Automatic Fish Population Counting by Machine Vision and a Hybrid Deep Neural Network Model

Zhang, Song; Yang, Xinting; Wang, Yizhong; Zhao, Zhenxi; Liu, Jintao; Liu, Yang; Sun, Cheng; Zhou, Chao

doi:10.3390/ani10020364

Cited by 79 publications

(34 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To achieve automatic counting of fish groups under high density and frequent occlusion characteristics, a fish distribution map can be constructed using DL; then, the fish distribution, density and quantity can be obtained. These values can indirectly reflect fish conditions such as starvation, abnormalities and other states, thereby providing an important reference for feeding or harvest decisions (Zhang et al 2020).…”

Section: Behavioural Analysismentioning

confidence: 99%

See 1 more Smart Citation

Deep learning for smart fish farming: applications, opportunities and challenges

Yang

Zhang

Liu

et al. 2020

Reviews in Aquaculture

Self Cite

213

View full text Add to dashboard Cite

The rapid emergence of deep learning (DL) technology has resulted in its successful use in various fields, including aquaculture. DL creates both new opportunities and a series of challenges for information and data processing in smart fish farming. This paper focuses on applications of DL in aquaculture, including live fish identification, species classification, behavioural analysis, feeding decisions, size or biomass estimation, and water quality prediction. The technical details of DL methods applied to smart fish farming are also analysed, including data, algorithms and performance. The review results show that the most significant contribution of DL is its ability to automatically extract features. However, challenges still exist; DL is still in a weak artificial intelligence stage and requires large amounts of labelled data for training, which has become a bottleneck that restricts further DL applications in aquaculture. Nevertheless, DL still offers breakthroughs for addressing complex data in aquaculture. In brief, our purpose is to provide researchers and practitioners with a better understanding of the current state of the art of DL in aquaculture, which can provide strong support for implementing smart fish farming applications.

show abstract

Section: Behavioural Analysismentioning

confidence: 99%

“…These values can indirectly reflect fish conditions such as starvation, abnormalities and other states, thereby providing an important reference for feeding or harvest decisions (Zhang et al . 2020).…”

Section: Applications Of Deep Learning In Smart Fish Farmingmentioning

confidence: 99%

Deep learning for smart fish farming: applications, opportunities and challenges

Yang

Zhang

Liu

et al. 2020

Reviews in Aquaculture

Self Cite

213

View full text Add to dashboard Cite

show abstract

“…In terms of quantity, traditional artificial sampling approaches have proven to be time-consuming and complex, and strenuous on fish. Zhang et al (2020) proposed an automated fish counting method based on resolving the aforementioned issues and providing real-time, accurate and lossless counting of fish populations in far offshore salmon mariculture. They constructed a hybrid neural network model; A multi-column convolution neural network used as a front end for capturing feature information of various receptive fields.…”

Section: Samplingmentioning

confidence: 99%

Sustainable aquaculture development: a review on the roles of cloud computing, internet of things and artificial intelligence (CIA)

Mustapha

Alhassan

Jiang

et al. 2021

Reviews in Aquaculture

109

View full text Add to dashboard Cite

Each year, there is a significant rise in demand for global food production due to population increase and a rise in demand for protein food sources. This puts pressure on capture fishery as fish is a preferred protein source worldwide. However, the more resources we put into the capture fisheries to obtain maximum catch, the faster the fisheries stock becomes depleted. The best option left to produce enough fish to meet demand is relying on advanced aquaculture. Unfortunately, the impact of technological advancement in the aquaculture sector is not profound compared to the agricultural and manufacturing sectors. The advent of Cloud computing, Internet of Things, and Artificial Intelligence (CIA) has expanded numerous possibilities for applying and integrating information technology in all works of life. This article reviews the emergence of research development on CIA and the potential to revolutionize the aquaculture industry. The use of CIA techniques and tools such as drones, nano and micro-sensors, bionic robots, remote cameras, intelligent sorting, energy-saving processing equipment, statistical modules, and algorithms will reduce human intervention and increase aquaculture productivity. Also, the application of CIA in the aquaculture value chain to ensure effectiveness in traceability, feeding, disease detection, growth prediction, environmental monitoring, market information, and others is key to increasing aquaculture productivity and sustainability. Therefore, the future of aquaculture operations with less human labour, effective maintenance, and resource utilization largely depend on innovative technologies. Here, we outlined the need for adopting innovative technologies and the limiting factors that hinge on CIA adoption in the aquaculture industry.

show abstract

“…Zhang et al [99] proposed a model to count the number of fish automatically instead of the traditional artificial sampling method. The goal of this system is to counting number of fish in cages cultured in an offshore environment.…”

Section: Countingmentioning

confidence: 99%

Overview of Smart Aquaculture System: Focusing on Applications of Machine Learning and Computer Vision

Kim

2021

Electronics

View full text Add to dashboard Cite

Smart aquaculture is nowadays one of the sustainable development trends for the aquaculture industry in intelligence and automation. Modern intelligent technologies have brought huge benefits to many fields including aquaculture to reduce labor, enhance aquaculture production, and be friendly to the environment. Machine learning is a subdivision of artificial intelligence (AI) by using trained algorithm models to recognize and learn traits from the data it watches. To date, there are several studies about applications of machine learning for smart aquaculture including measuring size, weight, grading, disease detection, and species classification. This review provides and overview of the development of smart aquaculture and intelligent technology. We summarized and collected 100 articles about machine learning in smart aquaculture from nearly 10 years about the methodology, results as well as the recent technology that should be used for development of smart aquaculture. We hope that this review will give readers interested in this field useful information.

show abstract

Automatic Fish Population Counting by Machine Vision and a Hybrid Deep Neural Network Model

Cited by 79 publications

References 38 publications

Deep learning for smart fish farming: applications, opportunities and challenges

Deep learning for smart fish farming: applications, opportunities and challenges

Sustainable aquaculture development: a review on the roles of cloud computing, internet of things and artificial intelligence (CIA)

Overview of Smart Aquaculture System: Focusing on Applications of Machine Learning and Computer Vision

Contact Info

Product

Resources

About