Innovations in Seafood Freshness Quality: Non-Destructive Detection of Freshness in Litopenaeus Vannamei Using the Yolo-Shrimp Model DOI
Mingxin Hou,

Xiaowen Zhong,

Zheng Ouyang

et al.

Published: Jan. 1, 2024

In this work, changes in the quality of Litopenaeus vannamei stored at 4 °C for one week are investigated, focusing on biochemical markers such as total volatile basic nitrogen (TVB-N), viable count (TVC), and degree melanosis, along with their correlations. Additionally, YOLO-Shrimp model, an advanced version YOLOv8 architecture incorporating focal EIOU loss function C3X computation module, is introduced. These enhancements significantly improve precision adaptability model assessing shrimp freshness. The employs a non-destructive, rapid assessment method by analyzing texture, color morphological attributes. Compared to YOLOv8, performance improvements were observed (5.07%), recall (1.58%), F1 score (3.25%), mAP50 (2.84%). Empirical validations confirmed that model's assessments align biochemical, microbiological, physical indicators, highlighting its effectiveness detecting freshness potential enhance food safety control standards.

Language: Английский

Classification of Different Plant Species Using Deep Learning and Machine Learning Algorithms DOI
Siddharth Singh Chouhan, Uday Pratap Singh, Utkarsh Sharma

et al.

Wireless Personal Communications, Journal Year: 2024, Volume and Issue: 136(4), P. 2275 - 2298

Published: June 1, 2024

Language: Английский

Citations

14
BiFPN-enhanced SwinDAT-based cherry variety classification with YOLOv8 DOI Creative Commons
Merve Varol Arısoy, İlhan Uysal

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 13, 2025

Accurate classification of cherry varieties is crucial for their economic value and market differentiation, yet genetic diversity visual similarity make manual identification challenging, hindering efficient agricultural trade practices. This study addresses this issue by proposing a novel deep learning-based hybrid model that integrates BiFPN with the YOLOv8n-cls framework, enhanced Swin Transformer Deformable Attention (DAT) techniques. The was trained evaluated on newly constructed dataset comprising from Turkey's Western Mediterranean region. Experimental results demonstrated effectiveness proposed approach, achieving precision 91.91%, recall 92.0%, F1-score 91.93%, an overall accuracy 91.714%. findings highlight model's potential to optimize harvest timing, ensure quality control, support export classification, thereby contributing improved practices outcomes.

Language: Английский

Citations

0
Innovations in seafood freshness quality: Non-destructive detection of freshness in Litopenaeus vannamei using the YOLO-shrimp model DOI
Mingxin Hou,

Xiaowen Zhong,

Zheng Ouyang

et al.

Food Chemistry, Journal Year: 2024, Volume and Issue: 463, P. 141192 - 141192

Published: Sept. 7, 2024

Language: Английский

Citations

2
Innovations in Seafood Freshness Quality: Non-Destructive Detection of Freshness in Litopenaeus Vannamei Using the Yolo-Shrimp Model DOI
Mingxin Hou,

Xiaowen Zhong,

Zheng Ouyang

et al.

Published: Jan. 1, 2024

In this work, changes in the quality of Litopenaeus vannamei stored at 4 °C for one week are investigated, focusing on biochemical markers such as total volatile basic nitrogen (TVB-N), viable count (TVC), and degree melanosis, along with their correlations. Additionally, YOLO-Shrimp model, an advanced version YOLOv8 architecture incorporating focal EIOU loss function C3X computation module, is introduced. These enhancements significantly improve precision adaptability model assessing shrimp freshness. The employs a non-destructive, rapid assessment method by analyzing texture, color morphological attributes. Compared to YOLOv8, performance improvements were observed (5.07%), recall (1.58%), F1 score (3.25%), mAP50 (2.84%). Empirical validations confirmed that model's assessments align biochemical, microbiological, physical indicators, highlighting its effectiveness detecting freshness potential enhance food safety control standards.

Language: Английский

Citations

1