Abstract
Fish production has become a roadblock to the development of fish farming, and one of the issues encountered throughout the hatching process is the counting procedure. Previous research has mainly depended on the use of non-machine learning-based and machine learning-based counting methods and so was unable to provide precise results. In this work, we used a robotic eye camera to capture shrimp photos on a shrimp farm to train the model. The image data were classified into three categories based on the density of shrimps: low density, medium density, and high density. We used the parameter calibration strategy to discover the appropriate parameters and provided an improved Mask Regional Convolutional Neural Network (Mask R-CNN) model. As a result, the enhanced Mask R-CNN model can reach an accuracy rate of up to 97.48%.
Highlights
Underwater Fish Detection and Shrimp counting is essential for farmers to estimate and manage hatching
To save time in the training models’ process and shorten the time in the labeling dataset, Mask R-convolutional neural network (CNN) is used in this paper to find out the best parameter and detect the total number of shrimps in an image
We studied the performance of the proposed improved Mask R-CNN model and compared it with the existing Mask R-CNN model using the shrimp datasets
Summary
Underwater Fish Detection and Shrimp counting is essential for farmers to estimate and manage hatching. An accurate automated shrimp detection and counting algorithm can enable farmers to optimize and streamline their hatching period. Shrimp farmers who use ponds for production rely on cast netting shrimp and relating the amount caught in the surface area of the cast net to the surface area of the entire pond (Figure 1a,b). While this has been useful for estimating growth, it is not a reliable predictor of survival. The farmer must drain the tank, collect the shrimp in a mesh bag and weigh them out of the water to accurately count the shrimps in a tank culture system
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