摘要
为实现高效、快速和准确的虾类识别和行为观测,提出一种基于主动识别声呐(DIDSON)的养殖塘南美白对虾探测和行为分析方法。该方法利用无人艇搭载主动识别声呐(DIDSON)对养殖塘设置的8个观测点进行连续定点观测,利用基于ECHOVIEW构建的虾类识别计数模型对采集的图像数据进行识别提取,通过目标识别、目标提取、通量分析以及方向分析等方法获得养殖塘南美白对虾的初步行为特征。试验在8个观测点观测的虾群数量极值分别为251只和208只,均值为234只,单位时间内的虾群通量区间为[108,131]只/(min·m^(2)),不同点位虾群正向游塘的占比均大于85%,通量变化较小。结果显示,该方法可以观测到养殖塘内虾群存在规律性的游塘行为,相较于传统的水下视觉观察和被动声呐调查,有效解决了养殖塘中虾群行为观测的难题,为制定更加高效的虾塘饲养和管理方案提供科学的数据支持。
Studying the underwater behavioral characteristics of shrimp is important for shrimp farming.In order to achieve efficient,rapid and accurate shrimp identification and behavior observation,a detection and behavior analysis method of Penaeus vannamei in aquaculture ponds based on active identification sonar(DIDSON)is proposed.Eight observation points were set up in the shrimp pond,each with a distance of 40 m between them,and the two banks were symmetrically distributed.Since the traditional way of setting up brackets for fixed-point observation will consume a lot of manpower and material resources,a set of DIDSON sonar observation system based on intelligent double-hull unmanned boats is designed,that is,the use of unmanned ships to carry active identification sonar(DIDSON)can realize continuous fixed-point observation of 8 observation points in a short period of time(observation time of each observation point is 3 min,interval time is 7 min).In this experiment,a shrimp identification and counting model was constructed based on the underwater acoustic data processing software ECHOVIEW,and the image data collected were identified and extracted,and the preliminary behavioral characteristics of Penaeus vannamei in the aquaculture pond were obtained by target recognition,target extraction,flux analysis and cruise direction analysis.The extreme values of the number of shrimps observed in the eight observation points were 251 and 208,respectively,and the average value was 234,indicating that the distribution of Penaeus vannamei shrimp in the breeding pond was relatively uniform.The flux interval of shrimp swarm per unit time was[108,131]/(min·m^(2)),the mean value was 122/(min·m^(2)),the standard deviation was 6/(min·m^(2)),and the flux change was small.The proportion of positive swimming ponds of shrimp groups at different points was greater than 85%,and the highest could reach 92.57%.The results show that this method can observe the regular swimming behavior of shrimp groups in aquaculture ponds,which effectively solves the problem of observation of shrimp groups in aquaculture ponds compared with traditional underwater visual observation and passive sonar investigation,and provides scientific data support for the formulation of more efficient shrimp pond feeding and management schemes.
作者
沈蔚
卢泉水
彭战飞
曹正良
张进
SHEN Wei;LU Quanshui;PENG Zhanfei;CAO Zhengliang;ZHANG Jin(College of Ocean Ography and Ecological Science,Shanghai Ocean,Shanghai 201306,China;Shanghai Estuary Marine Surveying and Mapping Engineering Technology Research Center,Shanghai Ocean University,Shanghai 201306,China)
出处
《渔业现代化》
CSCD
北大核心
2024年第1期64-70,共7页
Fishery Modernization
基金
上海市农委科技兴农技术创新项目(沪农科创字2022第2-1号)
上海市科技兴农项目(2019-02-08-00-10-F01117)。