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Identification of spectral signature for in situ real-time monitoring of smoltification

机译:识别SPOLTIZE的原位实时监测谱特征

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We describe the use of an optical hyperspectral sensing technique to identify the smoltification status of Atlantic salmon (Salmo salar) based on spectral signatures, thus potentially providing smolt producers with an additional tool to verify the osmoregulatory state of salmon. By identifying whether a juvenile salmon is in the biological freshwater stage (parr) or has adapted to the seawater stage (smolt) before transfer to sea, negative welfare impacts and subsequent mortality associated with failed or incorrect identification may be reduced. A hyperspectral imager has been used to collect data in two water flow-through and one recirculating production site in parallel with the standard smoltification evaluations applied at these sites. The results from the latter have been used as baseline for a machine-learning algorithm trained to identify whether a fish was parr or smolt based on its spectral signature. The developed method correctly classified fish in 86% to 100% of the cases for individual sites, and had an overall average classification accuracy of 90%, thus indicating that analysis of spectral signatures may constitute a useful tool for smoltification monitoring. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
机译:我们描述了使用光学高光谱传感技术,根据光谱特征识别大西洋鲑鱼(Salmo salar)的混浊状态,从而有可能为混浊生产者提供额外的工具,以验证鲑鱼的渗透调节状态。通过确定幼年三文鱼是否处于生物淡水阶段(parr)或在转移到海洋之前已经适应了海水阶段(smolt),可以减少与识别失败或错误相关的负面福利影响和随后的死亡率。一台高光谱成像仪已被用于在两个水流通过和一个再循环生产现场收集数据,同时在这些现场进行标准雾化评估。后者的结果被用作机器学习算法的基线,该算法经过训练,可以根据一条鱼的光谱特征识别它是帕尔还是斯莫尔特。所开发的方法在86%到100%的情况下对单个地点的鱼类进行了正确分类,总体平均分类准确率为90%,因此表明光谱特征分析可能是一种有用的监测工具。(c)OSA开放获取出版协议条款下的美国2021光学学会

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