Improving effort estimates and informing temporal distribution of recreational salmon fishing in British Columbia, Canada using high-frequency optical imagery data

Morrow, Benjamin D.; O’Hara, Patrick D.; Ban, Natalie C.; Marques, Tunai Porto; Fraser, Molly; Serra-Sogas, Norma; Bone, Christopher

doi:10.1016/j.fishres.2022.106251

Cited by 5 publications

(5 citation statements)

References 54 publications

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“…We found several studies providing evidence of positive bias among catch rate estimations derived during roving surveys compared to access point surveys (Keefe et al 2009;McCormick et al 2012;Alexiades et al 2015). The nonstationary nature of catch rates (Hoenig et al 1997;Pollock et al 1997) and angler behaviors (Smallwood et al 2012;Morrow et al 2022) can influence creel estimations when using incomplete trips. Anglers that experience higher catch rates are more likely to fish longer; thus, anglers that fish longer are also more likely to be intercepted during roving surveys (Pollock et al 1994), which can inflate incomplete trip estimates.…”

Section: Discussionmentioning

confidence: 88%

“…2012; Morrow et al. 2022) can influence creel estimations when using incomplete trips. Anglers that experience higher catch rates are more likely to fish longer; thus, anglers that fish longer are also more likely to be intercepted during roving surveys (Pollock et al.…”

Section: Discussionmentioning

confidence: 99%

“…The EPH effect indicates the partial effect that longitude and latitude had on black bass targeted effort (angler-hours) per hectare. g.,Heermann et al 2013;Feiner et al 2020;Jensen et al 2022;Morrow et al 2022;Sass et al 2023). Although catch rates in the spring putatively increase in relation to black bass spawning behaviors(Suski and Philipp 2004;…”

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confidence: 99%

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Seasonal, spatial, and water level predictors of angler catch and effort within Texas black bass reservoir fisheries

Smith,

Schlechte,

Myers

et al. 2024

N American J Fish Manag

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We applied generalized additive models to analyze creel data collected at 53 reservoirs between 2003 and 2021 to evaluate seasonal, spatial, and environmental predictors of angler catch rate and effort density (i.e., angler hrs/ha) within Texas black bass fisheries. Season significantly affected models with strong positive effects on catch rates during the spring and fall and strong positive effects on effort densities during the spring. Quarterly (i.e., three months) fluctuations in water level were identified as environmental drivers of catch rates and effort density during the springtime. Percent full (i.e., relative surface area to full pool) was also identified as a significant predictor with increasing effort densities and catch rates at higher levels of percent full. Our study provides a comparative baseline for future evaluations of black bass fisheries in Texas and a depiction of key factors influencing black bass angler catch rates and effort densities using a modeling approach that may be applied within a variety of fisheries research applications.

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Section: Discussionmentioning

confidence: 88%

Section: Discussionmentioning

confidence: 99%

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Seasonal, spatial, and water level predictors of angler catch and effort within Texas black bass reservoir fisheries

Smith,

Schlechte,

Myers

et al. 2024

N American J Fish Manag

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“…Given the limited resolution of a digital camera, some fishes might be constrained to a lower number of pixels, making it harder to detect by a deep learning classification model (64). Therefore, there is a requirement for a method to improve the resolution of these images for the classification model to work better (50). For this purpose, a form of deep learning technique called Enhanced Super Resolution Generative Adversarial Network (ESRGAN) is used, which specializes in improving the image resolution of an image from lower quality (78).…”

Section: Introductionmentioning

confidence: 99%

Fish Species Identification on Low Resolution - A Study with Enhanced Super Resolution Generative Adversarial Network (ESRGAN), YOLO and VGG-16

Adhikary,

Banerjee,

Singh

et al. 2024

Preprint

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An intelligent detection and recognition model for the fish species from camera footage is urgently required as fishery contributes to a large portion of the world economy, and these kinds of advanced models can aid fishermen on a large scale. Such models incorporating a pick-and-place machine can be beneficial to sorting different fish species in bulk without human intervention, significantly reducing costs for large-scale fishing industries. Existing methods for detecting and recognizing fish species have many limitations, such as limited scalability, detection accuracy, failure to detect multiple species, degraded performance at a lower resolution, or pinpointing the exact location of the fish. Modifying the head of a compelling deep learning model, namely VGG-16, with pre-trained weights, can be used to detect both the species of the fish and find the exact location of the fish in an image by implementing a modified YOLO to incorporate the bounding box regression head. We have proposed using the ESRGAN algorithm and the proposed neural network to amplify the image resolution by a factor of 4. With this method, an overall detection accuracy of 96.5% has been obtained. The experiment has been conducted based on a total of 9460 images spread across 9 species. After further improving the model, a pick-and-place machine could be integrated to quickly sort the fish according to their species in different large-scale fish industries.

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“…In a practical scenario, the model might need to detect fish species from a large number of fishes captured within a single frame of a camera, and given a limited resolution of a digital camera, some fishes might be constrained to a lower number of pixels making it harder to detect by a deep learning classification model [6]. Therefore, there is a requirement for a method to improve the resolution of these images for the classification model to work better [7]. For this purpose, a form of deep learning technique called Enhanced Super Resolution Generative Adversarial Network (ESRGAN) is used which specializes in improving the image resolution of an image from lower quality [8].…”

Section: Introductionmentioning

confidence: 99%

Fish Species Identification on Low Resolution - A Study with Enhanced Super Resolution Generative Adversarial Network (ESRGAN), YOLO and VGG-16

Adhikary¹

2022

Preprint

View full text Add to dashboard Cite

A smart detection and recognition model for the species of a fish from camera footage is of urgent requirement as fishery contributes to a large portion of the world economy and these kinds of advanced models can aid fishermen on a large scale. Such models incorporating a pick-and-place machine can be very useful to sort different fish species in a bulk without human intervention and this can greatly reduce costs for large scale fishing industries. Existing methods for detecting and recognizing fish species have many limitations such as limited scalability, detection accuracy, failing to detect multiple species, degraded performance at a lower resolution, or pinpointing the exact location of the fish. By modifying the head of a very powerful deep learning model namely VGG-16 with pre-trained weights can be used to detect both the species of the fish and find the exact location of the fish in an image by implementing a modified YOLO to incorporate the bounding box regression head. We have proposed the usage of the ESRGAN algorithm along with the proposed neural network to amplify the image resolution by a factor of 4. With this method, the overall detection accuracy of 96.5% has been obtained. The experiment has been conducted based on a total of 9460 images spread across 9 species. After further improving the model, a pick-and-place machine could be integrated for very fast sorting of the fish according to their species in different large-scale fish industries.

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Improving effort estimates and informing temporal distribution of recreational salmon fishing in British Columbia, Canada using high-frequency optical imagery data

Cited by 5 publications

References 54 publications

Seasonal, spatial, and water level predictors of angler catch and effort within Texas black bass reservoir fisheries

Seasonal, spatial, and water level predictors of angler catch and effort within Texas black bass reservoir fisheries

Fish Species Identification on Low Resolution - A Study with Enhanced Super Resolution Generative Adversarial Network (ESRGAN), YOLO and VGG-16

Fish Species Identification on Low Resolution - A Study with Enhanced Super Resolution Generative Adversarial Network (ESRGAN), YOLO and VGG-16

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