Incorporating Intelligence in Fish Feeding System for Dispensing Feed Based on Fish Feeding Intensity

Adegboye, Mutiu A.; Aibinu, Abiodun M.; Kolo, Jonathan G.; Aliyu, Ibrahim; Folorunso, Taliha A.; Lee, Sun-Ho

Abstract

The amount of feed dispense to match fish appetite plays a significant role in increasing fish cultivation. However, measuring the quantity of fish feed intake remains a critical challenge. To addressed this problem, this paper proposed an intelligent fish feeding regime system using fish behavioral vibration analysis and artificial neural networks. The model was developed using acceleration and angular velocity data obtained through a data logger that incorporated a triaxial accelerometer, magnetometer, and gyroscope for predicting fish behavioral activities. To improve the system accuracy, we developed a novel 8-directional Chain Code generator algorithm that extracts the vectors representing escape, swimming, and feeding activities. The set of sequence vectors extracted was further processed using Discrete Fourier Transform, and then the Fourier Descriptors of the individual activity representations were computed. These Fourier Descriptors are fed into an artificial neural network, the results of which are evaluated and compared with the Fourier Descriptors obtained directly from the acceleration and angular velocity data. The results show that the developed model using Fourier Descriptors obtained from Chain Code has an accuracy of 100%. In comparison, the developed classifier using Fourier Descriptors obtained directly from the fish movements acceleration, and angular velocity has an accuracy of 35.60%. These results showed that the proposed system could be used in dispensing feeds successfully without human intervention based on the fish requirements.

Citation

ADEGBOYE, M.A., AIBINU, A.M., KOLO, J.G., ALIYU, I., FOLORUNSO, T.A. and LEE, S.-H. 2020. Incorporating intelligence in fish feeding system for dispensing feed based on fish feeding intensity. IEEE access [online], 8, pages 91948-91960. Available from: https://doi.org/10.1109/ACCESS.2020.2994442

• Journal Article Type: Article
• Acceptance Date: May 3, 2020
• Online Publication Date: May 14, 2020
• Publication Date: Dec 31, 2020
• Deposit Date: Jan 14, 2022
• Publicly Available Date: Jan 14, 2022
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Pages: 91948-91960
• DOI: https://doi.org/10.1109/ACCESS.2020.2994442
• Keywords: Accelerometer; Artificial neural network; Aquaculture; Chain code; Fish; Fish activities; Fish feeding system; Fourier descriptor; IoT devices
• Public URL: https://rgu-repository.worktribe.com/output/984026