Regression-based artificial intelligence length and weight estimation for sustainable prawn aquaculture

Abstract

The need for sustainable aquaculture practices has become very important to ensure sufficient production in addressing the increasing global demand for seafood. In this context, accurately assessing the size and weight of prawns is pivotal for efficient farming and resource utilization, allowing farmers to make informed decisions and productions. The integration of advanced AI algorithms into aquaculture practices holds great promise for fostering sustainability, thereby enhancing the overall productivity and resilience of prawn farming in the face of growing global challenges. This paper compares different length-weight regression techniques to estimate the weight of prawns and proposed a novel Regression-based Artificial Intelligence Biomass Estimation (RAIBE) systems for prawn aquaculture. RAIBE leverages deep learning and regression models to estimate the weight from images captured from a mobile device. The proposed methodology employs YOLOv8 with Segmentation for precise prawn identification. A unique biomarker is applied to estimate the length information. Subsequently, a polynomial based regression model is selected to correlate prawn length with actual weights, utilising comprehensive datasets collected under real-world farm conditions. As many different regression approaches have been proposed for the length-weight relationship, four commonly used approaches have been analysed. Results from extensive statistical analysis revealed that the modified polynomial regression with correction factor provides the best weight prediction. The integration of these techniques has equipped farmers with a reliable tool for predicting prawn weight during the sampling process, thereby minimizing stress on the prawns, and optimizing the segregation process.