Time-series classification vegetables in detecting growth rate using machine learning

Ezahan Hilmi, Zakaria and Mohd Azraai, Mohd Razman and Jessnor Arif, Mat Jizat and Ismail, Mohd Khairuddin and Zelina Zaiton, Ibrahim and Anwar, P. P. Abdul Majeed (2021) Time-series classification vegetables in detecting growth rate using machine learning. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 3 (2). pp. 1-5. ISSN 2637-0883. (Published)

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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%.

Item Type: Article
Uncontrolled Keywords: Feature extraction; Machine learning; Classification; Sensor reading; Chilli plant
Subjects: T Technology > T Technology (General)
T Technology > TJ Mechanical engineering and machinery
Faculty/Division: Institute of Postgraduate Studies
Faculty of Manufacturing and Mechatronic Engineering Technology
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 09 May 2022 01:20
Last Modified: 09 May 2022 01:20
URI: http://umpir.ump.edu.my/id/eprint/33969
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