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Accurate and rapid detection of fish behaviors is critical to perceive health and welfare by allowing farmers to make informed management decisions about recirculating the aquaculture system while decreasing labor. The classic detection approach involves placing sensors on the skin or body of the fish, which may interfere with typical behavior and welfare. The progress of deep learning and computer vision technologies opens up new opportunities to understand the biological basis of this behavior and precisely quantify behaviors that contribute to achieving accurate management in precision farming and higher production efficacy. This study develops an intelligent fish behavior classification using modified invasive weed optimization with an ensemble fusion (IFBC-MIWOEF) model. The presented IFBC-MIWOEF model focuses on identifying the distinct kinds of fish behavior classification. To accomplish this, the IFBC-MIWOEF model designs an ensemble of Deep Learning (DL) based fusion models such as VGG-19, DenseNet, and EfficientNet models for fish behavior classification. In addition, the hyperparameter tuning of the DL models is carried out using the MIWO algorithm, which is derived from the concepts of oppositional-based learning (OBL) and the IWO algorithm. Finally, the softmax (SM) layer at the end of the DL model categorizes the input into distinct fish behavior classes. The experimental validation of the IFBC-MIWOEF model is tested using fish videos, and the results are examined under distinct aspects. An Extensive comparative study pointed out the improved outcomes of the IFBC-MIWOEF model over recent approaches.

Intelligent Fish Behavior Classification Using Modified Invasive Weed Optimization with Ensemble Fusion Model