to promote plant growth through weed control, soil preparation, soil permeability modification to irrigation, mixing of chemical fertilizers and insecticides with soil, providing protection for plants, and increased activity of microorganisms [1,3].
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