{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T17:52:52Z","timestamp":1774374772142,"version":"3.50.1"},"reference-count":93,"publisher":"Walter de Gruyter GmbH","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In the aquaculture industry, maintaining stable levels of dissolved oxygen (DO) is crucial for ensuring the health of aquatic organisms and enhancing farming efficiency. This article delves into the challenges faced in predicting and controlling DO levels, such as the need for real-time monitoring and response, the complexity of systems, and limitations in technology and resources. The paper comprehensively reviews various methods for DO prediction and control, including mechanistic modeling prediction, machine learning techniques, and both classical and intelligent control strategies. It analyzes their advantages, limitations, and applicability in aquaculture environments. Through this review and analysis, the article provides more comprehensive insights and guidance for future research directions in DO prediction and control in aquaculture.<\/jats:p>","DOI":"10.1515\/auto-2023-0212","type":"journal-article","created":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T06:47:04Z","timestamp":1719298024000},"page":"499-517","source":"Crossref","is-referenced-by-count":3,"title":["Advances in dissolved oxygen prediction and control methods in aquaculture: a review"],"prefix":"10.1515","volume":"72","author":[{"given":"Daoliang","family":"Li","sequence":"first","affiliation":[{"name":"National Innovation Center for Digital Fishery, China Agricultural University , Beijing 100083 , China"},{"name":"Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs , Beijing 100083 , China"}]},{"given":"Jianan","family":"Yang","sequence":"additional","affiliation":[{"name":"National Innovation Center for Digital Fishery, China Agricultural University , Beijing 100083 , China"},{"name":"College of Information and Electronics Engineering , China Agricultural University , Beijing 100083 , China"}]},{"given":"Yu","family":"Bai","sequence":"additional","affiliation":[{"name":"National Innovation Center for Digital Fishery, China Agricultural University , Beijing 100083 , China"},{"name":"Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs , Beijing 100083 , China"}]},{"given":"Zhuangzhuang","family":"Du","sequence":"additional","affiliation":[{"name":"National Innovation Center for Digital Fishery, China Agricultural University , Beijing 100083 , China"},{"name":"Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs , Beijing 100083 , China"}]},{"given":"Cong","family":"Wang","sequence":"additional","affiliation":[{"name":"National Innovation Center for Digital Fishery, China Agricultural University , Beijing 100083 , China"},{"name":"Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs , Beijing 100083 , China"}]}],"member":"374","published-online":{"date-parts":[[2024,6,25]]},"reference":[{"key":"2024062506462701382_j_auto-2023-0212_ref_001","doi-asserted-by":"crossref","unstructured":"B. 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