利用微环境和呼吸角多模态传感技术对无水低温胁迫下的鲟鱼呼吸进行无损检测

IF 4.4 1区 农林科学 Q1 AGRICULTURAL ENGINEERING
Luwei Zhang , You Li , Wensheng Wang , Huanhuan Feng , Jinyou Hu , Xiaoshuan Zhang
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引用次数: 0

摘要

无水低温运输是一种绿色高效的活鱼运输方式。然而,无水和低温条件可能会导致活鱼产生应激反应,从而降低运输成活率。如何智能监控逆境胁迫下活鱼的呼吸应激状态仍是一项挑战。温度(T)、相对湿度(RH)、氧气(O2)和二氧化碳(CO2)信号可以反映逆境应激环境的变化;而呼吸角传感器则可以监测鱼鳃的开合角度(呼吸角)来反映鱼类呼吸的变化。本研究设计并开发了微环境和呼吸角传感器系统,以全面评估鱼类的呼吸应激状态。同时,建立了卡尔曼滤波-四元数-快速傅立叶变换方法来处理呼吸角信号。呼吸角信号表明中华鲟的呼吸应激分为三个阶段:急性波动阶段(0-2.5h)、机体调节阶段(2.5-16h)和累积应激阶段(>16h)。此外,还建立了线性回归(LR)、反向传播神经网络(BPNN)、支持向量回归(SVR)和径向基函数神经网络(RBFNN)模型来预测呼吸效率信号。RBFNN 模型的 R2(0.9544)明显高于 LR(0.8092)、BPNN(0.9289)和 SVR(0.9428)模型。该研究为进一步智能监测和管理无水低温条件下鱼类呼吸应激状态提供了参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-destructive detection of sturgeon breath under waterless low temperature stress using microenvironment and breath angle multi-modal sensing

Waterless and low temperature transportation is a green and efficient way for the transportation of live fish. However, waterless and low temperature conditions could lead to a stress response in live fish, resulting in reduced transport survival rates. It is still a challenge to intelligently monitor the breath stress state of live fish under adversity stress. Temperature (T), relative humidity (RH), oxygen (O2) and carbon dioxide (CO2) signals can reflect changes in adversity stress environment; while the breath angle sensors can monitor the gill opening and closing angle (breath angle) to reflect changes in fish breath. In this work, microenvironment and breath angle sensor systems were designed and developed to comprehensively evaluate the breath stress state of fish. Meanwhile, the Kalman filter-quaternion-fast Fourier transform method was established to process the breath angle signal. The breath angle signal indicated that the sturgeon had three levels of breath stress: acute fluctuation stage (0–2.5h), organismal regulation stage (2.5–16h) and cumulative stress stage (>16h). In addition, linear regression (LR), back propagation neural network (BPNN), support vector regression (SVR), and radial basis function neural network (RBFNN) models were established for breath efficiency signal prediction. The R2 of the RBFNN (0.9544) model was significantly higher than the LR (0.8092), BPNN (0.9289), and SVR (0.9428) models. This study provided a reference for further intelligent monitoring and management of the fish breath stress state under waterless and low temperature conditions.

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来源期刊
Biosystems Engineering
Biosystems Engineering 农林科学-农业工程
CiteScore
10.60
自引率
7.80%
发文量
239
审稿时长
53 days
期刊介绍: Biosystems Engineering publishes research in engineering and the physical sciences that represent advances in understanding or modelling of the performance of biological systems for sustainable developments in land use and the environment, agriculture and amenity, bioproduction processes and the food chain. The subject matter of the journal reflects the wide range and interdisciplinary nature of research in engineering for biological systems.
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