Automating egg damage detection for improved quality control in the food industry using deep learning

Journal of Food Science, Год журнала: 2025, Номер 90(1)

Опубликована: Янв. 1, 2025

Abstract The detection and classification of damage to eggs within the egg industry are paramount importance for production healthy eggs. This study focuses on automatic identification cracks surface in chicken using deep learning algorithms. goal is enhance quality control food by accurately identifying with physical damage, such as cracks, fractures, or other defects, which could compromise their quality. A total 794 images were used study, comprising two different classes: damaged not (intact) Four models based convolutional neural networks employed: GoogLeNet, Visual Geometry Group (VGG)‐19, MobileNet‐v2, residual network (ResNet)‐50. GoogLeNet achieved a accuracy 98.73%, VGG‐19 97.45%, MobileNet‐v2 97.47%, ResNet‐50 96.84%. According results, model performed highest rate (98.73%). encompasses artificial intelligence techniques damage. early accurate interventions highlights significant these technologies industry. approach provides producers ability detect more quickly accurately, thereby minimizing product losses through timely intervention. Additionally, use offers efficient means classifying compared traditional methods.

Язык: Английский

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Quality prediction of seabream (SPARUS AURATA) by DEEP learning algorithms and explainable artificial intelligence

Food Chemistry, Год журнала: 2025, Номер 474, С. 143150 - 143150

Опубликована: Янв. 31, 2025

Язык: Английский

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Segmentation of dry bean (Phaseolus vulgaris L.) leaf disease images with U-Net and classification using deep learning algorithms

European Food Research and Technology, Год журнала: 2023, Номер 249(10), С. 2543 - 2558

Опубликована: Июнь 26, 2023

Язык: Английский

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Fisheye freshness detection using common deep learning algorithms and machine learning methods with a developed mobile application

European Food Research and Technology, Год журнала: 2024, Номер 250(7), С. 1919 - 1932

Опубликована: Апрель 18, 2024

Abstract Fish is commonly ingested as a source of protein and essential nutrients for humans. To fully benefit from the proteins substances in fish it crucial to ensure its freshness. If stored an extended period, freshness deteriorates. Determining can be done by examining eyes, smell, skin, gills. In this study, artificial intelligence techniques are employed assess The author’s objective evaluate analyzing eye characteristics. achieve this, we have developed combination deep machine learning models that accurately classify fish. Furthermore, application utilizes both learning, instantly detect any given sample was created. Two algorithms (SqueezeNet, VGG19) were implemented extract features image data. Additionally, five levels samples applied. Machine include (k-NN, RF, SVM, LR, ANN). Based on results, inferred employing VGG19 model feature selection conjunction with Artificial Neural Network (ANN) classification yields most favorable success rate 77.3% FFE dataset. Graphical

Язык: Английский

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Enhancing fish freshness prediction using NasNet-LSTM

Journal of Food Composition and Analysis, Год журнала: 2023, Номер 127, С. 105945 - 105945

Опубликована: Дек. 23, 2023

Язык: Английский

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Overview of aquaculture Artificial Intelligence (AAI) applications: enhance sustainability and productivity, reduce labor costs, and increase the quality of aquatic products

Annals of Animal Science, Год журнала: 2024, Номер unknown

Опубликована: Авг. 29, 2024

Abstract The current work investigates the prospective applications of Artificial Intelligence (AI) in aquaculture industry. AI depends on collecting, validating, and analyzing data from several aspects using sensor readings, feeding sheets. is an essential tool that can monitor fish behavior increase resilience quality seafood products. Furthermore, algorithms early detect potential pathogen infections disease outbreaks, allowing stakeholders to take timely preventive measures subsequently make proper decision appropriate time. predict ecological conditions should help farmers adopt strategies plans avoid negative impacts farms create easy safe environment for production. In addition, aids analyze collect regarding nutritional requirements, nutrient availability, price could adjust modify their diets optimize feed formulations. Thus, reduce labor costs, aquatic animal’s growth, health, formulation waste output detection outbreaks. Overall, this review highlights importance achieve sustainability boost net profits

Язык: Английский

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Comparative analysis of machine learning and deep learning algorithms for knee arthritis detection using YOLOv8 models

Journal of X-Ray Science and Technology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 26, 2025

Knee arthritis is a prevalent joint condition that affects many people worldwide. Early detection and appropriate treatment are essential to slow the disease's progression enhance patients’ quality of life. In this study, various machine learning deep algorithms were used detect knee arthritis. The models included k-NN, SVM, GBM, while DenseNet, EfficientNet, InceptionV3 as models. Additionally, YOLOv8 classification (YOLOv8n-cls, YOLOv8s-cls, YOLOv8m-cls, YOLOv8l-cls, YOLOv8x-cls) employed. “Annotated Dataset for Arthritis Detection” with five classes (Normal, Doubtful, Mild, Moderate, Severe) 1650 images divided into 80% training, 10% validation, testing using Hold-Out method. outperformed both algorithms. GBM achieved success rates 63.61%, 64.14%, 67.36%, respectively. Among models, 62.35%, 70.59%, 79.41%. highest was seen in YOLOv8x-cls model at 86.96%, followed by YOLOv8l-cls 86.79%, YOLOv8m-cls 83.65%, YOLOv8s-cls 80.37%, YOLOv8n-cls 77.91%.

Язык: Английский

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Applications of Machine Learning in Food Safety and HACCP Monitoring of Animal-Source Foods

Foods, Год журнала: 2025, Номер 14(6), С. 922 - 922

Опубликована: Март 8, 2025

Integrating advanced computing techniques into food safety management has attracted significant attention recently. Machine learning (ML) algorithms offer innovative solutions for Hazard Analysis Critical Control Point (HACCP) monitoring by providing data analysis capabilities and have proven to be powerful tools assessing the of Animal-Source Foods (ASFs). Studies that link ML with HACCP in ASFs are limited. The present review provides an overview ML, feature extraction, selection employed safety. Several non-destructive presented, including spectroscopic methods, smartphone-based sensors, paper chromogenic arrays, machine vision, hyperspectral imaging combined algorithms. Prospects include enhancing predictive models development hybrid Artificial Intelligence (AI) automation quality control processes using AI-driven computer which could revolutionize inspections. However, handling conceivable inclinations AI is vital guaranteeing reasonable exact hazard assessments assortment nourishment generation settings. Moreover, moving forward, interpretability will make them more straightforward dependable. Conclusively, applying allows real-time analytics can significantly reduce risks associated ASF consumption.

Язык: Английский

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Prediction method of large yellow croaker (Larimichthys crocea) freshness based on improved residual neural network

Journal of Food Measurement & Characterization, Год журнала: 2024, Номер 18(4), С. 2995 - 3007

Опубликована: Март 6, 2024

Язык: Английский

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Deep Learning-Based Classification of Black Gram Plant Leaf Diseases: A Comparative Study

Proceedings of the International Conference on Advanced Technologies, Год журнала: 2023, Номер unknown

Опубликована: Авг. 19, 2023

The escalating incidence of plant diseases presents considerable obstacles to the agricultural domain, resulting in substantial reductions crop yield and posing a threat food security. To address pressing concern Black Gram Plant Leaf Diseases (BPLD), this research endeavors tackle disease classification through application deep learning methodology. approach leverages comprehensive dataset that encompasses Anthracnose, Crinkle, Powdery Mildew, Yellow Mosaic diseases, all which affect black gram crop. By employing advanced technique, we aim contribute valuable insights combat BPLD effectively. Our applies models, including Darknet-53, ResNet-101, GoogLeNet, EfficientNet-B0, classify diseases. Darknet-53 achieved 98.51% accuracy, followed by ResNet-101 (97.51%), GoogLeNet (96.52%), EfficientNet-B0 (77.61%). These findings demonstrate potential for accurate identification, benefiting agriculture. study provides comparative analysis models Disease (BPLD) classification, revealing as superior performers. Implementing these real-world scenarios holds promise early detection intervention, reducing losses. high accuracy signifies significant progress automating recognition, sector.

Язык: Английский

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