Convolutional neural network for industrial egg classification

Shimizu, Ryo; Yanagawa, Shusuke; Shimizu, Toru; Hamada, Mototsugu; Kuroda, Tadahiro

doi:10.1109/isocc.2017.8368830

Cited by 13 publications

(3 citation statements)

References 1 publication

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“…Convolutional Neural Networks (CNN) are one of the most important and successful deep learning methods in the field of image analysis in which several layers are efficiently combined and trained. CNNs have proven to be promising tools in the food and agriculture industry field (Sladojevic et al, 2016), (Farooq and Sazonov, 2017), (Shimizu et al, 2017), however these approaches can be computationally expensive. Given the uncertainties of the advantages or disadvantages that these two approaches, classical and based on deep learning, may present in egg inspection systems.…”

Section: Defectsmentioning

confidence: 99%

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

Valencia

Majin

Taveira

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The objective of this work is to compare the use of classical image processing approaches with deep learning approaches in a visual inspection system for defects in commercial eggs. Currently, many industries perform the detection of defects in eggs manually, this implies a large number of workers with long working hours who are exposed to visual fatigue and physical and mental discomfort. As a solution, this work proposes to develop an automatic inspection technique for defects in eggs using computer vision, capable of being operable in the industry. Different image processing approaches were evaluated in order to determine the best solution in terms of performance and processing time.

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Section: Defectsmentioning

confidence: 99%

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

Valencia

Majin

Taveira

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…Data accuracy is necessary because people tend to verify and review the quality of data to ensure that a device is effective and efficient [20], [23], [24]. As an example, a study in [25] used CNN for image classifying. They used 2000 egg images to train the CNN, resulting to the project's accuracy of 92.3% for the 89,000 test subjects.…”

Section: Introductionmentioning

confidence: 99%

An Automated Egg Incubator with Raspberry Pi-Based Camera Assisted Candling and R-CNN-based Maturity Detection

Tolentino¹,

Alpay²,

Grutas³

et al. 2022

IJCDS

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This study focuses on developing an automated egg incubator with a camera-assisted candler for egg maturity detection of balut and penoy commercial duck eggs. The incubator is a four-layer chamber installed with a heater, fan, and DHT11 sensors. DHT11 sensors are interfaced with a Raspberry Pi 4 to observe and maintain the optimal parameters inside the incubator. Trays with built-in candlers made from fluorescent bulbs are placed per layer with a capacity of 20 eggs positioned on rollers. These rollers are programmed to drive every 8 hours for 5 minutes for the egg turning which is essential in incubating eggs. Cameras are installed to capture the images of the candled eggs on their 1st, 10th, and 18th day. The result will be shown on a monitor with a user-friendly GUI which will help the vendor to determine the condition and maturity of the eggs inside the incubator. A region-based convolutional neural network (R-CNN/RCNN) was used as the classifier algorithm for balut, penoy, and fresh eggs. The classification accuracy of the proposed system is 80.5%.

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“…The studies are only conducted using chicken eggs as its dataset provides us the opportunity to localize it and to utilize our local duck eggs as our dataset (Rachmawanto et al 2020, Garcia-Alegre et al 2000, Lunadei et al 2011, Shimizu et al 2017. Currently there is no available official standard procedure provided in the Philippines in assessing the Philippine Mallard Duck egg quality, thus this aims to develop a model based on standard practices from the duck egg industry.…”

Section: The Problem Gap or Opportunitymentioning

confidence: 99%

Duck Egg Quality Classification Based on its Shell Visual Property Through Transfer Learning Using ResNet-50

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Duck egg quality inspections are done manually in the Philippines which is not reliable due to human subjectivity, visual stress, and tiredness. There is no documentation regarding the general standard on determining the quality of duck eggs and local farmers have different standards. This research aims to classify duck egg quality into 3 classes namely, Balut/Penoy, Salted Egg, and Table egg. Two angles of 600 duck eggs were captured inside an image acquisition setup. Using ResNet-50 as a base model, its last fully connected layer was replaced by a classifier block. Hyperparameter tuning with Stratified 5-fold Cross Validation was utilized. It was observed that batch size of 8, epoch of 110, and learning rate of 0.0001 has given the lowest validation loss which was used to train the final model. Performance Metrics was obtained. Overall, the result of the model yielded 90%, 95%, and 65% for Balut/Penoy, Salted Egg, and Table egg, respectively, averaging an 83.33% overall accuracy of the model. As observed, the model is not able to accurately differentiate between hairline and broken cracks. Additionally, falsely classified images also occur when the size of an egg is close to the threshold to other sizes.

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Convolutional neural network for industrial egg classification

Cited by 13 publications

References 1 publication

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

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

An Automated Egg Incubator with Raspberry Pi-Based Camera Assisted Candling and R-CNN-based Maturity Detection

Duck Egg Quality Classification Based on its Shell Visual Property Through Transfer Learning Using ResNet-50

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