Li Daoliang, Liu Chang, Song Zhaoyang, Wang Guangxu
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China.
Animals (Basel). 2021 Sep 16;11(9):2709. doi: 10.3390/ani11092709.
Crustacean farming is a fast-growing sector and has contributed to improving incomes. Many studies have focused on how to improve crustacean production. Information about crustacean behavior is important in this respect. Manual methods of detecting crustacean behavior are usually infectible, time-consuming, and imprecise. Therefore, automatic growth situation monitoring according to changes in behavior has gained more attention, including acoustic technology, machine vision, and sensors. This article reviews the development of these automatic behavior monitoring methods over the past three decades and summarizes their domains of application, as well as their advantages and disadvantages. Furthermore, the challenges of individual sensitivity and aquaculture environment for future research on the behavior of crustaceans are also highlighted. Studies show that feeding behavior, movement rhythms, and reproduction behavior are the three most important behaviors of crustaceans, and the applications of information technology such as advanced machine vision technology have great significance to accelerate the development of new means and techniques for more effective automatic monitoring. However, the accuracy and intelligence still need to be improved to meet intensive aquaculture requirements. Our purpose is to provide researchers and practitioners with a better understanding of the state of the art of automatic monitoring of crustacean behaviors, pursuant of supporting the implementation of smart crustacean farming applications.
甲壳类养殖是一个快速发展的领域，对提高收入做出了贡献。许多研究都集中在如何提高甲壳类产量上。在这方面，有关甲壳类行为的信息很重要。人工检测甲壳类行为的方法通常不可靠、耗时且不准确。因此，根据行为变化进行自动生长状况监测受到了更多关注，包括声学技术、机器视觉和传感器。本文回顾了过去三十年这些自动行为监测方法的发展，总结了它们的应用领域以及优缺点。此外，还强调了个体敏感性和水产养殖环境对未来甲壳类行为研究的挑战。研究表明，摄食行为、运动节律和繁殖行为是甲壳类最重要的三种行为，先进机器视觉技术等信息技术的应用对于加速开发更有效的自动监测新手段和新技术具有重要意义。然而，准确性和智能性仍需提高以满足集约化水产养殖的要求。我们的目的是让研究人员和从业者更好地了解甲壳类行为自动监测的现状，以支持智能甲壳类养殖应用的实施。
