Using Deep Learning Neural Network in Artificial Intelligence Technology to Classify Beef Cuts

Gc, Sunil; Borhan, Saidul; Zhang, Yu; Reed, DeMetris D.; Ahsan, Mostofa; Berg, E. P.; Sun, Xiaorui

doi:10.3389/fsens.2021.654357

Cited by 18 publications

(3 citation statements)

References 60 publications

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“…To provide an elaborate evaluation of the seven models involved in our study, various evaluation metrics including Accuracy, Precision, Recall, F1 Score (GC et al, 2021), Inference Time, and Confusion Matrix (Sultan et al, 2019)are employed to evaluate the underlying models. Considering the slight imbalance between the numbers of each class, we calculate the weighted average value of each metric.…”

Section: Evaluation Metricsmentioning

confidence: 99%

AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for SARS-CoV-2 detection

Zhang

Lei²,

Xiao³

et al. 2022

Front. Sens.

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Integrating artificial intelligence with SARS-CoV-2 diagnostics can help in the timely execution of pandemic control and monitoring plans. To improve the efficiency of the diagnostic process, this study aims to classify fluorescent images via traditional machine learning and deep learning-based transfer learning. A previous study reported a CRISPR-Cas13a system combined with total internal reflection fluorescence microscopy (TIRFM) to detect the existence and concentrations of SARS-CoV-2 by fluorescent images. However, the lack of professional software and excessive manual labor hinder the practicability of the system. Here, we construct a fluorescent image dataset and develop an AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for the rapid diagnosis of SARS-CoV-2. Our study proposes Fluorescent Images Classification Transfer learning based on DenseNet-121 (FICTransDense), an approach that uses TIRF images (before and after sample introduction, respectively) for preprocessing, including outlier exclusion and setting and division preprocessing (i.e., SDP). Classification results indicate that the FICTransDense and Decision Tree algorithms outperform other approaches on the SDP dataset. Most of the algorithms benefit from the proposed SDP technique in terms of Accuracy, Recall, F1 Score, and Precision. The use of AI-boosted CRISPR-Cas13a and TIRFM systems facilitates rapid monitoring and diagnosis of SARS-CoV-2.

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Section: Evaluation Metricsmentioning

confidence: 99%

AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for SARS-CoV-2 detection

Zhang

Lei²,

Xiao³

et al. 2022

Front. Sens.

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show abstract

“…By the implementation of machine learning with imaging values a number of novel beef cuts have been recognized like flat iron, Denver cut, chuck eye steak (Von Seggern et al, 2005). Furthermore, the deep learning neural network technique proved exceptional ability in accurately classifying flank steak and tenderloin in beef meat, with a classification accuracy of 100% (GC et al, 2021).…”

Section: Machine Learning and Meat Fraud Assessmentmentioning

confidence: 99%

Cutting edge technologies for the evaluation of Plant-based food and meat Quality: A Comprehensive review

Nurul Alam,

Hashem,

Matar

et al. 2024

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Assessing the food quality is essential to confirm the safety, nutritional value, and sensory attractiveness of the food we ingest. Due to the globalization of food industry and concerns over food security have resulted to a greater demand from consumers for safe and nutritious food. As a result, there is a need for manufacturers to develop cutting edge technologies for recognizing and determining food quality. Presently, stakeholders in the food industry encounter the challenge of ensuring food quality and safety. Producers need to meet sustainable and environmentally robust food production standard, extended shelf life, sensory quality, nutritional value, and functional properties. The general analytical practices identify the primary metabolites for whole food quality which solely verifies the total composition of nutritional metrics. In present days consumers concentrate of functional quality of food items. So, it is essential to examine secondary metabolite compounds, enzymes and other functional quality, authenticity of food items, sources that are critical for health, development, safety, and other aspects of consumer well-being. This review paper seeks to examine the different aspects of evaluating food quality and the methods used to ensure that the food we consume meets the required criteria. Furthermore, it discusses cutting edge technologies for assessing especially secondary metabolites, enzyme functions, functional quality of food items, verifying food authenticity to optimize food manufacturing processes.

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“…An image DA includes a rotation in various angles, zoom in and out, cropping the image, shearing the image to different angles, flipping, changing brightness and contrast, adding and removing noise, scaling, and many segmentation and transformation techniques [29]. DA is not only used to increase the size of the dataset and find patterns that are otherwise obscured in the original dataset, but also used to reduce extensive overfitting in the model [31]. Different DA techniques are available in Tensor-flow that can be performed using the TFLearn DA method [32].…”

Section: Data Augmentation Implementationmentioning

confidence: 99%

Deep Learning Models to Determine Nutrient Concentration in Hydroponically Grown Lettuce Cultivars (Lactuca sativa L.)

et al. 2021

Self Cite

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Deep learning (DL) and computer vision applications in precision agriculture have great potential to identify and classify plant and vegetation species. This study presents the applicability of DL modeling with computer vision techniques to analyze the nutrient levels of hydroponically grown four lettuce cultivars (Lactuca sativa L.), namely Black Seed, Flandria, Rex, and Tacitus. Four different nutrient concentrations (0, 50, 200, 300 ppm nitrogen solutions) were prepared and utilized to grow these lettuce cultivars in the greenhouse. RGB images of lettuce leaves were captured. The results showed that the developed DL’s visual geometry group 16 (VGG16) and VGG19 architectures identified the nutrient levels of lettuces with 87.5 to 100% accuracy for four lettuce cultivars, respectively. Convolution neural network models were also implemented to identify the nutrient levels of the studied lettuces for comparison purposes. The developed modeling techniques can be applied not only to collect real-time nutrient data from other lettuce type cultivars grown in greenhouses but also in fields. Moreover, these modeling approaches can be applied for remote sensing purposes to various lettuce crops. To the best knowledge of the authors, this is a novel study applying the DL technique to determine the nutrient concentrations in lettuce cultivars.

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Using Deep Learning Neural Network in Artificial Intelligence Technology to Classify Beef Cuts

Cited by 18 publications

References 60 publications

AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for SARS-CoV-2 detection

AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for SARS-CoV-2 detection

Cutting edge technologies for the evaluation of Plant-based food and meat Quality: A Comprehensive review

Deep Learning Models to Determine Nutrient Concentration in Hydroponically Grown Lettuce Cultivars (Lactuca sativa L.)

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