Machine Learning Integrated Multivariate Water Quality Control Framework for Prawn Harvesting from Fresh Water Ponds
Water contamination, temperature imbalance, feed, space, and cost are key issues that traditional fish farming encounters. The aquaculture business still confronts obstacles such as the development of improved monitoring systems, the early detection of outbreaks, enormous mortality, and promoting su...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-Wiley
2023-01-01
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| Series: | Journal of Food Quality |
| Online Access: | http://dx.doi.org/10.1155/2023/3841882 |
| _version_ | 1811171088104685568 |
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| author | Gaganpreet Kaur M. Braveen Singamaneni Krishnapriya Surindar Gopalrao Wawale Jorge Castillo-Picon Dheeraj Malhotra Jonathan Osei-Owusu |
| author_facet | Gaganpreet Kaur M. Braveen Singamaneni Krishnapriya Surindar Gopalrao Wawale Jorge Castillo-Picon Dheeraj Malhotra Jonathan Osei-Owusu |
| author_sort | Gaganpreet Kaur |
| collection | DOAJ |
| description | Water contamination, temperature imbalance, feed, space, and cost are key issues that traditional fish farming encounters. The aquaculture business still confronts obstacles such as the development of improved monitoring systems, the early detection of outbreaks, enormous mortality, and promoting sustainability, all of which are open problems that need to be solved. The goal of this study is to provide a machine learning (ML)-based aquaculture solution that boosts prawn growth and production in ponds. The study described a proposed framework that collects data using sensors, analyses it using a machine learning framework, and provides results like a preferred list of water quality (QOW) variables that affect prawn development and yield, as well as pond categorization into low, medium, and high prawn-producing ponds. In this study, we use eight distinct machine-learning classifiers to discover the driving elements that influence the development and yield of aquatic food products in ponds in terms of QOW variables, as well as three feature selection approaches to identify the aspects that have the largest impact on the pond's total harvest performance. To validate and obtain satisfying results, the suggested system was installed and tested. The average F score and accuracy when yield is employed as a harvest parameter are determined to be 0.85 and 0.78, respectively. The average merit ratings of temperature, dissolved oxygen, and salinity are significantly higher than those of the other QOW components. The temperature variations are greatest during the second, fourth, and seventh weeks. Temperature, salinity, and dissolved oxygen are the three QOW variables that have the largest influence on overall pond harvest performance, according to the data. Additionally, it has been discovered that a key QOW factor in separating high-yielding ponds from low-yielding ponds is the temperature change following stocking. |
| first_indexed | 2024-04-10T17:07:33Z |
| format | Article |
| id | doaj.art-a03242df1c984127917373b198704d32 |
| institution | Directory Open Access Journal |
| issn | 1745-4557 |
| language | English |
| last_indexed | 2024-04-10T17:07:33Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi-Wiley |
| record_format | Article |
| series | Journal of Food Quality |
| spelling | doaj.art-a03242df1c984127917373b198704d322023-02-06T01:40:20ZengHindawi-WileyJournal of Food Quality1745-45572023-01-01202310.1155/2023/3841882Machine Learning Integrated Multivariate Water Quality Control Framework for Prawn Harvesting from Fresh Water PondsGaganpreet Kaur0M. Braveen1Singamaneni Krishnapriya2Surindar Gopalrao Wawale3Jorge Castillo-Picon4Dheeraj Malhotra5Jonathan Osei-Owusu6Chitkara University Institute of Engineering and TechnologySchool of Computer Science and EngineeringGuru Nanak Institutions Technical Campus (An Autonomous Institution)Agasti ArtsUniversidad Nacional Santiago Antúnez de MayoloDepartment of Information TechnologyDepartment of BiologicalWater contamination, temperature imbalance, feed, space, and cost are key issues that traditional fish farming encounters. The aquaculture business still confronts obstacles such as the development of improved monitoring systems, the early detection of outbreaks, enormous mortality, and promoting sustainability, all of which are open problems that need to be solved. The goal of this study is to provide a machine learning (ML)-based aquaculture solution that boosts prawn growth and production in ponds. The study described a proposed framework that collects data using sensors, analyses it using a machine learning framework, and provides results like a preferred list of water quality (QOW) variables that affect prawn development and yield, as well as pond categorization into low, medium, and high prawn-producing ponds. In this study, we use eight distinct machine-learning classifiers to discover the driving elements that influence the development and yield of aquatic food products in ponds in terms of QOW variables, as well as three feature selection approaches to identify the aspects that have the largest impact on the pond's total harvest performance. To validate and obtain satisfying results, the suggested system was installed and tested. The average F score and accuracy when yield is employed as a harvest parameter are determined to be 0.85 and 0.78, respectively. The average merit ratings of temperature, dissolved oxygen, and salinity are significantly higher than those of the other QOW components. The temperature variations are greatest during the second, fourth, and seventh weeks. Temperature, salinity, and dissolved oxygen are the three QOW variables that have the largest influence on overall pond harvest performance, according to the data. Additionally, it has been discovered that a key QOW factor in separating high-yielding ponds from low-yielding ponds is the temperature change following stocking.http://dx.doi.org/10.1155/2023/3841882 |
| spellingShingle | Gaganpreet Kaur M. Braveen Singamaneni Krishnapriya Surindar Gopalrao Wawale Jorge Castillo-Picon Dheeraj Malhotra Jonathan Osei-Owusu Machine Learning Integrated Multivariate Water Quality Control Framework for Prawn Harvesting from Fresh Water Ponds Journal of Food Quality |
| title | Machine Learning Integrated Multivariate Water Quality Control Framework for Prawn Harvesting from Fresh Water Ponds |
| title_full | Machine Learning Integrated Multivariate Water Quality Control Framework for Prawn Harvesting from Fresh Water Ponds |
| title_fullStr | Machine Learning Integrated Multivariate Water Quality Control Framework for Prawn Harvesting from Fresh Water Ponds |
| title_full_unstemmed | Machine Learning Integrated Multivariate Water Quality Control Framework for Prawn Harvesting from Fresh Water Ponds |
| title_short | Machine Learning Integrated Multivariate Water Quality Control Framework for Prawn Harvesting from Fresh Water Ponds |
| title_sort | machine learning integrated multivariate water quality control framework for prawn harvesting from fresh water ponds |
| url | http://dx.doi.org/10.1155/2023/3841882 |
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