鱼类养殖智能投喂方法研究进展

在鱼类养殖过程中，饲料成本是主要养殖成本，如何做到合理投喂是减少养殖成本、提高养殖效益的关键。智能投喂是基于各类传感器获取环境和鱼群的各类信息，结合相关算法模型进行决策的投喂方式，是提高鱼类养殖投喂效率的重要手段。目前，鱼类的智能投喂已经取得了一些成果，但由于复杂多变的养殖环境和鱼类行为的不确定性，实现鱼类智能投喂仍面临挑战。该研究综述了鱼类养殖智能投喂的应用与进展，包括基于计算机视觉技术的鱼类摄食行为分析与饲料检测，声学技术、其他传感器技术和生物模型在智能投喂中的应用与发展。此外，分析了投饵机和投喂系统的研究现状，并总结了目前研究存在的问题。今后，要进一步加强水产、工学、信息等多学科的交叉融合，对鱼类图像、声音、生长规律与生物特征等多种信息进行综合分析应用，以提高投喂系统对多场景和多种养殖方式的适应性。

Research progress on intelligent feeding methods in fish farming

Feeding has been one of the most cost sources in fish farming so far. Artificial and mechanized feeding has been the most two widely-applied approaches to fish feeding in China. However, both feeding cannot be cost saving , due mainly to the amount of feed always depends on the experience rather than the real hunger situation of fish. Once the feed is superfluous, it is likely to the water contamination in many cases. It is necessary to appropriately adjust the amount of feed, according to the timely feeding information. As a result, a more flexible feeding has been a high demand to control the feed amount, according to the changes in fish behavior, in order to effectively reduce costs for more benefits. Intelligent feeding has been partially realized, whether via the detected information from the feeding process through various sensors, or via the developed predicting models as a feeding guideline for the decision-making. However, there are still great challenges to realizing completely intelligent feeding. However, the predicted information depends mainly on the complex farming environment and uncertain fish behavior. In this review, the most technical applications were categorized in the fish intelligent feeding over the recent decades, including fish feeding behavior, visually intelligent feed detection, acoustics, sensor, and biological model technology. Several types of commonly-used fish feeders were determined, including the gravity drop, pneumatic conveying, centrifugal throwing, and hydraulic conveying types. A better performance was achieved to avoid the labor costs and feed wasting, if the feeder was precisely selected to fit well the actual breeding operation, according to the various fish types or breeding modes. Some aspects of the feeder still need to be improved for the goal of complete intelligence. More advanced techniques can be required to meet the future requirements of the feeder in the long-distance precise transmission for large-scale fish farming, specifically including average feeding and wide-range feeding. By contrast, an accurate and reliable feeding can be achieved for the feeder in the factory intensive farming mode, with emphasis on the small-size design and technical revolution. Furthermore, there is also some change from the timer and quantitative feeding to intelligent feeding in machines. Finally, a systematic investigation was made to summarize the national and international research on the specific application scenarios and basic components of intelligent feeding systems. It indicated that the signal transmission and control system were dominated in the most intelligent machines of fish feeding, where the input data was generally collected from the farming environment and fish behavior in practice. The document analysis on intelligent feeding demonstrated that the instant behavior of fish was usually identified to guide the feeding operation under the experimental condition in fish farming. Yet, there was still something to do better: 1) Some exogenous factors can pose some elusive impacts on the actual feeding operation, such as the environmental fluctuations or human activities; 2) The feeding decision-making models can be required to upgrade or modify for the wide universality suitable for the more types of scenarios and fish; 3) The computing equipment can be more powerful to promote more reliable data sampling. In conclusion, the main purpose of an intelligent feeding system can be to fit much better with the various feeding situations, whether different fish types or different breeding modes. As such, a much more accurate judgment can be achieved to take more variables into the comprehensive consideration. Therefore, it is a promising potential way to combine more available disciplines like Aquaculture, Engineering, and Computer Science, or incorporate more accessible information, such as images, sounds, growth patterns, and biological characteristics. The multidisciplinary or multimedia fusing entity can greatly contribute to the fish feeding system for fully complete intelligence in the near future.