Machine Learning Integrated Multivariate Water Quality Control Framework for Prawn Harvesting from Fresh Water Ponds

Malhotra, Dheeraj

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DC Field	Value	Language
dc.contributor.author	Malhotra, Dheeraj	-
dc.date.accessioned	2024-09-04T09:32:33Z	-
dc.date.available	2024-09-04T09:32:33Z	-
dc.date.issued	2023	-
dc.identifier.uri	http://localhost:8080/xmlui/handle/123456789/298	-
dc.description	Water contamination, temperature imbalance, feed, space, and cost are key issues that traditional fsh farming encounters. Te 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. Te goal of this study is to provide a machine learning (ML)-based aquaculture solution that boosts prawn growth and production in ponds. Te 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 afect 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 classifers to discover the driving elements that infuence 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. Te average F score and accuracy when yield is employed as a harvest parameter are determined to be 0.85 and 0.78, respectively. Te average merit ratings of temperature, dissolved oxygen, and salinity are signifcantly higher than those of the other QOW components. Te 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 infuence 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	en_US
dc.language.iso	en	en_US
dc.publisher	Hindawi Journal of Food Quality	en_US
dc.subject	Machine Learning	en_US
dc.subject	Water Quality	en_US
dc.subject	Harvesting	en_US
dc.title	Machine Learning Integrated Multivariate Water Quality Control Framework for Prawn Harvesting from Fresh Water Ponds	en_US
dc.type	Article	en_US
Appears in Collections:	VSIT

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