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摘要: 水产养殖装备是高效发展现代水产养殖，促进水产养殖产业结构改革的重要技术支撑。基于养殖装备、信息技术和自动控制等多学科协同发力的智慧水产养殖模式已成为现代渔业高质量发展的新趋势与重要抓手，这也对水产养殖现有装备及其相关技术提出了更高的智能化要求。本文梳理了池塘、工厂化、网箱、筏式和底播养殖等5种主要养殖方式装备发展现状，从数字化和智能化角度分析了环境监测、对象感知、饲料投喂、分级计数等养殖环节中常用装备的研究进展，指出了制约我国水产养殖智能装备与技术发展的关键问题，提出了“机械化、自动化、智能化”的水产养殖装备与技术发展的新思路，旨在实现我国从水产养殖大国向水产养殖强国的历史转变。Abstract: Aquaculture equipment is an important technical support for the efficient development of modern aquaculture and the promotion of structural reform in the aquaculture industry. The intelligent aquaculture model based on the collaborative efforts of multiple disciplines such as aquaculture equipment, information technology, and automatic control, has become a new trend and an important lever for the high-quality development of modern fisheries, which also puts forward higher intelligent requirements for the existing equipment and related technologies of aquaculture. This article summarizes the current development status of five main aquaculture methods and equipment, including pond, factory, cage, raft, and bottom sowing aquaculture. From the perspectives of digitization and intelligence, it analyzes the research progress of commonly used equipment in aquaculture processes such as environmental monitoring, object perception, feeding, and graded counting. It points out the key issues that restrict the development of intelligent equipment and technology in aquaculture in China, A new concept of "mechanization, automation, and intelligence" for the development of aquaculture equipment and technology has been proposed, aiming to achieve the historical transformation of China from a major aquaculture country to a strong aquaculture country.
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Key words:
- aquaculture /
- equipment /
- mechanization /
- automation /
- intelligence
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图 1 养殖对象信息感知技术框图
Figure 1. Aquaculture objects intelligent sensing technology framework
表 1 环境监测装备产业应用现状
Table 1. The current application status of environmental monitoring equipment industry
系统类型
type典型代表
typical representative获取数据
data procurement应用现状
current status养殖环境监测系统 养殖用水监测 溶氧、pH、水温、盐度、氧化还原电位(ORP)等 逐步开始产业化应用 尾水监测 三态氮、亚硝酸盐、COD等 应用较少 养殖气象监测 气温、气压、光辐照度、风速、风向、降雨量等 广泛应用 
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表 2 投喂装备研究现状
Table 2. Research status of feeding equipment
应用场景
application scenarios主要研究方向
main research directions成熟度
stage池塘 自巡航虾塘移动投喂船 试验样机 料塔式集中投喂机 产品成熟 工厂化 轨道式投饲机 试验样机 智能投饲车 研发阶段 网箱 远距离风力投喂 产品成熟 大型投喂船 产品成熟
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表 3 智能投喂决策算法研究现状
Table 3. Research status of intelligent feeding decision algorithms
技术类型
type of technology主要研究方向
main research directions适用场景
applicable scene成熟度
stage光学
optics水上摄食行为监测 池塘、工厂化、网箱 研发阶段 水下集群行为监测 工厂化、网箱 研发阶段 声学
acoustics被动声学技术 池塘、网箱 试验样机 主动声学技术 网箱 研发阶段
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Ministry of Agriculture and Rural Affairs of the People's Republic of China, National Fisheries Technology Extension Center, China Society of Fisheries. 2021 China Fisheries Statistical Yearbook[M]. Beijing: China Agriculture Press, 2021 (in Chinese).
Ruan W, Wang Y, Ji W W, et al. Progress of sustainable development and management of aquaculture[J]. Fishery Information & Strategy, 2013, 28(4): 267-272 (in Chinese). doi: 10.3969/j.issn.1004-8340.2013.04.004
Task Force for the Study on Sustainable Development Strategy of Chinese Aquaculture Comprehensive Research Group. Development strategy on environmentally friendly aquaculture[J]. Engineering Sciences, 2016, 18(3): 1-7 (in Chinese). doi: 10.3969/j.issn.1009-1742.2016.03.002
Ye N H, Zhuang Z M, Wang Q Y. Development strategy for realizing the healthy aquaculture industry concept[J]. Engineering Sciences, 2016, 18(3): 101-104 (in Chinese). doi: 10.3969/j.issn.1009-1742.2016.03.018
Liu C L, Lin H Z, Li Y M, et al. Unmanned fishing grounds leading agricultural intelligence[J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(1): 1-18 (in Chinese). doi: 10.6041/j.issn.1000-1298.2020.01.001
Huang Y X, Bao X T, Xu H. Research progress of fishery equipment science and technology in China[J]. Fishery Modernization, 2023, 50(4): 1-11 (in Chinese).
Ministry of Agriculture and Rural Affairs of the People's Republic of China, National Fisheries Technology Extension Center, China Society of Fisheries. 2022 China Fisheries Statistical Yearbook[M]. Beijing: China Agriculture Press, 2022 (in Chinese).
Liu T C, Liu J, Wang J, et al. Optimization of the intelligent sensing model for environmental information in aquaculture waters based on the 5G smart sensor network[J]. Journal of Sensors, 2022, 2022: 6409046.
Ehlers S M, Maxein J, Koop J H E. Low‐cost microplastic visualization in feeding experiments using an ultraviolet light‐emitting flashlight[J]. Ecological Research, 2020, 35(1): 265-273. doi: 10.1111/1440-1703.12080
Wei Y G, Li W S, An D, et al. Equipment and intelligent control system in aquaponics: A review[J]. IEEE Access, 2019, 7: 169306-169326. doi: 10.1109/ACCESS.2019.2953491
Føre M, Frank K, Norton T, et al. Precision fish farming: A new framework to improve production in aquaculture[J]. Biosystems Engineering, 2018, 173: 176-193. doi: 10.1016/j.biosystemseng.2017.10.014
Biazi V, Marques C. Industry 4.0-based smart systems in aquaculture: A comprehensive review[J]. Aquacultural Engineering, 2023, 103: 102360. doi: 10.1016/j.aquaeng.2023.102360
Cao S Q, Ge Z R, Zhang Z. Buoy water quality monitoring system and prediction model based on internet of things[J]. Journal of Agricultural Machinery, 2021, 52(11): 210-218 (in Chinese). doi: 10.6041/j.issn.1000-1298.2021.11.022
Zhang J L, Xu L H, Liu S J. Classification of Atlantic salmon feeding behavior based on underwater machine vision[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(13): 158-164 (in Chinese). doi: 10.11975/j.issn.1002-6819.2020.13.019
Zuo Q, Tian Y C, Ma G Q. Research progress and problems of aquaculture intelligent feeding system[J]. Journal of Tianjin Agricultural University, 2020, 27(4): 73-77 (in Chinese). doi: 10.19640/j.cnki.jtau.2020.04.014
Li M Z, Zhang G F, Yu G Z, et al. Design and experiment of grading and counting device for scallop seedling[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(21): 93-101 (in Chinese). doi: 10.11975/j.issn.1002-6819.2015.21.012
Gong M G, Meng F L, Huang Y X, et al. Research on development status and countermeasures of intelligent aquaculture in China[J]. Fishery Modernization, 2018, 45(6): 60-66 (in Chinese). doi: 10.3969/j.issn.1007-9580.2018.06.010
Huang Y X, Ding J L, Bao X T, et al. Development research on China fishery equipment and engineering technology[J]. Fishery Modernization, 2019, 46(5): 1-8 (in Chinese). doi: 10.3969/j.issn.1007-9580.2019.05.001
Zhou X Y, Ni Q, Xu H, et al. Development report of China aquaculture whole-process mechanization in 2021[J]. Journal of Chinese Agricultural Mechanization, 2022, 43(12): 1-4 (in Chinese). doi: 10.13733/j.jcam.issn.2095-5553.2022.12.001
Gu H T, Wang Y Q. The development status, issues and trends of pond aeration technology in China[J]. Fishery Modernization, 2014, 41(5): 65-68 (in Chinese). doi: 10.3969/j.issn.1007-9580.2014.05.19
Wang X Y, Hong J Q, Sun Y P, et al. Design of trajectory planning system for river crab farming with automatic feeding boat[J]. Journal of Physics:Conference Series, 2020, 1575(1): 012143. doi: 10.1088/1742-6596/1575/1/012143
Hong Y, Chen X L, Tian C F, et al. Design and test of a kind of moving feeding device for crab and shrimp ponds[J]. Fishery Modernization, 2018, 45(3): 9-14 (in Chinese). doi: 10.3969/j.issn.1007-9580.2018.03.002
Chen X L, Tian C F, Yang J P, et al. Research on pneumatic automatic feeding machine for intensive pond aquaculture[J]. Fishery Modernization, 2016, 43(5): 18-22 (in Chinese). doi: 10.3969/j.issn.1007-9580.2016.05.004
Liu X G, Xu H, Zhang Y J, et al. Development and experiment of movable pond aquaculture water quality regulation machine based on solar energy[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(19): 1-10 (in Chinese). doi: 10.3969/j.issn.1002-6819.2014.19.001
Wu Z F, Cheng G F, Wang X R, et al. Evaluation on aeration performance of movable solar aerator[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(23): 246-252 (in Chinese). doi: 10.3969/j.issn.1002-6819.2014.23.031
Shang J Y, Tang Y H. Research progress of the dissolved oxygen sensor[J]. Micronanoelectronic Technology, 2014, 51(3): 168-175,202 (in Chinese). doi: 10.13250/j.cnki.wndz.2014.03.006
Liu Y S, Diao Y X, Hu G X, et al. Renewable antimony-based pH sensor[J]. Journal of Electroanalytical Chemistry, 2023, 928: 117085. doi: 10.1016/j.jelechem.2022.117085
Trevathan J, Read W, Sattar A. Implementation and calibration of an IoT light attenuation turbidity sensor[J]. Internet of Things, 2022, 19: 100576. doi: 10.1016/j.iot.2022.100576
Chauhan M, Singh V K. Hydrothermally grown ZnO nanorods based optical fiber sensor for salinity detection[J]. Measurement, 2022, 203: 111913. doi: 10.1016/j.measurement.2022.111913
Liu F, Wei S P, Li B, et al. A novel fast response and high precision water temperature sensor based on Fiber Bragg Grating[J]. Optik, 2023, 289: 171257. doi: 10.1016/j.ijleo.2023.171257
Huang Y X, Tian C F, Meng F L, et al. Research on the history, current situation and development of pond culture facilities and equipment in China[J]. Fishery Modernization, 2020, 47(3): 10-15 (in Chinese). doi: 10.3969/j.issn.1007-9580.2020.03.002
Huang Y X, Xu H, Ding J L. Situation of China Land-based Aquaculture Engineering Equipment and Suggestion for Its Development[J]. Guizhou Agricultural Sciences, 2016, 44(7): 87-91 (in Chinese). doi: 10.3969/j.issn.1001-3601.2016.07.025
Yang J M, Zhu H F. Lift type automatic water quality detection system for aquaculture[J]. Fishery Modernization, 2016, 43(4): 1-5 (in Chinese). doi: 10.3969/j.issn.1007-9580.2016.04.001
Cui L X, Ni Q, Zhuang B L, et al. Design and experiment for PLC-based rail-type automatic feeding system of factory aquaculture[J]. Guangdong Agricultural Sciences, 2014, 41(22): 159-165 (in Chinese). doi: 10.3969/j.issn.1004-874X.2014.22.035
Xiao M H, Li Y J, Wang X C, et al. Research progress of aquaculture tailwater treatment technology and equipment[J]. Journal of Nanjing Agricultural University, 2023, 46(1): 1-13 (in Chinese). doi: 10.7685/jnau.202201028
Chen H. Design and application of rotary drum backwashing microfilter[J]. Fujian Agricultural Machinery, 2022(1): 4-7,25 (in Chinese). doi: 10.3969/j.issn.1004-3969.2022.01.002
Roy S M, Pareek C M, Machavaram R, et al. Optimizing the aeration performance of a perforated pooled circular stepped cascade aerator using hybrid ANN-PSO technique[J]. Information Processing in Agriculture, 2022, 9(4): 533-546. doi: 10.1016/j.inpa.2021.09.002
Guan C W, Yang J, Shan J J, et al. Water treatment performance of O3/UV reaction system in recirculating aquaculture systems[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(23): 253-259 (in Chinese). doi: 10.3969/j.issn.1002-6819.2014.23.032
Shi M M, Ruan Y J, Liu H, et al. Solid phase distribution simulation of culture pond with recirculating biofloc technology based on computational fluid dynamics[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(2): 252-258 (in Chinese). doi: 10.11975/j.issn.1002-6819.2017.02.035
Zhang H G, Guan C W. Effluent purifying performance of new style fluidized-sand biofilter in recirculating aquaculture system[J]. Chinese Agricultural Science Bulletin, 2016, 32(29): 29-35 (in Chinese). doi: 10.11924/j.issn.1000-6850.casb16070138
Huang X H, Pang G L, Yuan T P, et al. Review of engineering and equipment technologies for deep-sea cage aquaculture in China[J]. Progress in Fishery Sciences, 2022, 43(6): 121-131 (in Chinese). doi: 10.19663/j.issn2095-9869.20210816003
Bao X T, Chen Z X, Cui M C, et al. Discussion and consideration on the development of deep sea aquaculture equipment in China[J]. Fishery Modernization, 2022, 49(5): 8-14 (in Chinese).
Fu X Y, Huang D Z, Xu H L, et al. Overview of the Development of Cage Aquaculture in Deep Sea[J]. Journal of Aquaculture, 2021, 42(10): 23-26 (in Chinese). doi: 10.3969/j.issn.1004-2091.2021.10.006
Wang Z Y, Feng S Q. Current status of fish capture technology in cage aquaculture[J]. Journal of Aquaculture, 2021, 42(7): 64-65 (in Chinese). doi: 10.3969/j.issn.1004-2091.2021.07.017
Huang Y X, Xu H, Ding J L. Research on the development of offshore aquaculture facilities and equipment in China[J]. Fishery Modernization, 2016, 43(2): 76-81 (in Chinese). doi: 10.3969/j.issn.1007-9580.2016.02.014
Selection results of light simplified technical equipment for pond aquaculture tail water treatment and raft suspended and bottom sowing enhanced aquaculture in 2022[EB/EO]. (2022-10-14). http://www.amic.agri.cn/secondLevelPage/info/30/146652 (in Chinese).
Li D L, Yang H. State-of-the-art review for internet of things in agriculture[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(1): 1-20 (in Chinese). doi: 10.6041/j.issn.1000-1298.2018.01.001
Yin B Q, Cao S S, Fu Z T, et al. Review and trend analysis of water quality monitoring and control technology in aquaculture[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(2): 1-13 (in Chinese). doi: 10.6041/j.issn.1000-1298.2019.02.001
Zhang K, Zhang H L, Li W J, et al. PtOEP/PS composite particles based on fluorescent sensor for dissolved oxygen detection[J]. Materials Letters, 2016, 172: 112-115. doi: 10.1016/j.matlet.2016.02.119
Rungsima C, Boonyan N, Klorvan M, et al. Hydrogel sensors with pH sensitivity[J]. Polymer Bulletin, 2021, 78(10): 5769-5787. doi: 10.1007/s00289-020-03398-8
Prerana M R, Shenoy B P, Pal B D, et al. Design, analysis, and realization of a turbidity sensor based on collection of scattered light by a fiber-optic probe[J]. IEEE Sensors Journal, 2012, 12(1): 44-50. doi: 10.1109/JSEN.2011.2128306
Pu H B, Liu D, Qu J H, et al. Applications of imaging spectrometry in inland water quality monitoring—a review of recent developments[J]. Water, Air, & Soil Pollution, 2017, 228(4): 131.
Carstea E M, Bridgeman J, Baker A, et al. Fluorescence spectroscopy for wastewater monitoring: A review[J]. Water Research, 2016, 95: 205-219. doi: 10.1016/j.watres.2016.03.021
Li D L, Liu C. Recent advances and future outlook for artificial intelligence in aquaculture[J]. Smart Agriculture, 2020, 2(3): 1-20 (in Chinese).
Duan Y E, Li D L, Li Z B, et al. Review on visual characteristic measurement research of aquatic animals based on computer vision[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(15): 1-11 (in Chinese). doi: 10.11975/j.issn.1002-6819.2015.15.001
Endo H, Wu H Y. Biosensors for the assessment of fish health: A review[J]. Fisheries Science, 2019, 85(4): 641-654. doi: 10.1007/s12562-019-01318-y
Li D L, Wang G X, Du L, et al. Recent advances in intelligent recognition methods for fish stress behavior[J]. Aquacultural Engineering, 2022, 96: 102222. doi: 10.1016/j.aquaeng.2021.102222
Hvas M, Folkedal O, Oppedal F. Heart rate bio-loggers as welfare indicators in Atlantic salmon (Salmo salar) aquaculture[J]. Aquaculture, 2020, 529: 735630. doi: 10.1016/j.aquaculture.2020.735630
Wu H Y, Aoki A, Arimoto T, et al. Fish stress become visible: A new attempt to use biosensor for real-time monitoring fish stress[J]. Biosensors and Bioelectronics, 2015, 67: 503-510. doi: 10.1016/j.bios.2014.09.015
Wu H Y, Ohnuki H, Hibi K, et al. Development of a label-free immunosensor system for detecting plasma cortisol levels in fish[J]. Fish Physiology and Biochemistry, 2016, 42(1): 19-27. doi: 10.1007/s10695-015-0113-2
Wu H Y, Ohnuki H, Ota S, et al. New approach for monitoring fish stress: A novel enzyme-functionalized label-free immunosensor system for detecting cortisol levels in fish[J]. Biosensors and Bioelectronics, 2017, 93: 57-64. doi: 10.1016/j.bios.2016.10.001
Endo H, Yonemori Y, Musiya K, et al. A needle-type optical enzyme sensor system for determining glucose levels in fish blood[J]. Analytica Chimica Acta, 2006, 573-574: 117-124. doi: 10.1016/j.aca.2006.04.068
Takase M, Yoneyama Y, Murata M, et al. Mediator-type biosensor for real-time wireless monitoring of blood glucose concentrations in fish[J]. Fisheries Science, 2012, 78(3): 691-698. doi: 10.1007/s12562-012-0495-3
Makaras T, Razumienė J, Gurevičienė V, et al. A new approach of stress evaluation in fish using β-D-Glucose measurement in fish holding-water[J]. Ecological Indicators, 2020, 109: 105829. doi: 10.1016/j.ecolind.2019.105829
Saberioon M, Císař P. Automated within tank fish mass estimation using infrared reflection system[J]. Computers and Electronics in Agriculture, 2018, 150: 484-492. doi: 10.1016/j.compag.2018.05.025
Hao M M, Yu H L, Li D L. The measurement of fish size by machine vision-a review[C]//Proceedings of the 9th International Conference on Computer and Computing Technologies in Agriculture. Beijing, China: Springer, 2016: 15-32.
Pérez D, Ferrero F J, Álvarez I, et al. Automatic measurement of fish size using stereo vision[C]//Proceedings of 2018 IEEE International Instrumentation and Measurement Technology Conference. Houston: IEEE, 2018.
Liu S J, Li G D, Tu X Y, et al. Research on the development of aquaculture production information technology[J]. Fishery Modernization, 2021, 48(3): 1-9 (in Chinese).
Garcia R, Prados R, Quintana J, et al. Automatic segmentation of fish using deep learning with application to fish size measurement[J]. ICES Journal of Marine Science, 2020, 77(4): 1354-1366. doi: 10.1093/icesjms/fsz186
Costa C, Antonucci F, Boglione C, et al. Automated sorting for size, sex and skeletal anomalies of cultured seabass using external shape analysis[J]. Aquacultural Engineering, 2013, 52: 58-64. doi: 10.1016/j.aquaeng.2012.09.001
Qian C. The three-dimensional detection system for high-throughput growth status of underwater fish independently developed by the Fishery Machinery Institute has successfully completed on-board testing and been delivered for use on the "Guoxin 1" ship[EB/OL]. (2022-11-24). http://www.fmiri.ac.cn/info/1013/22528.htm (in Chinese).
Papadakis V M, Papadakis I E, Lamprianidou F, et al. A computer-vision system and methodology for the analysis of fish behavior[J]. Aquacultural Engineering, 2012, 46: 53-59. doi: 10.1016/j.aquaeng.2011.11.002
Wang H, Zeng L J, Yin C Y. A video tracking system for measuring the position and body deformation of a swimming fish[J]. Review of Scientific Instruments, 2002, 73(12): 4381-4384. doi: 10.1063/1.1518143
Dell A I, Bender J A, Branson K, et al. Automated image-based tracking and its application in ecology[J]. Trends in Ecology & Evolution, 2014, 29(7): 417-428.
Kleinhappel T K, Pike T W, Burman O H P. Stress-induced changes in group behaviour[J]. Scientific Reports, 2019, 9(1): 17200. doi: 10.1038/s41598-019-53661-w
Israeli D, Kimmel E. Monitoring the behavior of hypoxia-stressed Carassius auratus using computer vision[J]. Aquacultural Engineering, 1996, 15(6): 423-440. doi: 10.1016/S0144-8609(96)01009-6
Zheng H Y, Liu R, Zhang R, et al. A method for real-time measurement of respiratory rhythms in medaka (Oryzias latipes) using computer vision for water quality monitoring[J]. Ecotoxicology and Environmental Safety, 2014, 100: 76-86. doi: 10.1016/j.ecoenv.2013.11.016
Terayama K, Hioki H, Sakagami M A. Measuring tail beat frequency and coast phase in school of fish for collective motion analysis[C]//Proceedings of SPIE 10225, Eighth International
Kelley J L, Phillips B, Cummins G H, et al. Changes in the visual environment affect colour signal brightness and shoaling behaviour in a freshwater fish[J]. Animal Behaviour, 2012, 83(3): 783-791. doi: 10.1016/j.anbehav.2011.12.028
Li D L, Hao Y F, Duan Y Q. Nonintrusive methods for biomass estimation in aquaculture with emphasis on fish: A review[J]. Reviews in Aquaculture, 2020, 12(3): 1390-1411. doi: 10.1111/raq.12388
Ohshimo S. Spatial distribution and biomass of pelagic fish in the East China Sea in summer, based on acoustic surveys from 1997 to 2001[J]. Fisheries Science, 2004, 70(3): 389-400. doi: 10.1111/j.1444-2906.2004.00818.x
Rowell T J, Nemeth R S, Schärer M T, et al. Fish sound production and acoustic telemetry reveal behaviors and spatial patterns associated with spawning aggregations of two Caribbean groupers[J]. Marine Ecology Progress Series, 2015, 518: 239-254. doi: 10.3354/meps11060
Handegard N O, Tenningen M, Howarth K, et al. Effects on schooling function in mackerel of sub-lethal capture related stressors: Crowding and hypoxia[J]. PLoS One, 2017, 12(12): e0190259. doi: 10.1371/journal.pone.0190259
Jézéquel Y, Bonnel J, Coston-Guarini J, et al. Sound characterization of the European lobster Homarus gammarus in tanks[J]. Aquatic Biology, 2018, 27: 13-23. doi: 10.3354/ab00692
Hassan S G, Ahmed S, Iqbal S, et al. Fish as a source of acoustic signal measurement in an aquaculture tank: Acoustic sensor based time frequency analysis[J]. International Journal of Agricultural and Biological Engineering, 2019, 12(3): 110-117. doi: 10.25165/j.ijabe.20191203.4238
Yin L M, Chen X Z, Zhang X G, et al. Measurement and analysis of the aquaculture noise for Larimichthys crocea in the fiberglass fish tank[J]. Marine Fisheries, 2017, 39(3): 314-321 (in Chinese). doi: 10.3969/j.issn.1004-2490.2017.03.009
Brown A, Garg S, Montgomery J. Automatic rain and cicada chorus filtering of bird acoustic data[J]. Applied Soft Computing, 2019, 81: 105501. doi: 10.1016/j.asoc.2019.105501
Zuo Q, Tian Y C, Ma G Q. Research progress and problems of aquaculture intelligent feeding system[J]. Journal of Tianjin Agricultural University, 2020, 27(4): 73-77 (in Chinese). doi: 10.19640/j.cnki.jtau.2020.04.014
Yuan K, Zhuang B L, Ni Q, et al. Design and experiments of automatic feeding system for indoor industrialization aquaculture[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(3): 169-176 (in Chinese).
Xiao H J, Liu K, Li L L, et al. Design of intelligent feeding car for multi-layer sink-type industrialized recirculating aquaculture system[J]. Fishery Modernization, 2019, 46(1): 21-26 (in Chinese). doi: 10.3969/j.issn.1007-9580.2019.01.004
Liu Z Q, Liu S X, Li W, et al. Design and test on feeding gun for marine cage calture[J]. Fishery Modernization, 2015, 42(3): 38-42 (in Chinese). doi: 10.3969/j.issn.1007-9580.2015.03.008
Oehme M, Aas T S, Sørensen M, et al. Feed pellet distribution in a sea cage using pneumatic feeding system with rotor spreader[J]. Aquacultural Engineering, 2012, 51: 44-52. doi: 10.1016/j.aquaeng.2012.07.001
Zhao J, Zhu S M, Ye Z Y, et al. Assessing method for feeding activity of swimming fishes in RAS[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(8): 288-293 (in Chinese). doi: 10.6041/j.issn.1000-1298.2016.08.038
Chen C W, Du Y G, Zhou C, et al. Evaluation of feeding activity of shoal based on image texture[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(5): 232-237 (in Chinese). doi: 10.11975/j.issn.1002-6819.2017.05.034
Qiao F, Zheng D, Hu L Y, et al. Research on smart bait casting machine based on machine vision technology[J]. Chinese Journal of Engineering Design, 2015, 22(6): 528-533 (in Chinese). doi: 10.3785/j.issn.1006-754X.2015.06.003
Guo J. Research on feeding patterns and bait technology of fish culture based on information of image and sound[D]. Ningbo: Ningbo University, 2018 (in Chinese).
Zhao L, Song X F, Li X, et al. Design and simulation of roller fish grader[J]. Fishery Modernization, 2023, 50(4): 68-75 (in Chinese).
Hong Y, Zhu Y, Jiang T, et al. Design and test of rotary live fish grading and counting device[J]. Fishery Modernization, 2019, 46(4): 49-54 (in Chinese). doi: 10.3969/j.issn.1007-9580.2019.04.008
Ma X Y, Li M, Xiong W C, et al. Design of sea cucumber grading and counting equipment based on technology of image recognition[J]. Journal of Dalian Fisheries University, 2016, 24(6): 549-552 (in Chinese).
Zhou X L, Ma C, Wang Z P, et al. Design and implementation of trait measurement system for common carp (Cyprinus carpio) and crucian carp (Carassius auratus) based on machine vision[J]. Fishery Modernization, 2022, 49(6): 108-117 (in Chinese). doi: 10.3969/j.issn.1007-9580.2022.06.014
Li Y J, Huang K W, Xiang J. Measurement of dynamic fish dimension based on stereoscopic vision[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(21): 220-226 (in Chinese). doi: 10.11975/j.issn.1002-6819.2020.21.026
Liao Y H, Zhou C W, Liu W Z, et al. 3DPhenoFish: Application for two- and three-dimensional fish morphological phenotype extraction from point cloud analysis[J]. Zoological Research, 2021, 42(4): 492-502. doi: 10.24272/j.issn.2095-8137.2021.141
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