Automatic live fingerlings counting using computer vision

Albuquerque, Pedro Lucas França; Garcia, Vanir; Oliveira, Adair da Silva; Lewandowski, Tiago; Detweiler, Carrick; Gonçalves, Ariadne Barbosa; Costa, Celso Soares; Naka, Marco Hiroshi; Pistori, Hemerson

doi:10.1016/j.compag.2019.105015

Cited by 49 publications

(26 citation statements)

References 9 publications

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“…It is essential to continuously observe fish parameters such as abundance, quantity, size and weight when managing a fish farm (França Albuquerque et al . 2019). Quantitative estimation of fish biomass forms the basis of scientific fishery management and conservation strategies for sustainable fish production (Zion 2012; Lorenzen et al .…”

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

210

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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.

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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

210

View full text Add to dashboard Cite

show abstract

“…The technologies include artificial neural networks, machine learning, and sample comparison techniques. (18)(19)(20)(21)(22)…”

Section: Related Workmentioning

confidence: 99%

Portable Device for Ornamental Shrimp Counting Using Unsupervised Machine Learning

Yeh¹,

Ling²

2021

Sensors and Materials

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With the rapid development of emerging technologies, intelligent agriculture is incorporating techniques such as the Internet of Things, big data, cloud computing, artificial intelligence, blockchains, and fifth-generation mobile communication to improve work efficiency, prevent various disasters, and change the sales mode of agricultural products. Ornamental fishery is a part of agriculture and accounts for a significant proportion of commercial trade. This paper introduces image processing technology to help ornamental fisheries calculate the number of shrimps quickly. To solve the problem of overlapping live shrimps when counting, K-means unsupervised machine learning is adopted to determine the area of one shrimp. In addition, the proposed method using unsupervised machine learning is able to count different types of shrimp with high accuracy, such as crystal red shrimps, fire red shrimps, and Takashi Amano shrimps.We also analyze two background subtraction techniques, hue/saturation/value (HSV) histogrambased detection and Sobel edge detection, to compare the accuracy and calculation time of this application.

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“…Supervised learning is commonly used for classification and regression, where using data as a sample after trained by machine learning model which have the same target values [21]. From the theory of machine learning as well as its advantages, there are several implements in aquaculture recently such as biomass fish detection [22], size estimates [23][24][25], weight estimates [26][27][28], count [29][30][31][32], fish recognition [33][34][35][36][37][38], age detection [39,40], sex identification [34,[41][42][43], fish species classification [44][45][46][47][48][49][50], feeding behavior [51,52], group behavior [53], abnormal behavior [54,55], univariate prediction [38,[56][57][58][59], multivariate prediction [60][61][62], with the high accuracy rate.…”

Section: Introductionmentioning

confidence: 99%

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

Kim

2021

Electronics

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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.

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Automatic live fingerlings counting using computer vision

Cited by 49 publications

References 9 publications

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

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

Portable Device for Ornamental Shrimp Counting Using Unsupervised Machine Learning

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

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