Journal of Information Technology Applications and Management

Korea Data Strategy Society (한국데이터전략학회)
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Fuel Consumption Prediction and Life Cycle History Management System Using Historical Data of Agricultural Machinery

  • Jung Seung Lee (School of Business, Hoseo University) ;
  • Soo Kyung Kim (School of International Business Administration, Dankook University)
  • Received : 2022.08.30
  • Accepted : 2022.10.26
  • Published : 2022.10.31
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Abstract

This study intends to link agricultural machine history data with related organizations or collect them through IoT sensors, receive input from agricultural machine users and managers, and analyze them through AI algorithms. Through this, the goal is to track and manage the history data throughout all stages of production, purchase, operation, and disposal of agricultural machinery. First, LSTM (Long Short-Term Memory) is used to estimate oil consumption and recommend maintenance from historical data of agricultural machines such as tractors and combines, and C-LSTM (Convolution Long Short-Term Memory) is used to diagnose and determine failures. Memory) to build a deep learning algorithm. Second, in order to collect historical data of agricultural machinery, IoT sensors including GPS module, gyro sensor, acceleration sensor, and temperature and humidity sensor are attached to agricultural machinery to automatically collect data. Third, event-type data such as agricultural machine production, purchase, and disposal are automatically collected from related organizations to design an interface that can integrate the entire life cycle history data and collect data through this.

Keywords

Acknowledgement

This research was supported by the Academic Research Fund of Hoseo University in 2020"(20200417)

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