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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


Mutiu A. Adegboye

Abiodun M. Aibinu

Jonathan G. Kolo

Ibrahim Aliyu

Taliha A. Folorunso

Sun-Ho Lee


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.


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:

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
Keywords Accelerometer; Artificial neural network; Aquaculture; Chain code; Fish; Fish activities; Fish feeding system; Fourier descriptor; IoT devices
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