Artificial Neural Network-Based Decision Support for Shrimp Feed Type Classification

Abstract

Shrimp farming is a highly profitable business in the aquaculture industry. The farming profitability can be achieved by the implementation of better management practices in conjunction with optimal shrimp feed management and growth monitoring. Manual measurement for shrimp growth on a large population is a tedious and difficult task. Underfeeding results to lower growth rate, and overfeeding results to environmental pollution. Automated, continuous, and non-invasive methods therefore such as computer vision are being increasingly employed. However, existing researches of vision-based measurement of growth parameters are not yet incorporated to shrimp feed management. This paper presented an Artificial Neural Network-based decision support system of classifying feed type whether starter, grower or finisher using area, length and weight derived from image processing techniques. The neural network was trained using scaled conjugate gradient back propagation. The decision support system exhibited promising results in feed type classification.