Measuring the Weight of Egg with Image Processing and ANFIS Model

Javadikia, Payam; Dehrouyeh, Mohammad Hadi; Naderloo, Leila; Rabbani, Hekmat

doi:10.1007/978-3-642-27172-4_50

Cited by 14 publications

(8 citation statements)

References 18 publications

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“…The needed features were calculated for each image and fed to a multilayer perceptron (MLP) network with different training algorithms. They obtained the correlation coefficient between estimated and measured egg weight as 0.96 with maximum absolute error of 2.3 g. Javadikia et al (2011) predicted egg weight with image processing (IP) and ANFIS model. They measured the width and length of egg by real time IP, designed an ANFIS model and extracted the best relation between IP outputs and the weight of eggs.…”

Section: Introductionmentioning

confidence: 99%

Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network

Firouz¹,

Omid

Alimardani

2014

J Food Sci Technol

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Egg size is one of the important properties of egg that is judged by customers. Accordingly, in egg sorting and grading, the size of eggs must be considered. In this research, a new method of egg volume prediction was proposed without need to measure weight of egg. An accurate and efficient image processing algorithm was designed and implemented for computing major and minor diameters of eggs. Two methods of egg size modeling were developed. In the first method, a mathematical model was proposed based on Pappus theorem. In second method, Artificial Neural Network (ANN) technique was used to estimate egg volume. The determined egg volume by these methods was compared statistically with actual values. For mathematical modeling, the R 2 , Mean absolute error and maximum absolute error values were obtained as 0.99, 0.59 cm 3 and 1.69 cm 3 , respectively. To determine the best ANN, R 2 test and RMSE test were used as selection criteria. The best ANN topology was 2-28-1 which had the R 2 test and RMSE test of 0.992 and 0.66, respectively. After system calibration, the proposed models were evaluated. The results which indicated the mathematical modeling yielded more satisfying results. So this technique was selected for egg size determination.

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Section: Introductionmentioning

confidence: 99%

Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network

Firouz¹,

Omid

Alimardani

2014

J Food Sci Technol

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“…The method presented here is inferior to the prediction accuracy over those using neural networks and classifiers [13,28,29] in the analysis of eggs during storage using visual images data and dielectric characteristics. The disadvantage of these methods is that they use sophisticated computational procedures and require a long time to process data, making them unusable directly on the production line.…”

Section: A Summary Analysis Of the Results Obtainedmentioning

confidence: 78%

Application of Classification Techniques in the Analysis of External Characteristics of Eggs

Georgieva-Nikolova¹,

Zlatev²

2019

ARTTE

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In this article a comparative analysis is made to determine the influence of vectors of selected features derived from geometric, optical and dielectric characteristics of eggs on the accuracy of classification, depending on their weight. Suitable for classification are the principal components and latent variables that reduce feature vectors containing shape indices (D, A, V), spectral indices (TVI, GLI), dielectric characteristics (C, k), selected by four methods (CORR, SFCPP, RELIEFF, FSRNCA). By comparative studies it is found that the use of classification methods (DT, DA, SVM) are more effective in predicting weight of hen eggs than in quail eggs. The proposed egg analysis methods take precedence over the known solutions in this field as it takes into account changes in the internal properties of quail and hen eggs when stored.

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“…Later, they reported a modified pressure system to detect micro cracks in 15 eggs at a time 7 . Javadikia et al 16 tried to measure the width and the length of a given egg by real time image processing techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Some of the research is dedicated to the detection of cracks and dirt on eggs, 4,5,6,7,8,9,10,11,12,13 or measuring physical coordinates of eggs 5,11,14,15,16,17 . In a similar manner to the candling machines, most of these researches usually use light sources such as the incandescent lamp, fluorescent lamp or halogen lamp to back-illuminate the egg.…”

Section: Introductionmentioning

confidence: 99%

A Machine Vision System for Detecting Fertile Eggs in the Incubation Industry

Hashemzadeh¹,

Farajzadeh²

2016

IJCIS

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One of the important factors in increasing the productivity of the incubation industry is to be sure that the eggs placed in the incubators are fertile. In this research, a fertility detection machine vision system is developed and evaluated. To this end, a mechatronic machine is fabricated for acquiring accurate digital images of eggs without harming them. An appropriate and cheap light source is also introduced for illuminating the eggs, which potentially enables a CCD camera to obtain good quality and informative images from inner side of the eggs. Finally, a robust machine vision algorithm is developed to process the captured images and distinguish fertile eggs from infertile ones. In order to evaluate the system, a large egg image dataset is provided using 240 incubated eggs (including 190 fertile and 50 infertile eggs). The fertility detection accuracy of the system on the provided dataset reaches 47.13% at day 1 of incubation, 81.41% at day 2, 93.08% at day 3, 97.73% at day 4, and 98.25% at day 5. Comparisons with existing approaches show that the proposed method achieves a superior performance. The obtained results indicate that the proposed system is highly reliable and applicable in the incubation industry.

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Measuring the Weight of Egg with Image Processing and ANFIS Model

Cited by 14 publications

References 18 publications

Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network

Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network

Application of Classification Techniques in the Analysis of External Characteristics of Eggs

A Machine Vision System for Detecting Fertile Eggs in the Incubation Industry

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