Time-Series Classification Vegetables in Detecting Growth Rate Using Machine Learning

Authors

  • Ezahan Hilmi Zakaria Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan Pahang, Malaysia.
  • Mohd Azraai Mohd Razman Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan Pahang, Malaysia.
  • Jessnor Arif Mat Jizat Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan Pahang, Malaysia.
  • Ismail Mohd Khairuddin Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan Pahang, Malaysia.
  • Zelina Zaiton Ibrahim Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
  • Anwar P. P. Abdul Majeed Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan Pahang, Malaysia.
  • Suhaimi Puteh Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan Pahang, Malaysia.

DOI:

https://doi.org/10.15282/mekatronika.v3i2.7159

Keywords:

Feature extraction, Machine learning, Classification, Sensor reading, Chilli plant

Abstract

IoT based innovative irrigation management systems can help in attaining optimum water-resource utilisation in the exactness farming landscape. This paper presents a clustering of unsupervised learning based innovative system to forecast the irrigation requirements of a field using the sensing of a ground parameter such as soil moisture, light intensity, temperature, and humidity. The entire system has been established and deployed. The sensor node data is gained through a serial monitor from Arduino IDE software collected directly and saved using the computer. Orange and MATLAB software is used to apply machine learning for the visualisation, and the decision support system delivers real-time information insights based on the analysis of sensors data. The plants organise either water or non-water includes weather conditions to gain various types of results. kNN reached 100.0%, SVM achieved 99.0% owhile Naïve Bayes achieved 87.40%.

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Published

2021-07-28

How to Cite

[1]
E. H. Zakaria, “Time-Series Classification Vegetables in Detecting Growth Rate Using Machine Learning”, MEKATRONIKA, vol. 3, no. 2, pp. 1–5, Jul. 2021.

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Section

Original Article

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