ESTIMATING THE SURVIVAL OF AQUA FAUNA BY IMAGE ANALYSIS TECHNIQUES FROM THE VIDEO SEQUENCES OF POND AQUACULTURE

Abstract

Survival status of aqua fauna is estimated by studying the dynamic behaviour (motion) in underwater environment. Survival status of aqua fauna particularly, shrimp is estimated to prevent huge economic losses to the aqua farmers. A novel approach has been proposed in this research work to address this issue through segmentation, extraction of features, motion analysis and classification of aqua fauna from the pond aquaculture video sequences. First, the content-based video is translated into frames, and the image content is segmented using the Background Subtraction algorithm. Then, shrimp motion analysis is done with the assistance of the optical Flow algorithm. At the same time, shrimp body feature is extracted by Edge detection method for Shape analysis. Finally, the Decision Tree classifier is constructed to estimate the status of shrimp is alive or dead. Real-time video sequences of shrimp in pond aquaculture have illustrated the effectiveness of our process. The projected method shows that the results are more accurate to estimate the shrimp survival status as well as providing GMP (Good Manufacturing Practices) in shrimp cultivation to alert the aqua farmers in advance to avoid economic loss