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

天津农学院学报 ›› 2022, Vol. 29 ›› Issue (2): 63-68.doi: 10.19640/j.cnki.jtau.2022.02.013

基于鱼类摄食行为的智能投饲技术研究进展

隋金柱¹, 田云臣^{1,2,通信作者}, 李振忠³, 祝臻业³

1.天津农学院计算机与信息工程学院,天津 300392;
2.天津市水产生态与养殖重点实验室,天津 300392;
3.山东东润仪表科技股份有限公司,山东烟台 264003

收稿日期:2022-02-20 出版日期:2022-06-30 发布日期:2022-07-05
通讯作者: 田云臣（1967—）,男,教授,硕士,主要从事农业信息技术研究工作。E-mail：tianyunchen@tjau.edu.cn。
作者简介:隋金柱（1996—）,男,硕士在读,主要从事基于机器视觉的鱼群摄食行为研究工作。E-mail：s1013174768@163.com。
基金资助:
国家重点研发计划（2020YFD0900600）; 财政部和农业农村部国家现代农业产业技术体系（CARS-47）; 天津市海水养殖产业技术体系（ITTMRS2021000）; 天津农学院研究生科研创新项目（2021XY030）

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

摘要/Abstract

摘要： 饲料投喂是影响水产养殖效率和成本的主要因素,判断、分析鱼类摄食行为是进行智能投饲的前提。介绍基于机器视觉的鱼类摄食行为研究以及利用声学、水质参数变化开展智能投饲技术研究的现状,详细介绍了利用图像处理、饵料残留反馈、鱼群摄食声音监测对鱼群摄食行为进行分析及智能投饲的研究进展,对各种研究方法的优点和存在问题进行了讨论,并提出今后可以将机器视觉、声学和水质参数相结合进行全方位多角度的摄食强度评估,以实现智能投饲。

关键词: 智能投饲, 机器视觉, 摄食行为, 饵料残留, 摄食声音, 水质参数

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

中图分类号:

S951.2

隋金柱, 田云臣, 李振忠, 祝臻业. 基于鱼类摄食行为的智能投饲技术研究进展[J]. 天津农学院学报, 2022, 29(2): 63-68.

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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链接本文: http://xuebao.tjau.edu.cn/CN/10.19640/j.cnki.jtau.2022.02.013

http://xuebao.tjau.edu.cn/CN/Y2022/V29/I2/63

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基于鱼类摄食行为的智能投饲技术研究进展

Research progress on intelligent feeding technology based on fish feeding behavior

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