基于鱼类摄食行为的智能投饲技术研究进展

doi:10.19640/j.cnki.jtau.2022.02.013

Journal of Tianjin Agricultural University ›› 2022, Vol. 29 ›› Issue (2): 63-68.doi: 10.19640/j.cnki.jtau.2022.02.013

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Research progress on intelligent feeding technology based on fish feeding behavior

Sui Jinzhu¹, Tian Yunchen^{1,2,Corresponding Author}, Li Zhenzhong³, Zhu Zhenye³

1. College of Computer and Information Engineering, Tianjin Agricultural University, Tianjin 300392, China;
2. Tianjin Key Laboratory of Aquatic Ecology and Aquaculture, Tianjin 300392, China;
3. Shandong Dongrun Instrument Technology Co., Ltd., Yantai 264003, Shandong Province, China

Received:2022-02-20 Online:2022-06-30 Published:2022-07-05

Abstract

Abstract: Feed feeding is the main factor affecting the efficiency and cost of aquaculture. Judging and analyzing fish feeding behavior is the premise of intelligent feeding. In this paper the research on fish feeding behavior based on machine vision and the current situation of intelligent feeding technology using the changes of acoustic and water quality parameters is introduced. Then, the research progress on the analysis of fish feeding behavior and intelligent feeding with image processing, feed residue feedback and sound monitoring is introduced in detail. The advantages and existing problems of various research methods are also discussed. It is proposed that machine vision, acoustic and water quality parameters can be combined for comprehensive and multi-angle evaluation of feeding intensity to achieve intelligent feeding.

Key words: intelligent feeding, machine vision, feeding behavior, bait residue, ingestion sounds, water quality parameters

CLC Number:

S951.2

Sui Jinzhu, Tian Yunchen, Li Zhenzhong, Zhu Zhenye. Research progress on intelligent feeding technology based on fish feeding behavior[J]. Journal of Tianjin Agricultural University, 2022, 29(2): 63-68.

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References

[1] 农业农村部渔业渔政管理局. 2021中国渔业统计年鉴[M]. 北京:中国农业出版社,2021.
[2] 曹晓慧,刘晃. 养殖鱼类摄食行为的特征提取研究与应用进展[J]. 渔业现代化,2021,48(2):1-8.
[3] BONG J C,BONG S B,SAM K C,et al.A simple method to quantify fish behavior by forming time-lapse images[J]. Aquacultural Engineering,2012,51:15-20.
[4] PAPADAKIS V M,GLAROPOULOS A,KENTOURI M,et al.Sub-second analysis of fish behavior using a novel computer-vision system[J]. Aquacultural Engineering,2014,62:36-41.
[5] 段延娥,李道亮,李振波,等. 基于计算机视觉的水产动物视觉特征测量研究综述[J]. 农业工程学报,2015,31(15):1-11.
[6] YANG L,YU H,CHENG Y,et al.A dual attention network based on efficientNet-B2 for short-term fish school feeding behavior analysis in aquaculture[J]. Computers and Electronics in Agriculture,2021,187:1-10.
[7] ZHOU C,XU D,LIN K,et al.Intelligent feeding control methods in aquaculture with an emphasis on fish:a review[J]. Reviews in Aquaculture,2018,10(4):975-993.
[8] 左渠,田云臣,马国强. 水产养殖智能投饲系统研究进展和存在问题[J]. 天津农学院学报,2020,27(4):73-77.
[9] 袁超,朱瑞金. 基于KPCA的多特征融合的支持向量机鱼群摄食行为检测研究[J]. 水产养殖,2020,41(12):17-21.
[10] 张佳林,徐立鸿,刘世晶. 基于水下机器视觉的大西洋鲑摄食行为分类[J]. 农业工程学报,2020,36(13):158-164.
[11] 陈明,张重阳,冯国富,等. 基于特征加权融合的鱼类摄食活动强度评估方法[J]. 农业机械学报,2020,51(2):245-253.
[12] 陈志鹏,陈明. 基于光流法与图像纹理特征的鱼群摄食行为检测[J]. 南方农业学报,2019,50(5):1141-1148.
[13] 张重阳,陈明,冯国富,等. 基于多特征融合与机器学习的鱼类摄食行为的检测[J]. 湖南农业大学学报(自然科学版),2019,45(1):97-102.
[14] 郭强. 基于计算机视觉的循环水养殖镜鲤的摄食状态检测方法研究[D]. 上海:上海海洋大学,2018.
[15] 陈彩文,杜永贵,周超,等. 基于图像纹理特征的养殖鱼群摄食活动强度评估[J]. 农业工程学报,2017,33(5):232-237.
[16] SABERIOON M,GHOLIZADEH A,CISAR P,et al.Application of machine vision systems in aquaculture with emphasis on fish:state-of-the-art and key issues[J]. Reviews in Aquaculture,2017,9(4):1-19.
[17] LIU Z,LI X,FAN L,et al.Measuring feeding activity of fish in RAS using computer vision[J]. Aquacultural Engineering,2014,60:20-27.
[18] ZHOU C,ZHANG B,LIN K,et al.Near-infrared imaging to quantify the feeding behavior of fish in aquaculture[J]. Computers and Electronics in Agriculture,2017,135:233-241.
[19] ZHOU C,LIN K,XU D,et al.Near infrared computer vision and neuro-fuzzy model-based feeding decision system for fish in aquaculture[J]. Computers and Electronics in Agriculture,2018,146:114-124.
[20] 黄志涛,何佳,宋协法. 基于鱼体运动特征和图像纹理特征的鱼类摄食行为识别与量化[J]. 中国海洋大学学报(自然科学版),2022,52(1):32-41.
[21] 高叶玲,刘颖,褚海艳,等. 降低人工饲养和饲料对水产养殖水体影响的措施[J]. 中国饲料,2021(4):12-15.
[22] BABATUNDE D A,ABDULLATEEF A,SUSAN T A,et al.Waste production in aquaculture:Sources,components and managements in different culture systems[J]. Aquaculture and Fisheries,2019,4(3):81-88.
[23] 苗淑彦,王际英,张利民,等. 水产动物残饵及粪便对养殖水环境的影响[J]. 饲料研究,2009(2):64-67.
[24] UBINA N,CHENG S,CHANG C,et al.Evaluating fish feeding intensity in aquaculture with convolutional neural networks[J]. Aquacultural Engineering,2021,94:1-15.
[25] 何佳,黄志涛,宋协法,等. 基于计算机视觉技术的水产养殖中鱼类行为识别与量化研究进展[J]. 渔业现代化,2019,46(3):7-14.
[26] PREM R,TEWARI V K.Development of human- powered fish feeding machine for freshwater aquaculture farms of developing countries[J]. Aquacultural Engineering,2020,88:1-15.
[27] 刘杨. 基于深度学习的水下残饵检测方法研究与实现[D]. 扬州:扬州大学,2021.
[28] 穆春华,范良忠,刘鹰. 基于计算机视觉的循环水养殖系统残饵识别研究[J]. 渔业现代化,2015,42(2):33-37.
[29] 王吉祥. 基于嵌入式机器视觉的浮饵自动投放装置研制[D]. 镇江:江苏大学,2016.
[30] FOSTER M.Detection and counting of uneaten food pellets in a sea cage using image analysis[J]. Aquacultural Engineering,1995,14(3):251-269.
[31] 张惠娣,汪昌固,王贤成. 基于无线通信和PLC的网箱自动投饵系统设计[J]. 控制工程,2014,21(4):520-523.
[32] 张荣标,王吉祥,孙爱义,等. 循环水养殖浮饵自动投放方法与装置:201510513763.0[P].2015-12-02.
[33] IVAR R,MANUEL Y,BERND U,et al.Feeding behaviour and digestive physiology in larval fish:current knowledge,and gaps and bottlenecks in research[J]. Reviews in Aquaculture,2013,5(5):59-98.
[34] 唐荣,陈军,刘世晶,等. 基于声学方法的水产养殖投饲反馈技术研究进展[J]. 渔业现代化,2019,46(3):15-21.
[35] 曲蕊,刘晃,庄保陆,等. 水产养殖中摄食声学研究进展[J]. 渔业现代化,2020,47(4):1-6.
[36] LI D,MIAO Z,PENG F,et al.Automatic counting methods in aquaculture:A review[J]. Journal of the World Aquaculture Society,2020,52(2):1-15.
[37] JUELL J.过量饲料的水声检测一种估计海洋网箱养殖中鱼类群体最大饲料摄入量的方法[J]. 渔业机械仪器,1993,20(1):24-27.
[38] 刘丽珍,石晓天. 深水抗风浪网箱监测系统研制方案的探讨[J]. 海洋渔业,2007,29(1):90-94.
[39] 王顺杰,陈晶晶,荆成财,等. 网箱养殖智能投饲监控系统的设计[J]. 渔业现代化,2012,39(2):59-63.
[40] 马长震,谌志新,汤涛林,等. 基于超声探测技术的深水网箱剩余饵料监测系统[J]. 微计算机信息,2012,28(4):39-40,61.
[41] OLIVIER D,FRÉDÉRICH B,HERREL A,et al. A morphological novelty for feeding and sound production in the yellowtail clownfish[J]. Journal of Experimental Zoology Part A:Ecological Genetics and Physiology,2015,323(4):227-238.
[42] JOHN R,DAMON P G,ARTHUR N P.Bioacoustics of fishes of the family sciaenidae(Croakers and Drums)[J]. Transactions of the American Fisheries Society,2006,135(5):1409-1431.
[43] 曲蕊,刘晃,刘俊文,等. 大口黑鲈(Micropterus salmoides)摄食过程的声信号特征及养殖密度的影响[J]. 渔业现代化,2021,48(6):9.
[44] 任新敏,高大治,姚玉玲,等. 大黄鱼的发声及信号特性研究[J]. 大连水产学院学报,2007,22(2):123-128.
[45] 殷雷明,陈雪忠,张旭光,等. 玻璃钢水槽内大黄鱼养殖环境噪声测量与分析[J]. 海洋渔业,2017,39(3):314-321.
[46] LAGARDÈRE J P,MALLEKH R. Feeding sounds of turbot (Scophthalmus maximus)and their potential use in the control of food supply in aquaculture[J]. Aquaculture,2000,189(3):251-258.
[47] MALLEKH R,LAGARDÈRE J P,ENEAU J P,et al. An acoustic detector of turbot feeding activity[J]. Aquaculture,2003,221(1):481-489.
[48] 汤涛林,唐荣,刘世晶,等. 罗非鱼声控投饵方法[J]. 渔业科学进展,2014,35(3):40-43.
[49] SUN M,HASSAN S G,LI D.Models for estimating feed intake in aquaculture:A review[J]. Computers and Electronics in Agriculture,2016,127:425-438.
[50] WU T,HUANG Y,CHEN J.Development of an adaptive neural-based fuzzy inference system for feeding decision- making assessment in silver perch(Bidyanus bidyanus)culture[J]. Elsevier,2015,66(66):41-51.
[51] GENARO M S,ENRIQUE R,ROSALÍA O,et al. Fuzzy- logic-based feeder system for intensive tilapia production (Oreochromis niloticus)[J]. Aquaculture International,2010,18(3):379-391.
[52] GENARO M S,ROCÍO P,ENRIQUE R,et al. An automated recirculation aquaculture system based on fuzzy logic control for aquaculture production of tilapia (Oreochromis niloticus)[J]. Aquaculture International,2011,19(4):797-808.
[53] BÓRQUEZ-LOPEZ R A,CASILLAS-HERNANDEZ R, LOPEZ-ELIAS J A,et al. Improving feeding strategies for shrimp farming using fuzzy logic,based on water quality parameters[J]. Aquacultural Engineering,2018,81(81):38-45.
[54] 张重阳,陈明. 基于计算机视觉的鱼类摄食行为研究现状及展望[J]. 江苏农业科学,2020,48(24):31-36.

Research progress on intelligent feeding technology based on fish feeding behavior

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