计算和测量智能虾养殖系统的大小和胃饱度

IF 3.2 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Connection Science Pub Date : 2023-10-18 DOI:10.1080/09540091.2023.2268878
Yu-Kai Lee, Bo-Yi Lin, Tien-Hsiung Weng, Chien-Kang Huang, Chen Liu, Chih-Chin Liu, Shih-Shun Lin, Han-Ching Wang
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引用次数: 0

摘要

对虾养殖业正在经历快速增长。为了减少成本和劳动力,计数和尺寸估计等自动化技术正越来越多地被采用。根据胃的饱腹程度饲喂可以显著减少食物浪费和水污染。因此,我们提出了一种智能养虾系统,该系统包括虾的检测,虾的近似长度和虾的数量的测量,以及两种方法来确定消化道的丰满程度。我们在系统中引入AR-YOLOv5(角度旋转YOLOv5),以提高虾的生长和虾养殖的环境可持续性。我们的实验是在真实的虾养殖环境中进行的。根据边界框估计虾的长度和数量,并使用虾的消化道与体型的比例来估计胃的饱腹程度。在检测性能方面,采用AR-YOLOv5,我们提出的方法的准确率为97.70%,召回率为91.42%,平均准确率为94.46%,f1得分为95.42%。此外,我们的胃饱度测定方法在真实虾养殖环境中准确率为88.8%,准确率为91.7%,召回率为90.9%,f1得分为91.3%。
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Counting and measuring the size and stomach fullness levels for an intelligent shrimp farming system
The penaeid shrimp farming industry is experiencing rapid growth. To reduce costs and labour, automation techniques such as counting and size estimation are increasingly being adopted. Feeding based on the degree of stomach fullness can significantly reduce food waste and water contamination. Therefore, we propose an intelligent shrimp farming system that includes shrimp detection, measurement of approximated shrimp length, shrimp quantity, and two methods for determining the degree of digestive tract fullness. We introduce AR-YOLOv5 (Angular Rotation YOLOv5) in the system to enhance both shrimp growth and the environmental sustainability of shrimp farming. Our experiments were conducted in a real shrimp farming environment. The length and quantity are estimated based on the bounding box, and the level of stomach fullness is approximated using the ratio of the shrimp´s digestive tract to its body size. In terms of detection performance, our proposed method achieves a precision rate of 97.70%, a recall rate of 91.42%, a mean average precision of 94.46%, and an F1-score of 95.42% using AR-YOLOv5. Furthermore, our stomach fullness determined method achieves an accuracy of 88.8%, a precision rate of 91.7%, a recall rate of 90.9%, and an F1-score of 91.3% in real shrimp farming environments.
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来源期刊
Connection Science
Connection Science 工程技术-计算机:理论方法
CiteScore
6.50
自引率
39.60%
发文量
94
审稿时长
3 months
期刊介绍: Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.
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