Research on a precise feeding method for fry in recirculating aquaculture systems

IF 3.7 2区 农林科学 Q1 FISHERIES
Haihui Yang , Xiaochan Wang , Yinyan Shi , Jihao Wang , Bo Jia , Chengquan Zhou , Hongbao Ye
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Abstract

Fry feeding in recirculating aquaculture systems (RAS) has gained prominence following China’s ban on fishing in the Yangtze River. Although previous researches have focused on dynamic adjustments to adult fish feeding status, research on fry feeding has been subsequently neglected. To fill this research gap, a precise fry feeding method was developed, comprising four main components: a fry feeding status detection module, a feeding control module, a precise feed discharging module, and a variable feed distribution module. The detection module utilizes the improved FFD-YOLO network which incorporates GhostNet, BiFPN and CA attention to detect fry feeding status, and real-time feeding decisions were made accordingly. Numerical simulations using Python were conducted to calculate the optimal feed coverage ratio, and Fuzzy-PID control was employed to rapidly adjust the rotational speed of the spreading disc. The experiments demonstrate that the FFD-YOLO algorithm achieved a precision of 91.33 %, a recall rate of 74.15 %, and a mAP_0.5 of 85.06 %, with a detection speed of 75 frames per second (FPS). Feeding distribution coverage ratios of 40 % and 80 % were recommended based on simulation results. The experimental results demonstrated that when feeding based on clear images, the errors of discharge and distribution were less than 10.2 % and 12.6 %, respectively. In contrast, when feeding based on blurred images, the errors exceeded 18.4 % and 24.1 %, respectively. Control experiments demonstrated that the proposed method can promote the growth of fry. This study provides a significant reference for future research on automatic fry feeding in industrial RAS.
循环水养殖系统鱼苗精准投料方法研究
在中国禁止在长江捕鱼后,循环水养殖系统(RAS)的鱼苗饲养得到了广泛关注。虽然以往的研究主要集中在成鱼摄食状态的动态调整上,但对鱼苗摄食的研究却被忽视了。为了填补这一研究空白,开发了一种鱼苗精确进料方法,该方法主要由四个部分组成:鱼苗进料状态检测模块、进料控制模块、精确出料模块和可变进料分配模块。检测模块采用改进的FFD-YOLO网络,结合GhostNet、BiFPN和CA关注,检测鱼苗的摄食状态,并据此做出实时摄食决策。利用Python软件进行数值模拟,计算出最优进料覆盖率,并采用Fuzzy-PID控制快速调节扩散盘转速。实验表明,FFD-YOLO算法的检测精度为91.33 %,召回率为74.15 %,mAP_0.5为85.06 %,检测速度为75帧/秒(FPS)。根据仿真结果,推荐投料分配覆盖率为40 %和80 %。实验结果表明,基于清晰图像进料时,出料和分布误差分别小于10. %和12.6 %。相比之下,基于模糊图像的馈送误差分别超过18.4 %和24.1 %。对照实验表明,该方法能促进鱼苗的生长。本研究为今后工业RAS鱼苗自动投料的研究提供了重要参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Aquaculture Reports
Aquaculture Reports Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
5.90
自引率
8.10%
发文量
469
审稿时长
77 days
期刊介绍: Aquaculture Reports will publish original research papers and reviews documenting outstanding science with a regional context and focus, answering the need for high quality information on novel species, systems and regions in emerging areas of aquaculture research and development, such as integrated multi-trophic aquaculture, urban aquaculture, ornamental, unfed aquaculture, offshore aquaculture and others. Papers having industry research as priority and encompassing product development research or current industry practice are encouraged.
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