A Novel Method for Inspection Defects in Commercial Eggs Using Computer Vision

Valencia, Y. M.; Majin, J. J.; Taveira, V. B.; Salazar, J. D.; Stivanello, Maurício Edgar; Ferreira, Lucilene; Stemmer, Marcelo Ricardo

doi:10.5194/isprs-archives-xliii-b2-2021-809-2021

Cited by 5 publications

(5 citation statements)

References 19 publications

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“…Computer vision (CV) is a branch of AI that uses various deep‐learning algorithms to train computers to analyze and interpret data from conventional images and videos (Kamruzzaman & Sun, 2016; Siswantoro et al., 2017; Valencia et al., 2021; Wang, 2014). With the help of CV, constant monitoring of any object can be done, which is impossible for a human.…”

Section: An Overview Of I40 Technologiesmentioning

confidence: 99%

“…CV technology with deep‐learning approaches is used for defect detection in commercial eggs (Valencia et al., 2021). These authors used a set of algorithms for identifying and classifying dirty and cracked eggs based on three distinct methods: classical approaches, CNN, and semantic segmentation.…”

Section: Application Of I40 Technologies In the Egg Industrymentioning

confidence: 99%

“…These results would enable machines to make separation, classification, and packing decisions independently. Numerous characteristics, such as cracks, breakage, weight, yolk quantity, and composition, would be readily recognized on the monitoring farm or on a processing line, and then sent to controllers so that decisions can be taken more quickly (Ma et al., 2017; Siswantoro et al., 2017; Valencia et al., 2021; Wang, 2014).…”

Section: Application Of I40 Technologies In the Egg Industrymentioning

confidence: 99%

“…Linear modeling techniques may not be effective enough, requiring more advanced algorithms for egg sorting. Nonlinear techniques, especially deep learning, have been reported to be effective for egg sorting with CV using color and textural features (Valencia et al., 2021). In product lines, eggs often travel at a relatively rapid speed.…”

Section: Challenges and Future Trendsmentioning

confidence: 99%

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Non‐destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

Ahmed,

Hossainy,

Khaliduzzaman

et al. 2023

Comp Rev Food Sci Food Safe

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The egg is considered one of the best sources of dietary protein, and has an important role in human growth and development. With the increase in the world's population, per capita egg consumption is also increasing. Ground‐breaking technological developments have led to numerous inventions like the Internet of Things (IoT), various optical sensors, robotics, artificial intelligence (AI), big data, and cloud computing, transforming the conventional industry into a smart and sustainable egg industry, also known as Egg Industry 4.0 (EI 4.0). The EI 4.0 concept has the potential to improve automation, enhance biosecurity, promote the safeguarding of animal welfare, increase intelligent grading and quality inspection, and increase efficiency. For a sustainable Industry 4.0 transformation, it is important to analyze available technologies, the latest research, existing limitations, and prospects. This review examines the existing non‐destructive optical sensing technologies for the egg industry. It provides information and insights on the different components of EI 4.0, including emerging EI 4.0 technologies for egg production, quality inspection, and grading. Furthermore, drawbacks of current EI 4.0 technologies, potential workarounds, and future trends were critically analyzed. This review can help policymakers, industrialists, and academicians to better understand the integration of non‐destructive technologies and automation. This integration has the potential to increase productivity, improve quality control, and optimize resource management toward sustainable development of the egg industry.

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Section: An Overview Of I40 Technologiesmentioning

confidence: 99%

Section: Application Of I40 Technologies In the Egg Industrymentioning

confidence: 99%

Section: Application Of I40 Technologies In the Egg Industrymentioning

confidence: 99%

Section: Challenges and Future Trendsmentioning

confidence: 99%

See 2 more Smart Citations

Non‐destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

Ahmed,

Hossainy,

Khaliduzzaman

et al. 2023

Comp Rev Food Sci Food Safe

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“…Numerous studies have been conducted to automate the egg selection process with a focus on improving accuracy. One of the most popular methods is deep learning, where deep learning techniques are employed to classify eggs as either good or defective, ensuring the quality of egg classification [5].…”

Section: Introductionmentioning

confidence: 99%

Development of Classification Method for Determining Chicken Egg Quality Using GLCM-CNN Method

Setiaji,

Adi,

Surarso

2024

ISI

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Eggs are widely consumed due to their high content of vitamin B12, choline and iron. External factors can be detected by observing the eggshell or taken into consideration. The nutrition in eggs is influenced by egg quality, which can be directly observed from the shell. If the shell is cracked, it can be inferred that the egg has poor quality because Salmonella bacteria are dangerous pathogens that can enter the egg. The current issue lies in the complexity and inefficiency of individually classifying eggs by workers, as it is a complicated, time-consuming, frustrating, and inefficient task. Therefore, it is important to separate them automatically. The selection of cracked and intact eggs in this research is an innovative approach to classification using a highly accurate machine learning method. The application of the GLCM-CNN method is an innovative strategy employed for selecting and classifying cracked eggs, as outlined in this research. VGG 19, one of the computational methods, is utilized as a comparative method alongside RESNET 50 and VGG 16. The GLCM-CNN algorithm in this research employed 1,000 images for each class, with a validation set of 20% for each class, resulting in an accuracy of 98%. The inefficient classification process and complexity of automated egg quality classification can be significantly addressed through the findings presented in this research.

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Deep Learning Algorithm as an Alternative of Automatic Classification of Dirty Eggs

Setiaji,

Adi,

Surarso

2023

E3S Web Conf.

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Egg, which is one of the daily needs, contains important nutrition like protein, vitamins, mineral and 75 Kcal. Thus, one of the external qualities of eggs becomes one of the considerations in determining egg quality. The poor condition of the egg shell can be unhealthy and harmful. The dirty eggs and the clean ones are important elements in an automatic separation system since they relate to the consumer’s economic and health values. An element such as dirt at the eggshell leads the raise of Salmonella bacteria and will be worse if the bacteria get into the eggshell. An automatic method of egg selection is crucial to determining the egg's quality before it reaches the consumer. Selecting and classifying egg manually is inefficient for the seller, due to it takes more time and complicated processes. The development of an automatic method is required to categorize and simplify the manual system. Deep Learning Algorithm become one of remarkable solution in developing the method of classification and qualified egg. The method of deep learning algorithms is a computational process that is simpler than other computing methods. The process of egg selection is conducted based on the dirtiness of the eggs. The result of this system is able to classify accurately and efficiently to the select whether the egg has good or bad quality, in order to simplify the breeders’ job.

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A Novel Method for Inspection Defects in Commercial Eggs Using Computer Vision

Cited by 5 publications

References 19 publications

Non‐destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

Non‐destructive optical sensing technologies for advancing the egg industry toward Industry 4.0: A review

Development of Classification Method for Determining Chicken Egg Quality Using GLCM-CNN Method

Deep Learning Algorithm as an Alternative of Automatic Classification of Dirty Eggs

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