Design of the intelligent feeding machine for largemouth bass on the basis of feeding intensity

Authors

  • Huang Huang 1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; 2. Key Laboratory of Aquaculture Facility Engineering, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China; 3. Key Laboratory of Agricultural Equipment in Mid-Lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China
  • Zeyu Zheng 1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
  • Yan Shi 1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
  • Xiao Li 1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
  • Peng Wan 1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; 2. Key Laboratory of Aquaculture Facility Engineering, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China
  • Zhuo Chen 1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
  • Yunfei Guo 1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
  • He Huang 4. Wuhan Mingming Agricultural Technology Co., Ltd., Wuhan 430072, China

Abstract

Conventional feeders can achieve timed and quantitative feeding, but they cannot optimize feeding strategies on the basis of actual aquaculture conditions. This study evaluated the feeding intensity of largemouth bass and developed an intelligent feeder to achieve efficient and precise feeding. A mobile feeding system was built by designing and simulating the structure of the data acquisition, control, feeding power, storage, and mobile modules of the feeder. The surface water pressure signals during largemouth bass feeding were collected through pressure sensors and analyzed, and the feeding intensity was classified into three levels: strong, weak, and none. Signal features were extracted to construct a dataset and input into five machine learning models for optimal parameter tuning. The precision, recall, F1 score, and average accuracy of the random forest model were 96.2%, 95.5%, 95.6%, and 93.4%, respectively. The YOLOv5 model was adopted to detect remaining feed on the water surface. The feeding system was designed to enable the feeder to automatically track and provide feed into the tank. Experiments were conducted on the intelligent feeding system, with the feed residue rate as the indicator of the practicality of the feeding system. Verification experiments were also performed on eight tanks, and the average feed residue rate was less than 3%, proving that the feeding system has good practicality in actual aquaculture environments.

Keywords: largemouth bass, feeding intensity, intelligent feeding machine, machine learning, mobile feeding

DOI: 10.25165/j.ijabe.20261902.9955

 

Citation: Huang H, Zheng Z Y, Shi Y, Li X, Wan P, Chen Z, et al. Design of an intelligent feeding machine for largemouth bass on the basis of feeding intensity. Int J Agric & Biol Eng, 2026; 19(2): 13–27.

References

[1] Xie L. Analysis on the current situation of industrial recirculating aquaculture. Contemporary Fisheries, 2019; 44（8）: 90–91. （in Chinese）

[2] Atoum Y, Srivastava S, Liu X. Automatic feeding control for dense aquaculture fish tanks. IEEE Signal Processing Letters, 2014; 22（8）: 1089–1093.

[3] Zhang S M, Li J K, Tang F H, Wu Z, Dai Y, Fan W. Research progress on fish farming monitoring based on deep learning technology. Transactions of the CSAE, 2024; 40（5）: 1–13. （in Chinese）

[4] Barraza-Guardado R H, Martínez-Córdova L R, Enríquez-Ocaña L F, Martínez-Porchas M. Effect of shrimp farm effluent on water and sediment quality parameters off the coast of Sonora, Mexico. Ciencias Marinas, 2014; 40（4）: 221–235.

[5] Zhou C, Xu D M, Chen L, Zhang S, Sun C H, Yang X T, et al. Evaluation of fish feeding intensity in aquaculture using a convolutional neural network and machine vision. Aquaculture, 2019; 507: 457–465.

[6] Zhang L, Li B, Sun X B, Hong Q Q, Duan Q L. Intelligent fish feeding based on machine vision: A review. Biosystems Engineering, 2023; 231: 133–164.

[7] Sadoul B, Mengues P E, Friggens N C, Prunet P, Colson V. A new method for measuring group behaviours of fish shoals from recorded videos taken in near aquaculture conditions. Aquaculture, 2014; 430: 179–187.

[8] Sneddon L U. Fish behaviour and welfare. Applied Animal Behaviour Science, 2007; 104（3/4）: 173–175.

[9] Israeli D, Kimmel E. Monitoring the behavior of hypoxia-stressed Carassius auratus using computer vision. Aquacultural Engineering, 1996; 15（6）: 433–440.

[10] Liu H Y, Xu L H, Li D W. Detection and recognition of uneaten fish food pellets in aquaculture using image processing. In: Sixth International Conference on Graphic and Image Processing （ICGIP）. 2015, Beijing. DOI: 10.1117/12.2179138.

[11] Zhang L, Zhou X H, Li B B, Zhang H X, Duan Q L. Automatic shrimp counting method using local images and lightweight YOLOv4. Biosystems Engineering, 2022; 220: 39–54.

[12] Li D W, Xu L H, Liu H Y. Detection of uneaten fish food pellets in under water images for aquaculture. Aquacultural Engineering, 2017; 78: 85–94.

[13] Qiao F, Zheng D, Hu Y L, Wei Y Y. Research on intelligent feeding system based on real-time decision making of machine vision. Chinese Journal of Engineering Design, 2015; 22（6）: 528–533. （in Chinese） DOI: 10.3785/j.issn. 1006-754X.2015.06.003.

[14] Zhu M, Zhang Z F, Huang H, Chen Y Y, Liu Y D, Dong T. Classification of perch ingesting condition using lightweight neural network MobileNetV3-Small. Transactions of the CSAE, 2021; 37（19）: 165–172.

[15] Zhao S Q, Ding W M, Zhao S Q, Gu J B. Adaptive neural fuzzy inference system for feeding decision-making of grass carp （Ctenopharyngodon idellus） in outdoor intensive culturing ponds. Aquaculture, 2019; 498: 28–36.

[16] Pan S Q, Mao H P, Wang B, Cao H Y, Zhu S Y, Ye Y T. Research on feeding rules and feeding methods of fish schools based on six-axis sensors. Fishery Modernization, 2023; 50（3）: 56–63. （in Chinese） DOI: 10.3969/j.issn.1007-9580.2023.03.007.

[17] Du Z Z, Cui M, Xu X B, Bai Z Z, Han J, Li W C, et al. Harnessing multimodal data fusion to advance accurate identification of fish feeding intensity. Biosystems Engineering, 2024; 246: 135–149.

[18] Shen C L, Zhang L L, Liu H, Wang B Q. Numerical simulation analysis of a strengthening method for shear capacity of a voided slab bridge using finite element software ABAQUS. International Journal of New Developments in Engineering and Society, 2023; 7（2）: 070205.

[19] Pavitra S, Pinakeswar M, Pankaj K. Numerical investigation of fluid flow and heat transfer in a gas-solid vortex reactor without slit: Scale-up and optimization. International Communications in Heat and Mass Transfer, 2021; 128: 105590.

[20] Windsor S P, Norris S E, Cameron S M, Mallinson G D, Montgomery J C. The flow fields involved in hydrodynamic imaging by blind Mexican cave fish （Astyanax fasciatus）. Part II: Gliding parallel to a wall. Journal of Experimental Biology, 2010; 213（22）: 3819–3831.

[21] Overli O, Sorensen C, Nilsson G E. Behavioral indicators of stress-coping style in rainbow trout: do males and females react differently to novelty. Physiology& Behavior, 2006; 87（3）: 506–512.

[22] Ling J, Heldman D R. Integration of machine learning technologies in food flavor research: Current applications, challenges, and future perspectives. Int J Agric & Biol Eng, 2025; 18（3）: 1–11.

[23] Park S, Kim K, Hibino T, Kim K. Machine learning-based prediction of seasonal hypoxia in eutrophic estuary using capacitive potentiometric sensor. Marine Environmental Research, 2024; 196: 106445.

[24] Boutilier J, Michini C, Zhou Z. Optimal multivariate decision trees. Constraints, 2023; 28（4）: 549–577.

[25] Reddy D, Murugan R, Nandi A, Goel T. Classification of arrhythmia disease through electrocardiogram signals using sampling vector random forest classifier. Multimedia Tools and Applications, 2022; 82（17）: 26797–26827.

[26] Chen Z, Zhang T, Zhang R, Zhu Z M, Yang J, Chen P Y. Extreme gradient boosting model to estimate PM 2.5 concentrations with missing-filled satellite data in China. Atmospheric Environment, 2019; 202: 180–189.

[27] Gary F, Oliver J. On fast computation of finite-time coherent sets using radial basis functions. Chaos （Woodbury, NY）, 2015; 25（8）: 087409.

[28] Febri L, Agus L H. Adaptive ant colony optimization on mango classification using k-nearest neighbor and support vector machine. Journal of Information Systems Engineering and Business Intelligence, 2017; 3（2）: 75–79.

[29] Gopalakrishna A K, Ozcelebi T, Liotta A, Lukkien J J. Relevance as a metric for evaluating machine learning algorithms. Machine Learning and Data Mining in Pattern Recognition: 9th International Conference, MLDM 2013, New York, NY, USA, July 19-25, 2013. Proceedings 9. Springer Berlin Heidelberg, 2013: 195–208. DOI: 10.1007/978-3-642-39712-7_15.

[30] Ho C K, Young S K. Real-time object detection and segmentation technology: an analysis of the YOLO algorithm. JMST Advances, 2023; 5（2-3）: 69–76.

[31] Jaisut D, Prachayawarakorn S, Varanyanond W, Tungtrakul P, Soponronnarit S. Accelerated aging of jasmine brown rice by high-temperature fluidization technique. Food Research International, 2009; 42（5-6）: 674–681.

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Published

2026-05-21

How to Cite

(1)
Huang, H.; Zheng, Z.; Shi, Y.; Li, X.; Wan, P.; Chen, Z.; Guo, Y.; Huang, H. Design of the Intelligent Feeding Machine for Largemouth Bass on the Basis of Feeding Intensity. Int J Agric & Biol Eng 2026, 19, 13-27.

Issue

Vol. 19 No. 2 (2026): IJABE

Section

Applied Science, Engineering and Technology

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