A practical framework for automatic food products classification using computer vision and inductive characterization

Calvo, Hiram; Moreno-Armendáriz, Marco A.; Godoy-Calderón, Salvador

doi:10.1016/j.neucom.2015.06.095

Cited by 11 publications

(5 citation statements)

References 26 publications

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“…The angular second moment (ASM) is a parameter that measures the homogeneity of an image [48], and ASM showed a time-dependent increment (Figure 6B). Samples with edible coating showed higher values than those without it.…”

Section: Image Analysismentioning

confidence: 99%

Effect of an Edible Coating Based on Chitosan and Oxidized Starch on Shelf Life of Carica papaya L., and Its Physicochemical and Antimicrobial Properties

et al. 2018

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Papaya production plays an important economic role in Mexico’s economy. After harvest, it continues to ripen, leading to softening, skin color changes, development of strong aroma, and microbial spoilage. The objective of this work was to apply an active coating of chitosan–starch to increase papaya shelf life and to evaluate physicochemical and antimicrobial properties of the coating. Papaya surfaces were coated with a chitosan-oxidized starch (1:3 w/w) solution and stored at room temperature (25 ± 1 °C) for 15 days. Variables measured were color, titratable acidity, vitamin C, pH, soluble solids, volatile compounds by gas chromatography, texture, homogeneity by image analysis, and coating antimicrobial activity. At the end of the storage time, there were no significant differences (p > 0.05) between coated and uncoated papayas for pH (4.3 ± 0.2), titratable acidity (0.12% ± 0.01% citric acid), and soluble solids (12 ± 0.2 °Bx). Papaya firmness decreased to 10 N for coated and 0.5 N for uncoated papayas. Volatile compounds identified in uncoated papaya (acetic acid, butyric acid, ethyl acetate, ethyl butanoate) are related to fermentation. Total microbial population of coated papaya decreased after 15 days, whereas population of uncoated papaya increased. This active coating permitted longer shelf life of papaya than that of the uncoated fruit.

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Section: Image Analysismentioning

confidence: 99%

Effect of an Edible Coating Based on Chitosan and Oxidized Starch on Shelf Life of Carica papaya L., and Its Physicochemical and Antimicrobial Properties

et al. 2018

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“…Computer vision and inductive characterization with a limited number of characteristics have been provided [3] with three examples of how this framework has been effectively implemented to enhance the quality inspection process.…”

Section: Literature Surveymentioning

confidence: 99%

“…e direct sensory quality of agricultural goods is mostly indicated by their exterior quality. Exterior characteristics such as color, texture, size, form, and flaws are often used to assess product's quality [3]. While output and revenues are important, food manufacturing company administrators also take into account the product's prominence, the social context, and the problems faced by farmers in completing their agricultural chores [4].…”

Section: Introductionmentioning

confidence: 99%

Food Quality Inspection and Grading Using Efficient Image Segmentation and Machine Learning-Based System

Hemamalini

Subbaraj

Nachiyappan

et al. 2022

Journal of Food Quality

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One of the most critical aspects of quality assurance is inspecting products for defects before they are sold or shipped. A good product is more vital than having more of the same item for a customer’s enjoyment. The client has a significant role in determining the quality of a product. Another way to think about quality is as the total of all the characteristics that contribute to the creation of items that the client enjoys. Recently, the application of machine vision and image processing technology to improve the surface quality of fruits and other foods has increased significantly. This is primarily because these technologies make significant advancements in areas where the human eye falls short. This means that, by utilizing computer vision and image processing techniques, time-consuming and subjective industrial quality control processes can be eliminated. This article discusses how to check and assess food using picture segmentation and machine learning. It is capable of classifying fruits and determining whether a piece of fruit is rotten. To begin, Gaussian elimination is used to remove noise from images. Then, photos are subjected to histogram equalization in order to improve their quality. Segmentation of the image is carried out using the K-means clustering technique. Then, fruit photos are classified using machine learning methods such as KNN, SVM, and C4.5. These algorithms determine if a fruit is damaged or not.

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“…Click here to view linked References security [2][3][4]; for example, the recent outbreak of desert locust in Africa. To prevent pest plagues and enable effective treatments, real-time information of pests (e.g.…”

Section: Manuscriptmentioning

confidence: 99%

Probabilistic faster R-CNN with stochastic region proposing: Towards object detection and recognition in remote sensing imagery

Chen

2021

Neurocomputing

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A practical framework for automatic food products classification using computer vision and inductive characterization

Cited by 11 publications

References 26 publications

Effect of an Edible Coating Based on Chitosan and Oxidized Starch on Shelf Life of Carica papaya L., and Its Physicochemical and Antimicrobial Properties

Effect of an Edible Coating Based on Chitosan and Oxidized Starch on Shelf Life of Carica papaya L., and Its Physicochemical and Antimicrobial Properties

Food Quality Inspection and Grading Using Efficient Image Segmentation and Machine Learning-Based System

Probabilistic faster R-CNN with stochastic region proposing: Towards object detection and recognition in remote sensing imagery

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