TY - GEN
T1 - Behavioural Pattern Analysis of Fishes for Smart Aquaculture
T2 - 2021 IEEE Region 10 Conference, TENCON 2021
AU - Shreesha, S.
AU - Pai, Manohara
AU - Verma, Ujjwal
AU - Pai, Radhika M.
AU - Girisha, S.
N1 - Funding Information:
VI. ACKNOWLEDGEMENT We wish to acknowledge Department of Science and Technology (DST), Government of India, under which the work has been carried out.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
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U2 - 10.1109/TENCON54134.2021.9707293
DO - 10.1109/TENCON54134.2021.9707293
M3 - Conference contribution
AN - SCOPUS:85125965516
T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON
SP - 917
EP - 922
BT - TENCON 2021 - 2021 IEEE Region 10 Conference
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 7 December 2021 through 10 December 2021
ER -