Page 215 - 《水产学报》2025年第12期

母刚，等 水产学报, 2025, 49(12): 129516

Feeding prediction model of Patinopecten yessoensis seedlings based on
BP neural network

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MU Gang 1,2,3,4 , LI Haidong , CHANG Yizhi , WU Yitao , ZHANG Qian 1,3,4 , LI Hangqi ,
ZHANG Hanbing 1,3,4 , LI Xiuchen 1,3,4 , ZHANG Guochen 1,3,4* , SONG Ruobing 1,3*
1. College of Mechanical and Power Engineering, Dalian Ocean University, Dalian 116023, China;
2. College of China and Nea Zealand Collaboration, Dalian Ocean University, Dalian 116023, China;
3. Innovation Center of Ocean Fisheries Equipment Professional Technology of Liaoning Province,
Dalian Ocean University, Dalian 116023, China;
4. Key Laboratory of Facility Fisheries, Ministry of Education, Dalian Ocean University, Dalian 116023, China

Abstract: To solve the problems of extensive manual feeding methods and poor accuracy in the scallop (Patinopecten yessoen-
sis) seedlings, a feeding prediction model based on a BP neural network for the P. yessoensis seedlings was proposed. The num-
ber of days of growth, feed consumption, and water temperature of P. yessoensis seedlings were taken as input vectors of the
prediction model, the feeding amount was taken as output vector, and the feeding amount was taken as output vector, and the
mapping relationship between the data was mined by BP neural network to establish the prediction model, the accuracy and sta-
bility of the model were verified by feeding test. The results showed that after being automatically fed by the system for 3 d, the
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root mean square error of 30 d P. yessoensis seedlings decreased from 456.6 × 10 for manual feeding to 226.6 × 10 , a reduc-
tion of 50.34%. The absolute percentage error of automatic feeding was 0.041, which was lower than that of manual feeding at
0.043; after being automatically fed by the system for 3 d, the root mean square error of the P. yessoensis seedlings cultivated
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for 42 d decreased from 194.2 × 10 for manual feeding to 149.3 × 10 , a decrease of 23.09%. The absolute percentage error of
automatic feeding was 0.020, which was less than 0.039 for manual feeding, which indicated that the accuracy and stability of
the prediction model of feeding of P. yessoensis seedlings were better than that of manual, which provided an important refer-
ence for the research and development of automatic feeding equipment for Patinopecten yessoensis seedlings.
Key words: Patinopecten yessoensis; seedling; automatic feeding; prediction model; BP neural network

Corresponding authors: ZHANG Guochen. E-mail: zhangguochen@dlou.edu.cn;
SONG Ruobing. E-mail: srb@dlou.edu.cn
Funding projects: National Key Research and Development Program of China (2023YFD2400800); Dalian's First Batch of
Unveiling and Leading Projects (2021JB11SN035); Liaoning Provincial Department of Education Basic Research Project
(LJ232410158048); Liaoning Provincial Undergraduate University Basic Research Business Expense Project (2024JBPTZ002);
Dalian Science and Technology Innovation Fund (2024JJ13GX039)

https://www.china-fishery.cn 中国水产学会主办 sponsored by China Society of Fisheries
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