Automatic Weight Estimation of Harvested Fish from Images

Konovalov, Dmitry A.; Saleh, Alzayat; Efremova, Dina B.; Domingos, Jose A.; Jerry, Dean R.

doi:10.48550/arxiv.1909.02710

Cited by 2 publications

(2 citation statements)

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“…Masked R-CNNs successfully located heads of European hake in images from which head size and thus subsequent overall fish length was estimated (Álvarez-Ellacuría, Palmer, Catalán, & Lisani, 2020). Finally, large-segmentation CNNs were able to successfully predict the weight of harvested Asian sea bass and barramundi (Konovalov, Saleh, Efremova, & Domingos, 2019).…”

Section: Existing Approaches To Obtain Biometric Data Of Fishmentioning

confidence: 98%

Applications of deep convolutional neural networks to predict length, circumference, and weight from mostly dewatered images of fish

Bravata

Kelly

Eickholt

et al. 2020

Ecology and Evolution

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Simple biometric data of fish aid fishery management tasks such as monitoring the structure of fish populations and regulating recreational harvest. While these data are foundational to fishery research and management, the collection of length and weight data through physical handling of the fish is challenging as it is time consuming for personnel and can be stressful for the fish. Recent advances in imaging technology and machine learning now offer alternatives for capturing biometric data. To investigate the potential of deep convolutional neural networks to predict biometric data, several regressors were trained and evaluated on data stemming from the FishL™ Recognition System and manual measurements of length, girth, and weight. The dataset consisted of 694 fish from 22 different species common to Laurentian Great Lakes. Even with such a diverse dataset and variety of presentations by the fish, the regressors proved to be robust and achieved competitive mean percent errors in the range of 5.5 to 7.6% for length and girth on an evaluation dataset. Potential applications of this work could increase the efficiency and accuracy of routine survey work by fishery professionals and provide a means for longer‐term automated collection of fish biometric data.

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Section: Existing Approaches To Obtain Biometric Data Of Fishmentioning

confidence: 98%

Applications of deep convolutional neural networks to predict length, circumference, and weight from mostly dewatered images of fish

Bravata

Kelly

Eickholt

et al. 2020

Ecology and Evolution

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show abstract

“…The problem of fish counting on a smaller scale is presented in [36][37][38][39][40]. The estimation of fish weight and its classification is studied in [41][42][43]. All these approaches are based only on information collected below the sea surface.…”

Section: Fish Population Modelingmentioning

confidence: 99%

Heterogeneous Autonomous Robotic System in Viticulture and Mariculture: Vehicles Development and Systems Integration

Kapetanović

Goričanec

Vatavuk

et al. 2022

Sensors

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There are activities in viticulture and mariculture that require extreme physical endurance from human workers, making them prime candidates for automation and robotization. This paper presents a novel, practical, heterogeneous, autonomous robotic system divided into two main parts, each dealing with respective scenarios in viticulture and mariculture. The robotic components and the subsystems that enable collaboration were developed as part of the ongoing HEKTOR project, and each specific scenario is presented. In viticulture, this includes vineyard surveillance, spraying and suckering with an all-terrain mobile manipulator (ATMM) and a lightweight autonomous aerial robot (LAAR) that can be used in very steep vineyards where other mechanization fails. In mariculture, scenarios include coordinated aerial and subsurface monitoring of fish net pens using the LAAR, an autonomous surface vehicle (ASV), and a remotely operated underwater vehicle (ROV). All robotic components communicate and coordinate their actions through the Robot Operating System (ROS). Field tests demonstrate the great capabilities of the HEKTOR system for the fully autonomous execution of very strenuous and hazardous work in viticulture and mariculture, while meeting the necessary conditions for the required quality and quantity of the work performed.

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Automatic Weight Estimation of Harvested Fish from Images

Cited by 2 publications

References 0 publications

Applications of deep convolutional neural networks to predict length, circumference, and weight from mostly dewatered images of fish

Applications of deep convolutional neural networks to predict length, circumference, and weight from mostly dewatered images of fish

Heterogeneous Autonomous Robotic System in Viticulture and Mariculture: Vehicles Development and Systems Integration

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