{"title":"水产养殖回顾:从单一模式分析到多模式融合","authors":"","doi":"10.1016/j.compag.2024.109367","DOIUrl":null,"url":null,"abstract":"<div><p>Efficient management and accurate monitoring are crucial for the sustainable development of the aquaculture industry. Traditionally, monitoring methods have relied on single-modality approaches (e.g., physical sensors, vision, and audio). However, these methods are limited by environmental interference and inability to comprehensively capture the complex characteristics of aquatic organisms, leading to data bias, low identification accuracy, and poor model portability across different settings. In contrast, multimodal fusion technologies have emerged as a promising solution for intelligent aquaculture due to their strong environmental adaptability, information complementarity, and high generalization ability. Despite this potential, there is a lack of comprehensive literature reviewing the transition from single-modal to multimodal systems in aquaculture. This paper addresses this gap by presenting a systematic review of both single-modal and multimodal fusion technologies in aquaculture over the past two decades. We analyze the strengths and limitations of each approach, focusing on four key areas: water quality monitoring, feeding behavior analysis, disease prediction, and biomass estimation. Through this comprehensive analysis, we provide theoretical and practical insights into the application of multimodal fusion technology in aquaculture, highlighting its potential to enhance efficiency and sustainability while overcoming current limitations.</p></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A review of aquaculture: From single modality analysis to multimodality fusion\",\"authors\":\"\",\"doi\":\"10.1016/j.compag.2024.109367\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Efficient management and accurate monitoring are crucial for the sustainable development of the aquaculture industry. Traditionally, monitoring methods have relied on single-modality approaches (e.g., physical sensors, vision, and audio). However, these methods are limited by environmental interference and inability to comprehensively capture the complex characteristics of aquatic organisms, leading to data bias, low identification accuracy, and poor model portability across different settings. In contrast, multimodal fusion technologies have emerged as a promising solution for intelligent aquaculture due to their strong environmental adaptability, information complementarity, and high generalization ability. Despite this potential, there is a lack of comprehensive literature reviewing the transition from single-modal to multimodal systems in aquaculture. This paper addresses this gap by presenting a systematic review of both single-modal and multimodal fusion technologies in aquaculture over the past two decades. We analyze the strengths and limitations of each approach, focusing on four key areas: water quality monitoring, feeding behavior analysis, disease prediction, and biomass estimation. Through this comprehensive analysis, we provide theoretical and practical insights into the application of multimodal fusion technology in aquaculture, highlighting its potential to enhance efficiency and sustainability while overcoming current limitations.</p></div>\",\"PeriodicalId\":50627,\"journal\":{\"name\":\"Computers and Electronics in Agriculture\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.7000,\"publicationDate\":\"2024-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers and Electronics in Agriculture\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0168169924007580\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Electronics in Agriculture","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168169924007580","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
A review of aquaculture: From single modality analysis to multimodality fusion
Efficient management and accurate monitoring are crucial for the sustainable development of the aquaculture industry. Traditionally, monitoring methods have relied on single-modality approaches (e.g., physical sensors, vision, and audio). However, these methods are limited by environmental interference and inability to comprehensively capture the complex characteristics of aquatic organisms, leading to data bias, low identification accuracy, and poor model portability across different settings. In contrast, multimodal fusion technologies have emerged as a promising solution for intelligent aquaculture due to their strong environmental adaptability, information complementarity, and high generalization ability. Despite this potential, there is a lack of comprehensive literature reviewing the transition from single-modal to multimodal systems in aquaculture. This paper addresses this gap by presenting a systematic review of both single-modal and multimodal fusion technologies in aquaculture over the past two decades. We analyze the strengths and limitations of each approach, focusing on four key areas: water quality monitoring, feeding behavior analysis, disease prediction, and biomass estimation. Through this comprehensive analysis, we provide theoretical and practical insights into the application of multimodal fusion technology in aquaculture, highlighting its potential to enhance efficiency and sustainability while overcoming current limitations.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.