Use of an Artificial Neural Network to Assess the Degree of Training of an Operator of Selected Devices Used in Precision Agriculture

Karolina Trzyniec and Adam Kowalewski
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Karolina Trzyniec: Ergonomics and Production Processes, Department of Machinery Operation, Faculty of Production and Power Engineering, University of Agriculture, 30-149 Cracow, Poland
Adam Kowalewski: Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, 30-059 Cracow, Poland

Energies, 2020, vol. 13, issue 23, 1-17

Abstract: The article concerns the issue of automatic recognition of the moment of achieving the desired degree of training of an operator of devices used in precision agriculture. The aim of the research was to build a neural model that recognizes when an operator has acquired the skill of operating modern navigation on parallel strips used in precision agriculture. To conduct the test, a standard device to assist the operator in guiding the machine along given paths, eliminating overlaps, was selected. The thesis was proven that the moment of operator training (meaning driving along designated paths with an accuracy of up to eight centimeters) can be automatically recognized by a properly selected artificial neural network. This network was learned on the basis of data collected during the observation of the operator training process, using a criterion defined by experts. The data collected in the form of photos of the actual and designated route was converted into numerical data and entered into the network input. The output shows the binary evaluation of the trip. It has been shown that the developed neural model will allow the determining of the moment when operators acquire the skills to drive a vehicle along the indicated path and thus shorten the training time.

Keywords: GPS; artificial neural network; navigation; precision agriculture; operator training (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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