Time-series classification vegetables in detecting growth rate using machine learning
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33969/ http://umpir.ump.edu.my/id/eprint/33969/1/Time%20series%20classification%20vegetables%20in%20detecting%20growth%20rate.pdf |
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| author | Ezahan Hilmi, Zakaria Mohd Azraai, Mohd Razman Jessnor Arif, Mat Jizat Ismail, Mohd Khairuddin Zelina Zaiton, Ibrahim Anwar, P. P. Abdul Majeed |
| author_facet | Ezahan Hilmi, Zakaria Mohd Azraai, Mohd Razman Jessnor Arif, Mat Jizat Ismail, Mohd Khairuddin Zelina Zaiton, Ibrahim Anwar, P. P. Abdul Majeed |
| author_sort | Ezahan Hilmi, Zakaria |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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%. |
| first_indexed | 2025-11-15T03:12:15Z |
| format | Article |
| id | ump-33969 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:12:15Z |
| publishDate | 2021 |
| publisher | Penerbit UMP |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-339692022-05-09T01:20:06Z http://umpir.ump.edu.my/id/eprint/33969/ Time-series classification vegetables in detecting growth rate using machine learning Ezahan Hilmi, Zakaria Mohd Azraai, Mohd Razman Jessnor Arif, Mat Jizat Ismail, Mohd Khairuddin Zelina Zaiton, Ibrahim Anwar, P. P. Abdul Majeed T Technology (General) TJ Mechanical engineering and machinery 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%. Penerbit UMP 2021 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/33969/1/Time%20series%20classification%20vegetables%20in%20detecting%20growth%20rate.pdf 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) https://doi.org/10.15282/mekatronika.v3i2.7159 https://doi.org/10.15282/mekatronika.v3i2.7159 |
| spellingShingle | T Technology (General) TJ Mechanical engineering and machinery Ezahan Hilmi, Zakaria Mohd Azraai, Mohd Razman Jessnor Arif, Mat Jizat Ismail, Mohd Khairuddin Zelina Zaiton, Ibrahim Anwar, P. P. Abdul Majeed Time-series classification vegetables in detecting growth rate using machine learning |
| title | Time-series classification vegetables in detecting growth rate using machine learning |
| title_full | Time-series classification vegetables in detecting growth rate using machine learning |
| title_fullStr | Time-series classification vegetables in detecting growth rate using machine learning |
| title_full_unstemmed | Time-series classification vegetables in detecting growth rate using machine learning |
| title_short | Time-series classification vegetables in detecting growth rate using machine learning |
| title_sort | time-series classification vegetables in detecting growth rate using machine learning |
| topic | T Technology (General) TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/33969/ http://umpir.ump.edu.my/id/eprint/33969/ http://umpir.ump.edu.my/id/eprint/33969/ http://umpir.ump.edu.my/id/eprint/33969/1/Time%20series%20classification%20vegetables%20in%20detecting%20growth%20rate.pdf |