Fish Weight prediction

Keywords: Fish, Weight

Background

In the current environment, accurate fish weight estimation is essential for a variety of applications The goal of this study is to create an ANN-based predictive model that accurately predicts fish weight based on key characteristics like species, length measures, height, and width. To achieve this, the study trains and assesses the ANN model. In areas like commercial fishing, fish farming, and conservation initiatives, precise fish weight prediction is very valuable. In the past, weight estimates required laborious and prone to error manual measurements. The development of machine learning methods, notably Artificial Neural Networks, has given rise to the possibility of developing precise weight prediction models. The data used in this work offers a wide variety of fish samples from different species, along with weight measurements for each sample belonging to that species. Important morphometric characteristics such as length measures height, and width are present in the dataset and are anticipated to have a significant effect on fish weight.

Aim & Objectives

Aim
To Predict the Fish Weight Using DL Techniques
Objective
Develop an accurate predictive model using Artificial Neural Networks (ANN) to estimate fish weight based on species and morphometric characteristics like length, height, and width. The study aims to enhance various applications such as commercial fishing, fish farming, and conservation efforts by providing a reliable and automated method for predicting fish weight, replacing labor-intensive and error-prone manual measurements with advanced machine learning techniques.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

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