FoodScan: Food Monitoring App by Scanning the Groceries Receipts

Abajo, Beatriz Sainz de; Garcí­a-Alonso, José; Berrocal, Javier; Laso, Sergio; Díez, Isabel de la Torre

doi:10.1109/access.2020.3046031

Cited by 15 publications

(6 citation statements)

References 27 publications

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“…Thereafter, we put the small piece of meat into a high-speed universal crusher to make a puree. Finally, FoodScan™ (FOSS, Hillerød, Denmark) was used to measure the crude protein content, crude fat content, water content, and collagen content [ 34 ]. To determine the correlation of all meat qualities, the R /corrplot package was used for the phenotypic correlation visualization [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

Genome-Wide Association Study of Potential Meat Quality Trait Loci in Ducks

Guo

Huang

Bai

et al. 2022

Genes

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With continuously increasing living standards and health requirements of consumers, meat quality is becoming an important consideration while buying meat products. To date, no genome-wide association study (GWAS) for copy number variants (CNVs) and single nucleotide polymorphisms (SNPs) has been conducted to reveal the genetic effects on meat quality in ducks. This study analyzed the phenotypic correlation and heritability of fat, water, collagen, and protein content of duck breast muscle. To identify the candidate variants for meat quality, we performed a GWAS using 273 ducks from an F2 population. The results of the SNP GWAS showed that the BARHL2, COPS7B, and CCDC50 genes were associated with fat content; BLM, WDR76, and EOMES with water content; CAMTA1, FGD5, GRM7, and RAPGEF5 with collagen production; and RIMS2, HNRNPU, and SPTBN1 with protein content. Additionally, 3, 7, 1, and 3 CNVs were associated with fat, water, collagen, and protein content, respectively, in duck breast muscle. The genes identified in this study can serve as markers for meat quality. Furthermore, our findings may help devise effective breeding plans and selection strategies to improve meat quality.

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Section: Methodsmentioning

confidence: 99%

Genome-Wide Association Study of Potential Meat Quality Trait Loci in Ducks

Guo

Huang

Bai

et al. 2022

Genes

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show abstract

“…The suggested work makes use of Flask, which allows users to request on-demand database access to the product, and SQLite to store all user data. For each of the routing commands, the corresponding GET and POST requests are mentioned to ensure the proper functioning of the application [3]. There can be as many numbers of routes, models, and forms.…”

Section: Figure 1 System Architecturementioning

confidence: 99%

Market Maestro: A Smart E-commerce Application

Kalaivani B,

Bharathi B,

Kasu Manaswi

et al. 2024

Int Res J Adv Engg Mgt

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In an era marked by digital transformation, “Market Maestro” emerges as a pioneering e-commerce application, poised to redefine the very essence of shopping. The application transcends the commonplace, presenting a sophisticated shopping experience that seamlessly melds convenience, sustainability, and innovation. At the heart of the application lies an impeccable user interface, meticulously crafted to provide discerning shoppers with a comprehensive array of products, ranging from fresh produce to everyday essentials. The application is developed using Flask and SQLite which help in providing a better server-side logic. What truly distinguishes “Market Maestro” is its eco-conscious packaging options and strategic partnerships with local farmers and suppliers which embarks on a sustainable journey that harmonizes quality with environmental responsibility. The proposed application further prioritizes security, offering a robust and secure payment gateway rendering a seamless checkout and contactless endeavor. Real-time order tracking provides transparency ensuring customers remain informed about their delivery status. Furthermore, the application also encloses a recommendation feature that provides the users with the possible recipes that can be made from the list of ingredients as specified by the user. “Market Maestro” allows users into a new era of shopping, marked by intelligent design, environmental stewardship, and unwavering customer-centricity.

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“…Grocery product recognition offers many applications, such as the control of eating habits [30]. Over the past few years, several datasets have been created for grocery store products, such as the Grocery Store Dataset [2], the MVTec D2S dataset [31], the Retail Product Checkout dataset (RPC) [32] or the Freiburg groceries dataset [33].…”

Section: Overview Of Related Workmentioning

confidence: 99%

Cross Validation Voting for Improving CNN Classification in Grocery Products

Domingo¹,

Medina²,

Rodrigo³

2022

IEEE Access

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The development of deep neural networks that has been carried out in recent years allows solving highly complex computer vision classification problems. Often, although the results obtained with these classifiers are high, there are certain sectors that seek greater accuracy from these systems. Increasing the accuracy of neural networks can be achieved through ensemble learning, which combines different classifiers with the aim of selecting a winner based on different criteria about them. These techniques have traditionally shown good results although they involve training models of different nature and can even produce an overfitting with respect to the training data, so datasets must be chosen to correctly evaluate the result. In this paper, a Cross-Validation-Voting (CVV) technique for grocery product classification is presented. This technique improves several single state-of-the-art classifiers without combining different ones and avoids the problems of overfitting with respect to the training set. The single classifiers are trained multiple times against distributed sets to show how the results obtained to date from the classification of a well-known dataset are improved. In this dataset, an extensive test set was previously selected by the authors to show comparable results with other papers in the literature. The technique is valid not only for vision nets and can be used to solve numerous problems with different kinds of neural networks and classifiers.

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FoodScan: Food Monitoring App by Scanning the Groceries Receipts

Cited by 15 publications

References 27 publications

Genome-Wide Association Study of Potential Meat Quality Trait Loci in Ducks

Genome-Wide Association Study of Potential Meat Quality Trait Loci in Ducks

Market Maestro: A Smart E-commerce Application

Cross Validation Voting for Improving CNN Classification in Grocery Products

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