Deep learning-based intelligent precise aeration strategy for factory recirculating aquaculture systems

IF 8.2 Q1 AGRICULTURE, MULTIDISCIPLINARY Artificial Intelligence in Agriculture Pub Date : 2024-04-15 DOI:10.1016/j.aiia.2024.04.001
Junchao Yang , Yuting Zhou , Zhiwei Guo , Yueming Zhou , Yu Shen
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Abstract

Factory recirculating aquaculture system (RAS) is facing in a stage of continuous research and technological innovation. Intelligent aquaculture is an important direction for the future development of aquaculture. However, the RAS nowdays still has poor self-learning and optimal decision-making capabilities, which leads to high aquaculture cost and low running efficiency. In this paper, a precise aeration strategy based on deep learning is designed for improving the healthy growth of breeding objects. Firstly, the situation perception driven by computer vision is used to detect the hypoxia behavior. Then combined with the biological energy model, it is constructed to calculate the breeding objects oxygen consumption. Finally, the optimal adaptive aeration strategy is generated according to hypoxia behavior judgement and biological energy model. Experimental results show that the energy consumption of proposed precise aeration strategy decreased by 26.3% compared with the manual control and 12.8% compared with the threshold control. Meanwhile, stable water quality conditions accelerated breeding objects growth, and the breeding cycle with the average weight of 400 g was shortened from 5 to 6 months to 3–4 months.

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基于深度学习的工厂化循环水产养殖系统智能精确曝气策略
工厂化循环水养殖系统(RAS)正面临着一个不断研究和技术创新的阶段。智能化养殖是未来水产养殖发展的重要方向。然而,目前的 RAS 自我学习和优化决策能力仍然较差,导致养殖成本高、运行效率低。本文设计了一种基于深度学习的精准曝气策略,以改善养殖对象的健康生长状况。首先,利用计算机视觉驱动的态势感知来检测缺氧行为。然后结合生物能量模型,计算繁殖对象的耗氧量。最后,根据缺氧行为判断和生物能模型生成最优自适应曝气策略。实验结果表明,所提出的精确曝气策略的能耗比手动控制降低了 26.3%,比阈值控制降低了 12.8%。同时,稳定的水质条件加快了繁殖对象的生长,平均体重 400 克的繁殖周期从 5 至 6 个月缩短到 3 至 4 个月。
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来源期刊
Artificial Intelligence in Agriculture
Artificial Intelligence in Agriculture Engineering-Engineering (miscellaneous)
CiteScore
21.60
自引率
0.00%
发文量
18
审稿时长
12 weeks
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