Cited by Enhanced Detection of Aspergillus Flavus in Peanut Kernels Using a Multi-Scale Attention Transformer (Msat): Advancements in Food Safety and Contamination Analysis

Enhanced detection of Aspergillus flavus in peanut kernels using a multi-scale attention transformer (MSAT): Advancements in food safety and contamination analysis DOI

Zhen Guo,

Jing Zhang, Haifang Wang

et al.

International Journal of Food Microbiology, Journal Year: 2024, Volume and Issue: 423, P. 110831 - 110831

Published: July 20, 2024

Language: Английский

Citations

Recent Advances in Detection and Control Strategies for Foodborne Bacteria in Raw and Ready‐to‐Eat Fruits and Vegetables DOI

Asem Mahmoud Abdelshafy, Hala A. Younis, Ahmed I. Osman

et al.

Food Frontiers, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 26, 2025

ABSTRACT The prevalence of foodborne outbreaks due to the consumption uncooked and ready‐to‐eat fruits vegetables has seen a noticeable increase, particularly in environments lacking sanitation. This article extensively explores recent advancements detection pathogens vegetables, alongside potential prevention strategies. Predominantly, like Listeria monocytogenes , Escherichia coli Salmonella enterica are main culprits linked these food items globally. Notably, contamination is more prevalent fresh leafy greens than fruit products. Various methods such as culturing, microscopy, immunological assays, polymerase chain reaction (PCR), biosensors, hyperspectral imaging have proven effective identifying foods. Nonetheless, come with challenges, including time consumption, accuracy concerns, high costs. Research ongoing refine techniques, efforts combining methodologies PCR–enzyme‐linked immunosorbent assay integrating culturing PCR. Additionally, several interventions, cold plasma treatment, ultraviolet irradiation, application edible coatings, shown promise mitigating risks, thereby enhancing safety produce items.

Language: Английский

Citations

Early detection of rice blast using UAV hyperspectral imagery and multi-scale integrator selection attention transformer network (MS-STNet) DOI

Tan Liu, Qi Yuan, Fan Yang

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 231, P. 110007 - 110007

Published: Feb. 7, 2025

Language: Английский

Citations

DiffusionAAE: Enhancing hyperspectral image classification with conditional diffusion model and Adversarial Autoencoder DOI

Zeyu Cao, Jinhui Li,

Xiangrui Xu

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103118 - 103118

Published: April 1, 2025

Language: Английский

Citations

Efficient tuna detection and counting with improved YOLOv8 and ByteTrack in pelagic fisheries DOI

Yuanchen Cheng, Zichen Zhang, Yuqing Liu

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103116 - 103116

Published: April 1, 2025

Language: Английский

Citations

Non-destructive geographical traceability of American ginseng using near-infrared spectroscopy combined with a novel deep learning model DOI

Yang Yu, Siqi Wang, Qibing Zhu

et al.

Journal of Food Composition and Analysis, Journal Year: 2024, Volume and Issue: unknown, P. 106736 - 106736

Published: Sept. 1, 2024

Language: Английский

Citations

Assessing water quality environmental grades using hyperspectral images and a deep learning model: A case study in Jiangsu, China DOI

Hongran Li,

Hui Zhao,

Chao Wei

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 84, P. 102854 - 102854

Published: Oct. 16, 2024

Language: Английский

Citations

Deep learning for plant stress detection: A comprehensive review of technologies, challenges, and future directions DOI

Nijhum Paul,

G C Sunil,

David J. Horvath

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 229, P. 109734 - 109734

Published: Dec. 13, 2024

Language: Английский

Citations

ECVNet: A Fusion Network of Efficient Convolutional Neural Networks and Visual Transformers for Tomato Leaf Disease Identification DOI

Fendong Zou,

Jing Hua, Yuanhao Zhu

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(12), P. 2985 - 2985

Published: Dec. 15, 2024

Tomato leaf diseases pose a significant threat to plant growth and productivity, necessitating the accurate identification timely management of these issues. Existing models for tomato disease recognition can primarily be categorized into Convolutional Neural Networks (CNNs) Visual Transformers (VTs). While CNNs excel in local feature extraction, they struggle with global recognition; conversely, VTs are advantageous extraction but less effective at capturing features. This discrepancy hampers performance improvement both model types task identification. Currently, fusion that combine still relatively scarce. We developed an efficient network named ECVNet recognition. Specifically, we first designed Channel Attention Residual module (CAR module) focus on channel features enhance model’s sensitivity importance channels. Next, created Fusion (CAF effectively extract integrate features, thereby improving spatial capabilities. conducted extensive experiments using Plant Village dataset AI Challenger 2018 dataset, achieving state-of-the-art cases. Under condition 100 epochs, achieved accuracy 98.88% 86.04% dataset. The introduction provides solution diseases.

Language: Английский

Citations

Advancements and outlooks in utilizing Convolutional Neural Networks for plant disease severity assessment: A comprehensive review DOI

Douglas Squizatto Leite, Alisson V. Brito,

Gregorio Faccioli

et al.

Smart Agricultural Technology, Journal Year: 2024, Volume and Issue: 9, P. 100573 - 100573

Published: Sept. 27, 2024

Language: Английский

Citations