Fish farmers are looking for sustainable methods of fishing to meet the ever-increasing demand for quality aquatic products. However, the water quality parameters, such as temperature, Dissolved Oxygen (DO) and pH plays a significant role in the success of aquaculture. The Dissolved oxygen concentration in the fish farms has a greater influence on the outcome of the aquaculture. DO can vary drastically depending upon many external factors, such as feeding, stocking density, diseases etc. Sudden depletion in DO can result in mass mortality of fishes if the preventive actions are not prompt. To this end, computer vision-based behaviour detection plays a significant role. The present study proposes to develop a novel computer vision-based approach to detect swimming at the surface pattern. An experiment is a setup to capture and develop the dataset of fish movement patterns. The proposed method uses detections alone to identify the swimming at the surface pattern. These detections are clustered and the mean of the clusters are compared against the threshold for classifying the pattern as Swimming at the surface pattern. The threshold is identified using the position histogram from the dataset. The proposed method is efficient, lightweight and reliable making it suitable for deployment in smart systems. The proposed method is also compared with pattern detection using a tracking algorithm. The results highlight the reliability of the proposed method to detect the patterns in aquaculture.