Length–Weight Relationships of the Prized Sea Cucumber Holothuria lessoni from In Situ and Ex Situ Measurements DOI Creative Commons

Lea A. F. Djenidi,

Steven W. Purcell, Aaron W. Thornton

et al.

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(12), P. 2283 - 2283

Published: Dec. 12, 2024

Fisheries science draws on morphometric data for stock assessments. Length–weight relationships are essential estimating body weight from length measurements taken either underwater (in situ) or out of the water (ex situ). We examined models high-valued sea cucumber, Holothuria lessoni. From 77 specimens captured in 2024 (mean ± SD: 1774 372 g), we measured and width situ ex situ, then weighed animals situ. compared using four biometric parameters. The fitted were more statistically significant (p < 0.001) when to measurements. length–weight relationship our study was with those two previous studies same species at location. Each generated significantly different relationships. These findings suggest that should be re-evaluated regular intervals, as they may evolve over time. Our indicates estimation (and width) must rely established corresponding whether made results could provide reliable certain holothuroids.

Language: Английский

100 Years of Penaeid Domestication and Meta-Analysis of Breeding Traits DOI
Shengjie Ren, José M. Yáñez, Ricardo Pérez-Enríquez

et al.

Reviews in Fisheries Science & Aquaculture, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 20

Published: May 7, 2025

Language: Английский

Citations

0
A deep learning model for estimating body weight of live pacific white shrimp in a clay pond shrimp aquaculture DOI Creative Commons
Nitthita Chirdchoo, Suvimol Mukviboonchai,

Weerasak Cheunta

et al.

Intelligent Systems with Applications, Journal Year: 2024, Volume and Issue: 24, P. 200434 - 200434

Published: Sept. 1, 2024

This paper presents a novel approach to address the crucial challenge of accurately determining total weight shrimp within aquaculture ponds. Precise estimation is in mitigating issues overfeeding and underfeeding, thus enhancing efficiency productivity farming. The proposed system leverages image processing techniques detect individual live extract pertinent features for clay pond environment. Specifically, an automated feed tray captures images shrimp, which are then processed using combination Detectron2, PyTorch, CUDA (Compute Unified Device Architecture) detection. Essential such as area, perimeter, width, length, posture extracted through analysis, enabling accurate weight. An Artificial Neural Network (ANN) model, utilizing these features, predicts with coefficient determination (R2) 94.50% when incorporating all features. Furthermore, our integrates user-friendly web application that empowers farmers monitor trends, facilitating precision feeding strategies effective farm management. study contributes low-cost solution deep learning model estimate Pacific white ponds, daily calculations, helping optimize quantities, providing size distribution insights, reducing Feed Conversion Ratio (FCR) greater profitability. procedure feature extraction also provided, including calculation length well shimp classification.

Language: Английский

Citations

3
Shrimp phenotypic data extraction and growth abnormality identification method based on instance segmentation DOI

Xun Ran,

Yiran Liu,

Hongyu Pan

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 229, P. 109701 - 109701

Published: Dec. 2, 2024

Language: Английский

Citations

1
100 years domestication of penaeid shrimp and meta-analysis of breeding traits DOI
Shengjie Ren, José M. Yáñez, Ricardo Pérez-Enríquez

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: June 27, 2024

Abstract Penaeid shrimp farming plays a pivotal role in ensuring future food security and promoting economic sustainability. Compared to the extensive long history of domestication observed terrestrial agriculture species, selective breeding penaeids are relatively recent endeavors. Selective aimed at improving production traits holds significant promise for enhancing efficiency reducing environmental impact farming, thereby contributing its long-term Assessing genotype-by-environment (G-by-E) interactions is essential programs ensure that improved penaeid strains perform consistently across different environments, with genomic selection proving more effective than sib-testing alone mitigating sensitivity. Genome editing tools like CRISPR/Cas9 offer potential accelerate genetic gains by enabling rapid introduction desired changes, advancements showing promising results achieving high transfection embryos. Additionally, artificial intelligence machine learning being leveraged streamline phenotyping enhance decision-making accuracy managing predicting disease outbreaks. Herein, we provide an overview update on shrimp, including current status principal farmed key milestones history, targeted programs, advantages integrating genomeic traits, directions shrimp.

Language: Английский

Citations

0
Body weight prediction models of Macrognathus pancalus (Hamilton, 1822) from morphometric traits using principal component analysis DOI Open Access
Surendra Kumar Ahirwal, Jaspreet Singh,

Tarkeshwar Kumar

et al.

Indian Journal of Fisheries, Journal Year: 2024, Volume and Issue: 71(2)

Published: June 30, 2024

In the present study, an attempt was made to evaluate body weight of barred spiny eel based on morphologic traits. Ten morphometric characters and five meristic counts were measured for 38 specimens, ranging in standard length from 77.80 149.60 mm 2.02 17.89 g weight. All data sets standardised using z-transformationmethod. The Kaiser-Meyer-Olkin (KMO) test performed measure sample adequacy level, which found be 0.85. significance correlation matrix all traits tested with Bartlett’s sphericity, significant (χ2 = 793.360, df 105, p<0.01). Four fifteen principal components (PCs) explained around 85% total variation. first component (PC1) contributed 57.52% variation represented by positive high-loading factors pre-dorsal (PDL), pre-anal (PAL), (SL). second (PC2) 10.90% anal fin rays (AFR) dorsal (DFR). third fourth 9.26 7.41% showed high loading pectoral (PFR), caudal (CFR), respectively. estimated communalities ranged 0.658 eye diameter (ED) 0.978 (PDL). species’ predicted stepwise multiple regression interdependent four extracted PCs. Stepwise revealed that a combination three variables, such as depth (BD), post-orbital (POL), best predict species coefficient determinant value (r2 93). Therefore, study confirms is function variables rather than orthogonal variables. Keywords: Body measurements. Macrognathus pancalus, Meristic counts. Prediction model

Language: Английский

Citations

0
Length–Weight Relationships of the Prized Sea Cucumber Holothuria lessoni from In Situ and Ex Situ Measurements DOI Creative Commons

Lea A. F. Djenidi,

Steven W. Purcell, Aaron W. Thornton

et al.

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(12), P. 2283 - 2283

Published: Dec. 12, 2024

Fisheries science draws on morphometric data for stock assessments. Length–weight relationships are essential estimating body weight from length measurements taken either underwater (in situ) or out of the water (ex situ). We examined models high-valued sea cucumber, Holothuria lessoni. From 77 specimens captured in 2024 (mean ± SD: 1774 372 g), we measured and width situ ex situ, then weighed animals situ. compared using four biometric parameters. The fitted were more statistically significant (p < 0.001) when to measurements. length–weight relationship our study was with those two previous studies same species at location. Each generated significantly different relationships. These findings suggest that should be re-evaluated regular intervals, as they may evolve over time. Our indicates estimation (and width) must rely established corresponding whether made results could provide reliable certain holothuroids.

Language: Английский

Citations

0