Published: July 9, 2024
Language: Английский
Aquaculture Fish and Fisheries, Journal Year: 2025, Volume and Issue: 5(1)
Published: Jan. 16, 2025
ABSTRACT Increasing consideration of welfare in aquaculture has prompted interest non‐invasive methods monitoring that avoid unnecessary stress and handling. Machine vision (MV) provides a potential solution to these needs, as it can be used for animal health real‐time. We examined the practical applications MV aquaculture, hardware algorithms automated data collection, main challenges solutions processing analysis. The most common application been estimation size‐related metrics (growth, biomass) fish, but key aspects welfare, such parasites disease or detection stress‐related behaviours, are lagging behind. Numerous camera setups have used, ranging from single stereoscopic cameras emersed submerged cameras, often under optimal conditions may not always reflect those prevalent industry (high densities, low visibility), likely overestimating performance. Object algorithms, YOLO, approach choice our review identified an increasing number alternatives help circumvent some posed by high densities poor lighting typical commercial farms. transform there still important need overcome before become mainstream, namely ability detect ectoparasites diseases, identify abnormal work across taxa, particularly crustaceans.
Language: Английский
Citations
2
Fishes, Journal Year: 2022, Volume and Issue: 7(6), P. 335 - 335
Published: Nov. 17, 2022
Computer vision has been applied to fish recognition for at least three decades. With the inception of deep learning techniques in early 2010s, use digital images grew strongly, and this trend is likely continue. As number articles published grows, it becomes harder keep track current state art determine best course action new studies. In context, article characterizes by identifying main studies on subject briefly describing their approach. contrast with most previous reviews related technology recognition, monitoring, management, rather than providing a detailed overview being proposed, work focuses heavily challenges research gaps that still remain. Emphasis given prevalent weaknesses prevent more widespread type practical operations under real-world conditions. Some possible solutions potential directions future are suggested, as an effort bring developed academy closer meeting requirements found practice.
Language: Английский
Citations
46
Information Sciences, Journal Year: 2024, Volume and Issue: 679, P. 121078 - 121078
Published: June 19, 2024
Language: Английский
Citations
9
Artificial Intelligence in Agriculture, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 1, 2025
Language: Английский
Citations
1
Applied Sciences, Journal Year: 2023, Volume and Issue: 13(6), P. 3812 - 3812
Published: March 16, 2023
Automatic fish recognition using deep learning and computer or machine vision is a key part of making the industry more productive through automation. An automatic sorting system will help to tackle challenges increasing food demand threat scarcity in future due continuing growth world population impact global warming climate change. As far as authors know, there has been no published work so detect classify moving for culture industry, especially purposes based on species vision. This paper proposes an approach algorithm YOLOv4, optimized with unique labeling technique. The proposed method was tested videos real running conveyor, which were put randomly position order at speed 505.08 m/h could obtain accuracy 98.15%. study simple but effective expected be guide automatically detecting, classifying, fish.
Language: Английский
Citations
19
Journal of The Institution of Engineers (India) Series B, Journal Year: 2023, Volume and Issue: 104(3), P. 603 - 621
Published: April 21, 2023
Language: Английский
Citations
16
Published: Jan. 11, 2024
The utilization of smart IoT devices, commonly referred to as digital twins, is aimed at the digitalization human knowledge within aquaculture processes. This involves incorporation cutting-edge technologies, including information-based management with big data and modeling, automate machinery gain comprehensive insights into environment fish farm conditions. ultimate objective empower farmers make informed decisions, furnishing them enhance their capacity in monitoring controlling various factors impacting production. As a result, farming decisions can be fine-tuned health optimize output. In context large modern farms, technological innovation becomes imperative processes, minimize labor requirements, streamline feeding operations. Remarkably, literature currently offers limited discussions on transformation through application twin methodologies. A prior study underscores critical influence such market prices survival rates profitability offshore caging culture. this study, we embark an analysis prerequisites for establishing infrastructure tailored intelligent management. designed facilitate integration technology data-driven decision-making, ultimately enhancing efficiency proposed architecture encompasses components, encompassing water quality forecasting, population assessment, metrics estimation, feed prediction, evaluation intensity. Furthermore, daily process reinforcement learning algorithms. Finally, implement cloud-based AIoT system that provides runtime executing twins our machinery. Experimental findings underscore efficacy significantly improving traditional notably terms reducing food costs requirements.
Language: Английский
Citations
5
Lecture notes on data engineering and communications technologies, Journal Year: 2025, Volume and Issue: unknown, P. 111 - 120
Published: Jan. 1, 2025
Language: Английский
Citations
0
Frontiers in Marine Science, Journal Year: 2025, Volume and Issue: 12
Published: April 15, 2025
In the context of growing demand for sustainable development and conservation fish stocks, artificial intelligence (AI) technologies are essential supporting scientific stock management. Artificial technology provides an effective solution intelligent recognition information. This study used bibliometric analysis to review a sample 719 articles from WoSCC (Web Science Core Collection) database 2014-2024. The results revealed significant increase in number publications 2014-2024, with mainly China, USA (the United States) other developed countries. top three impactful journals Ecological Informatics, Computers Electronics Agriculture ICES Journal Marine Science. most frequent keyword co-occurrence was deep learning, best clustering effect computer vision. findings indicate that this evaluation holistic visualization research frontier AI information identification, our underscore global importance identification highlight publication trends, hotspots, future directions area. conclusion, provide valuable insights into emerging frontiers AI-based identification.
Language: Английский
Citations
0
Nonlinear Engineering, Journal Year: 2025, Volume and Issue: 14(1)
Published: Jan. 1, 2025
Abstract The traditional fault diagnosis of agricultural sprinkler irrigation machinery and equipment has the disadvantages low accuracy time-consuming. To solve these problems, study designs a machine vision (MV)-based model for equipment. first investigates MV in diagnosis, then constructs using improved convolutional neural network, finally compares it with other methods to verify performance model. results showed that Iris dataset, proposed algorithm was 95.13%, training 0.95, recall rate 89.7%, which were better than comparison methods. In mechanical highest could reach 98.45%. This indicates constructed higher efficiency equipment, provides convenience maintenance repair.
Language: Английский
Citations
0