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Enhancing Aquaculture Efficiency: Automated Feed Management Through Deep Learning
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Author(s): Kiran Sree Pokkuluri (Shri Vishnu Engineering College for Women, Vishnupur, India), Alex Khang (Global Research Institute of Technology and Engineering, USA)and S. S. S. N. Usha Devi N. (Jawaharlal Nehru Technological University College of Engineering, India)
Copyright: 2024
Pages: 11
Source title:
Agriculture and Aquaculture Applications of Biosensors and Bioelectronics
Source Author(s)/Editor(s): Alex Khang (Global Research Institute of Technology and Engineering, USA)
DOI: 10.4018/979-8-3693-2069-3.ch022
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Abstract
A major part of supplying the increasing demand for seafood around the world is aquaculture. This work suggests a novel deep learning-based method for automated feed management to improve efficiency. Conventional aquaculture feeding methods frequently depend on manual supervision and recurring feeding schedules, which can have an adverse effect on the environment and result in an inadequate use of resources. By using less resources and addressing environmental issues, this technology not only increases aquaculture productivity, but also promotes sustainable practices. In conclusion, there is a lot of potential for aquaculture operations to be revolutionised by the suggested automated feed management system that uses CNNs (convolution neural networks) and deep learning. It solves long-standing feed management inefficiencies by fusing real-time data analysis with adaptive decision-making, opening the door for a more fruitful and sustainable future for the aquaculture sector.
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