Shrimp growth monitoring system using image processing techniques
Authors: Rex Paolo C. Gamara, Camille Tabalanza, Tim V. Cruz, Jennie Lou C. Tindugan, and Pocholo James M. Loresco
Abstract
All over the world, the aquaculture industry is very profitable, especially the shrimp farming industry. The various species of shrimps cultivated are considered as the most vital seafood product traded internationally. The better management practices (BMPs) implementation in combination with feed management and growth pattern monitoring achieves farming profitability. Currently, most Filipino farmers assess whether the shrimps reached the marketable size by estimating the length and weight without using measuring tools such as caliper and weighing scale. However, the use of these tools on a large population is a tedious and challenging task in terms of data recording and analysis. For the feed given, farmers usually approximate the amount without any tool; this could lead to underfeeding, resulting in lower growth rate, and overfeeding resulting in habitat pollution. This paper discussed an approach in developing a shrimp growth monitoring system capable of measuring the growth parameters and calculating the optimal feed amount based on the real-time images of live shrimp samples. The system utilizes a specialized conveyor belt and camera controlled by an Arduino microcontroller for image acquisition. The acquired images are sent to the computer for image processing-based measurements using Artificial Neural Network (ANN). The output is presented using the system.