In the present work, a method for indirect determination of the weight of Japanese quail eggs is proposed, taking into account changes in their internal properties. Visual data and transmission spectra is used. Shape features and spectral indices are selected and applied. It has been found that egg weight M can be predicted by the volume V of eggs and the spectral index GLI, M=f(V,GLI). The resulting model has a coefficient of determination R2=0,89, low error values, up to 3%. Mean square error MSE=0,03 and root mean square error RMSE=0,2. The results obtained can be used to indirect determination the weight of Japanese quail eggs when incubated, packaged.

In the present work an analysis of the separability of hen egg yolks from different manufacturers is made using image and spectral processing and analysis techniques. Apparent properties of three types of egg yolks were determined and a comparative analysis of these properties was made. Discriminant and SVM (Support vector machines) classifiers were used for classification. A general classification error with lower values ​​is obtained with the b (Lab) color component. In the studies of the spectral characteristics of egg yolks from different manufacturers, the highest accuracy of separation of the target areas is obtained with the kernel SVM classifier combined with the kernel variant of the principal components. When using this classifier, general classification errors of up to 1% were obtained. The results confirm the hitherto known research in this area because the major part of the chicken egg yolk properties studied are visible properties that can be analyzed in the visible spectrum of the reflected light.

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.

No abstract

Mathematical models for describing the shape of eggs find application in various fields of practice. The article proposes a method and tools for a detailed study of the shape and peripheral contours of digital images of eggs that are suitable for grouping and sorting. A scheme has been adapted to determine the morphological characteristics of eggs, on the basis of which an algorithm has been created for obtaining their 3D models, based on data from color digital images. The deviation from the dimensions of the major and minor axes measured with a caliper and the proposed algorithm is 0.5–1.5 mm. A model of a correction factor has been established by which the three-dimensional shape of eggs can be determined with sufficient accuracy. The results obtained in this work improve the assumption that the use of algorithms to determine the shape of eggs strongly depends on those of the bird species studied. It is approved with data for Mallard eggs which have a more elliptical shape and correspondingly lower values of correction coefficient ‘c’ (c = 1.55–4.96). In sparrow (c = 9.55–11.19) and quail (c = 11.71–13.11) eggs, the form tends to be ovoid. After testing the obtained model for eggs from three bird species, sparrow, mallard, and quail, the coefficient of the determination of proposed model was R2 = 0.96. The standard error was SE = 0.08. All of the results show a p-value of the model less than α = 0.05. The proposed algorithm was applied to create 3D egg shapes that were not used in the previous calculations. The resulting error was up to 9%. This shows that in the test, the algorithm had an accuracy of 91%. An advantage of the algorithm proposed here is that the human operator does not need to select points in the image, as is the case with some of the algorithms developed by other authors. The proposed methods and tools for three-dimensional transformation of egg images would be applicable not only for the needs of poultry farming, but also in ornithological research when working with different shaped varieties of eggs. Experimental results show that the proposed algorithm has sufficient accuracy.

In this article a possibility of application of the non-contact method for prediction the poultry eggs weight in storage period of 21 days was researched. A prediction assessment was made by shape features, capacitance, resistance and conductance of eggs. Feature vectors were selected and reduced data by principal component and partial least squares regression methods were used. A non-contact device was proposed and developed to determine the egg weight by video sensor and measurement cell. In order to obtain data for post-processing, a software application was designed. The developed algorithms and procedures were applied to determine the eggs weight, whereby that parameter of eggs could be predicted with the lowest relative error. The survey results show that the eggs weight could be predicted by the proposed system for contactless measurement with accuracy of 94-98%.

The report presents a comparative analysis of algorithms for counting objects in images. They are used in counting eggs. From the three algorithms compared Threshold, Circular Hough and Wateshed, with high performance and small error values ​​is the algorithm Circular Hough. When recognizing the eggs, it is necessary to make a selection of the color model, by which to separate the eggs from the background according to their color and the breed of birds. More research is needed on the impact of the image capturing conditions on the accuracy of algorithms.

The report presents results of selection of informative geometric features of eggs in order to sort them according to the requirements in the normative documents adopted in Bulgaria. The ABC Analysis and Correspondence Analysis methods were used. The results obtained show that for the sorting of eggs in qualitative groups, features based on perimeter, area, large axis, volume, obtained by egg models may be used. Selected informative features confirm and complement those in accessible literature.