Aquaculture is now the main source of seafood in human diets and is one of its fastest-growing industries worldwide. However, the industry is facing several difficulties, including infectious diseases, the most significant limiting factor for aquaculture expansion. The impact of diseases on aquaculture growth, fecundity, mortality rates, and marketability is profound. Hence, the ability to predict disease outbreaks is crucial to overcoming these challenges. Various infectious agents such as bacteria, viruses, fungi, and parasites can cause significant losses of fish in intensive aquaculture practices. In an aquaculture environment, the high host density coupled with restricted water flow promotes pathogen spread. Early detection of disease is crucial for farmers as mortality rates can reach as high as 100% if left untreated. Therefore, new techniques and technical solutions for disease management in aquaculture are required. In this context, data analytics technologies, such as internet of things (IoT) sensors, artificial intelligence, and machine learning, allow farmers to proactively monitor their farms and detect potential disease outbreaks before they strike. Here, we highlighted the potential of machine learning algorithms in early pathogen detection and the possibilities of intelligent aquaculture in controlling disease outbreaks at the farm level. IoT is currently a popular study topic for smarter and sustainable aquaculture, as seen by the growing interest and broad overall assumptions. Therefore, this review aims to provide comprehensive information on the various aspects and challenges associated with modern technologies for controlling pathogenic microorganisms, as well as the potential benefits of using the IoT to improve fish health and welfare in aquaculture.

中文翻译：

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鱼病检测和控制的前沿技术：现状、前景和挑战

水产养殖现在是人类饮食中海鲜的主要来源，也是全球增长最快的行业之一。然而，该行业面临着一些困难，其中传染病是水产养殖扩张的最重要限制因素。疾病对水产养殖生长、繁殖力、死亡率和适销性的影响是深远的。因此，预测疾病爆发的能力对于克服这些挑战至关重要。各种传染源，如细菌、病毒、真菌和寄生虫，可能会导致集约化水产养殖中鱼类的大量损失。在水产养殖环境中，高寄主密度加上有限的水流促进了病原体的传播。及早发现疾病对农民至关重要，因为如果不及时治疗，死亡率可能高达 100%。因此，需要水产养殖疾病管理的新技术和技术解决方案。在此背景下，物联网 (IoT) 传感器、人工智能和机器学习等数据分析技术使农民能够主动监控他们的农场，并在潜在的疾病爆发之前发现它们。在这里，我们强调了机器学习算法在早期病原体检测中的潜力以及智能水产养殖在农场层面控制疾病爆发的可能性。从人们日益增长的兴趣和广泛的总体假设可以看出，物联网目前是智能和可持续水产养殖的热门研究主题。因此，本综述旨在提供与控制病原微生物的现代技术相关的各个方面和挑战的全面信息，以及利用物联网改善水产养殖中鱼类健康和福利的潜在好处。