Tilapia feeding decision system based on adaptive neuro-fuzzy inference


  • Haiyang Cao School of Agricultural Equipment Engineering, JiangsuUniversity,Zhenjiang, China
  • Hanping Mao School of Agricultural Equipment Engineering, JiangsuUniversity,Zhenjiang, China




Control method, Precise feeding, Feeding behavior, Machine vision, Tilapia


In industrial recirculating aquaculture, the feed required by fish accounts for a major part of the total expenditure. In this paper, a multi-factor decision making system based on aggregation FIFFB of fish feeding behavior, water temperature T of environmental factors and biomass weight W was proposed to solve the problem of feed waste under the traditional mode. To verify the performance of this model, a fuzzy inference FIS model is constructed for comparison. The experimental results show that the root mean square error (RMSE) and mean absolute error (MAE) between the predicted and actual feeding amount of ANFIS triplet are 0.78 and 0.19, respectively, which are much lower than the FIS model, and this model is more suitable for predicting the feeding amount decision. At the same time, growth parameters such as WGR, FMAE, K and FCR were compared. The fish growth specifications were fatter and the economic benefits were higher, and the feed conversion rate was increased by 12.35%. Therefore, the triplet ANFIS feeding prediction and decision-making system based on fish aggregation degree, water temperature and body weight is effective and has guiding significance for the precise feeding design of unmanned aquaculture.


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An, D., Huang, J., & Wei, Y. (2021). A survey of fish behaviour quantification indexes and methods in aquaculture. Reviews in Aquaculture, 13(4), 2169-2189.https://doi.org/10.1111/raq.12564

Ardianto, M. A. D. (2022). Development of conceptual model integrated estimation system for fish growth and feed requirement in aquaculture supply chain management. Procedia Computer Science, 197, 461-468.https://doi.org/10.1016/j.procs.2021.12.162

Barbedo, J. G. A. (2022). A Review on the Use of Computer Vision and Artificial Intelligence for Fish Recognition, Monitoring, and Management. Fishes, 7(6), 335.https://doi.org/10.3390/fishes7060335

Chen, L., Yang, X., Sun, C., Wang, Y., Xu, D., & Zhou, C. (2020). Feed intake prediction model for group fish using the MEA-BP neural network in intensive aquaculture. Information Processing in Agriculture, 7(2), 261-271.https://doi.org/10.1016/j.inpa.2019.09.001

Chen, L.(2020). Research on fish feeding prediction method based on adaptive fuzzy neural network. China Agricultural Science and Technology Heral, 22, 91-100.https://doi.org/10.13304/ j.nykjdb.2018.0599

Feng, S., Yang, X., Liu, Y., Zhao, Z., Liu, J., Yan, Y., & Zhou, C. (2022). Fish feeding intensity quantification using machine vision and a lightweight 3D ResNet-GloRe network. Aquacultural Engineering, 98, 102244.https://doi.org/10.1016/j.aquaeng.2022.102244

Liu, X., Du, K., Zhang, C., Luo, Y., Sha, Z., & Wang, C. (2023). Precision feeding system for largemouth bass (Micropterus salmoides) based on multi-factor comprehensive control. Biosystems Engineering, 227, 195-216.https://doi.org/10.1016/j.biosystemseng.2023.02.005

Ubina, N., Cheng, S. C., Chang, C. C., & Chen, H. Y. (2021). Evaluating fish feeding intensity in aquaculture with convolutional neural networks. Aquacultural Engineering, 94, 102178.https://doi.org/10.1016/j.aquaeng.2021.102178

Wu, T. H., Huang, Y. I., & Chen, J. M. (2015). Development of an adaptive neural-based fuzzy inference system for feeding decision-making assessment in silver perch (Bidyanus bidyanus) culture. Aquacultural Engineering, 66, 41-51.https://doi.org/10.1016/j.aquaeng.2015.02.001

Wang, Y., Yu, X., Liu, J., An, D., & Wei, Y. (2022). Dynamic feeding method for aquaculture fish using multi-task neural network. Aquaculture, 551, 737913.https://doi.org/10.1016/j.aquaculture.2022.737913

Zhou, C., Lin, K., Xu, D., Chen, L., Guo, Q., Sun, C., & Yang, X. (2018). Near infrared computer vision and neuro-fuzzy model-based feeding decision system for fish in aquaculture. Computers and electronics in agriculture, 146, 114-124.https://doi.org/10.1016/j.compag.2018.02.006




How to Cite

Cao, H. ., & Mao , H. . (2023). Tilapia feeding decision system based on adaptive neuro-fuzzy inference. JOURNAL OF ADVANCES IN AGRICULTURE, 14, 1–10. https://doi.org/10.24297/jaa.v14i.9452




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