Oreochromis niloticus Growth Performance Analysis Using Pixel Transformation and Pattern Recognition

Abstract

To achieve healthy development and optimal growth for harvest in an aquaculture system, correct determination of fish growth stages is very important. The sizes or growth stages of the fish are used by farm managers to regulate stocking densities, optimize daily feeding, and ultimately choose the ideal time for harvesting. This paper presented a vision system-based fish classification using pixel transformation and neural network pattern recognition. Morphometrics parameters are used to facilitate a supervised gathering of datasets. Before feature extraction, the images go through intensity transformation using histogram analysis and Otsu’s thresholding. Using Pearson’s correlation coefficient, the six most important characteristics of the original ten attributes were identified. The developed intelligent model using neural network pattern recognition has an overall training accuracy equal to 90.3%. The validation, test, and overall accuracy are equal to 85.7%, 85.7%, and 88.9%, respectively.