Intellectual Property India Publishes Patent Application for 'AI-Powered Aquaculture Architect: Optimizing Fish Farm Design And Operation With Deep Learning And Multi-Agent Reinforcement Learning' Filed by Chaitanya Bharathi Institute Of Technology; Dr. R. Madana Mohana; Dr. Kadiyala Ramana; Mrs. Shobarani Salvadi; Mrs. Satya Kiranmai Tadepalli; Mrs. Sheena Mohammed; and Mrs. Kaneez Fatima

MUMBAI, India, Aug. 1 -- Intellectual Property India has published a patent application (202441005951 A) filed by Chaitanya Bharathi Institute Of Technology; Dr. R. Madana Mohana; Dr. Kadiyala Ramana; Mrs. Shobarani Salvadi; Mrs. Satya Kiranmai Tadepalli; Mrs. Sheena Mohammed; and Mrs. Kaneez Fatima, Hyderabad, Telangana, on Jan. 30, 2024, for 'ai-powered aquaculture architect: optimizing fish farm design and operation with deep learning and multi-agent reinforcement learning.'

Inventor(s) include Dr. R. Madana Mohana; Dr. Kadiyala Ramana; Mrs. Shobarani Salvadi; Mrs. Satya Kiranmai Tadepalli; Mrs. Sheena Mohammed; and Mrs. Kaneez Fatima.

The application for the patent was published on Aug. 1, under issue no. 31/2025.

According to the abstract released by the Intellectual Property India: "AI-Powered Aquaculture Architect: Optimizing Fish Farm Design and Operation with Deep Learning and Multi-agent Reinforcement Learning" is a Some of the main problems with traditional fish farming include water pollution, temperature imbalances, feed, space constraints, and expense. The aquaculture industry still has to overcome challenges including creating better monitoring systems, identifying outbreaks early, addressing high mortality, and advancing sustainability-all of which are unresolved issues that require attention. The objective of this research is to develop an aquaculture solution based on machine learning (ML) that increases prawn growth and productivity in ponds. The research presented a suggested architecture that uses sensors to gather data, a machine learning framework to analyze it, and a recommended list of water quality (QOW) factors that impact .Prawn growth and output, and the classification of ponds into those that produce low, medium, and high amounts of prawns. In this study, we employ three feature selection approaches to determine the factors that have the biggest influence on the overall harvest performance of the pond, in addition to eight different machine-learning classifiers to identify the driving factors that affect the development and yield of aquatic food products in ponds in terms of QOW variables."

Disclaimer: Curated by HT Syndication.