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Unsupervised Fertigation and Machine Learning for Crop Vegetation Parameter Analysis

This study proposes an IoT-based smart irrigation management system that can optimize water-resource utilization in a smart agricultural system. The system uses unsupervised learning-based clustering to predict the irrigation needs of a field based on the ground parameters sensed by automated monito...

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Main Authors: Mohd Izzat, Mohd Rahman, Mohd Azraai, Mohd Razman, Abdul Majeed, Anwar P. P., Muhammad Nur Aiman, Shapiee, Muhammad Amirul, Abdullah, Musa, Rabiu Muazu
Format: Article
Language:English
Published: Auricle Global Society of Education and Research 2023
Subjects:
TA Engineering (General). Civil engineering (General)
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/38907/1/Unsupervised%20Fertigation%20and%20Machine%20Learning%20for%20Crop%20Vegetation%20Parameter%20Analysis.pdf
http://umpir.ump.edu.my/id/eprint/38907/
https://www.ijisae.org/index.php/IJISAE/article/view/3183
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spelling my.ump.umpir.389072023-10-17T04:05:01Z http://umpir.ump.edu.my/id/eprint/38907/ Unsupervised Fertigation and Machine Learning for Crop Vegetation Parameter Analysis Mohd Izzat, Mohd Rahman Mohd Azraai, Mohd Razman Abdul Majeed, Anwar P. P. Muhammad Nur Aiman, Shapiee Muhammad Amirul, Abdullah Musa, Rabiu Muazu TA Engineering (General). Civil engineering (General) TS Manufactures This study proposes an IoT-based smart irrigation management system that can optimize water-resource utilization in a smart agricultural system. The system uses unsupervised learning-based clustering to predict the irrigation needs of a field based on the ground parameters sensed by automated monitoring devices. These parameters include soil moisture, light intensity, temperature, and humidity. The system extracts feature such as the maximum, minimum, mean, and standard deviation of four soil moisture sensors from the primary dataset of plants. Then, it applies lag features to enhance the accuracy of the classification model. The system uploads the dataset of 108 features to the Orange GUI and performs k-means clustering to assign cluster labels to the data as meta-attributes in a new dataset. The study evaluates the system using a month’s worth of data and demonstrates its functionality and effectiveness. The system employs machine learning techniques such as Random Forest, Neural Network, and kNN, which achieve 100%, 99.9%, and 99.8% accuracy respectively. Auricle Global Society of Education and Research 2023 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/38907/1/Unsupervised%20Fertigation%20and%20Machine%20Learning%20for%20Crop%20Vegetation%20Parameter%20Analysis.pdf Mohd Izzat, Mohd Rahman and Mohd Azraai, Mohd Razman and Abdul Majeed, Anwar P. P. and Muhammad Nur Aiman, Shapiee and Muhammad Amirul, Abdullah and Musa, Rabiu Muazu (2023) Unsupervised Fertigation and Machine Learning for Crop Vegetation Parameter Analysis. International Journal of Intelligent Systems and Applications in Engineering (IJISAE), 11 (3). pp. 417-425. ISSN 2147-6799. (Published) https://www.ijisae.org/index.php/IJISAE/article/view/3183
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TA Engineering (General). Civil engineering (General)
TS Manufactures
spellingShingle TA Engineering (General). Civil engineering (General)
TS Manufactures
Mohd Izzat, Mohd Rahman
Mohd Azraai, Mohd Razman
Abdul Majeed, Anwar P. P.
Muhammad Nur Aiman, Shapiee
Muhammad Amirul, Abdullah
Musa, Rabiu Muazu
Unsupervised Fertigation and Machine Learning for Crop Vegetation Parameter Analysis
description This study proposes an IoT-based smart irrigation management system that can optimize water-resource utilization in a smart agricultural system. The system uses unsupervised learning-based clustering to predict the irrigation needs of a field based on the ground parameters sensed by automated monitoring devices. These parameters include soil moisture, light intensity, temperature, and humidity. The system extracts feature such as the maximum, minimum, mean, and standard deviation of four soil moisture sensors from the primary dataset of plants. Then, it applies lag features to enhance the accuracy of the classification model. The system uploads the dataset of 108 features to the Orange GUI and performs k-means clustering to assign cluster labels to the data as meta-attributes in a new dataset. The study evaluates the system using a month’s worth of data and demonstrates its functionality and effectiveness. The system employs machine learning techniques such as Random Forest, Neural Network, and kNN, which achieve 100%, 99.9%, and 99.8% accuracy respectively.
format Article
author Mohd Izzat, Mohd Rahman
Mohd Azraai, Mohd Razman
Abdul Majeed, Anwar P. P.
Muhammad Nur Aiman, Shapiee
Muhammad Amirul, Abdullah
Musa, Rabiu Muazu
author_facet Mohd Izzat, Mohd Rahman
Mohd Azraai, Mohd Razman
Abdul Majeed, Anwar P. P.
Muhammad Nur Aiman, Shapiee
Muhammad Amirul, Abdullah
Musa, Rabiu Muazu
author_sort Mohd Izzat, Mohd Rahman
title Unsupervised Fertigation and Machine Learning for Crop Vegetation Parameter Analysis
title_short Unsupervised Fertigation and Machine Learning for Crop Vegetation Parameter Analysis
title_full Unsupervised Fertigation and Machine Learning for Crop Vegetation Parameter Analysis
title_fullStr Unsupervised Fertigation and Machine Learning for Crop Vegetation Parameter Analysis
title_full_unstemmed Unsupervised Fertigation and Machine Learning for Crop Vegetation Parameter Analysis
title_sort unsupervised fertigation and machine learning for crop vegetation parameter analysis
publisher Auricle Global Society of Education and Research
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/38907/1/Unsupervised%20Fertigation%20and%20Machine%20Learning%20for%20Crop%20Vegetation%20Parameter%20Analysis.pdf
http://umpir.ump.edu.my/id/eprint/38907/
https://www.ijisae.org/index.php/IJISAE/article/view/3183
_version_ 1822923778792882176
score 13.234278

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