Nonintrusive and Effective Volume Reconstruction Model of Swimming Sturgeon Based on RGB-D Sensor

文献类型： 外文期刊

第一作者： Lin, Kai

作者： Lin, Kai;Hu, Junjie;Hu, Hongxia;Lin, Kai;Hu, Junjie;Hu, Hongxia;Lin, Kai;Hu, Hongxia;Zhang, Shiyu;Hu, Junjie;Li, Hongsong;Guo, Wenzhong

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关键词： sturgeon; volume reconstruction; deep learning; nonintrusive; RGB-D sensor

期刊名称：SENSORS （ 影响因子：3.4； 五年影响因子：3.7 ）

ISSN：

年卷期： 2024 年 24 卷 15 期

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收录情况： SCI

摘要： The sturgeon is an important commercial aquaculture species in China. The measurement of sturgeon mass plays a remarkable role in aquaculture management. Furthermore, the measurement of sturgeon mass serves as a key phenotype, offering crucial information for enhancing growth traits through genetic improvement. Until now, the measurement of sturgeon mass is usually conducted by manual sampling, which is work intensive and time consuming for farmers and invasive and stressful for the fish. Therefore, a noninvasive volume reconstruction model for estimating the mass of swimming sturgeon based on RGB-D sensor was proposed in this paper. The volume of individual sturgeon was reconstructed by integrating the thickness of the upper surface of the sturgeon, where the difference in depth between the surface and the bottom was used as the thickness measurement. To verify feasibility, three experimental groups were conducted, achieving prediction accuracies of 0.897, 0.861, and 0.883, which indicated that the method can obtain the reliable, accurate mass of the sturgeon. The strategy requires no special hardware or intensive calculation, and it provides a key to uncovering noncontact, high-throughput, and highly sensitive mass evaluation of sturgeon while holding potential for evaluating the mass of other cultured fishes.

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