• LAKE ARUNA LAVANYA KUMARI
• S Pallam setty

The precise estimation of group fish feed consumption is thought to be essential to any feeding system in aquaculture. This research proposed a feed intake prediction model for group fish using machine learning and deep learning to address the aforementioned problems. First, fouz variables were chosen as the input of the prediction model: the number of servings of fish food consumed daily, the total daily consumption of fish food (in grams), the total weekly consumption of fish food (in grams) and the total annual consumption of fish food (in grams) After training, the prediction model was finally realized. Because fish food is not always consumed by the farmed species and sometimes sinks to the seafloor, it is a significant source of localized organic enrichment in aquaculture.

"Identification of prediction of fish feeding using machine learning and deep learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.f768-f774, December-2023, Available :http://www.jetir.org/papers/JETIR2312594.pdf

"Identification of prediction of fish feeding using machine learning and deep learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. ppf768-f774, December-2023, Available at : http://www.jetir.org/papers/JETIR2312594.pdf