Method for determining sexual dimorphism of embryos of batch of poultry eggs before incubation, involves using selected data in machine learning process applicable in solving problem of binary classification for small samples with large dimension of initial set of features
	ALEYNIKOV A F; OSIPENKO I V; YAKOVINA I N; SMIRNOV E S
	2023-03-09
专利权人	AS RUSSIA SIBERIAN FEDERAL RES CENT AGRO (ASSI-Non-standard)
申请日期	2023-03-09
专利号	RU2023105569-A
成果简介	NOVELTY - The method involves calculating the perimeters and sums, differences and ratios to each other on sections outside the inscribed circle in pixels the areas, at the acute and blunt ends of the image of the egg. The values are divided into a system of m radius vectors and the modules are determined in pixels from the selected center of the figure to the boundary of the contour of the images of specific egg. Four new images are formed from each sector, to determine the halves of the transverse and longitudinal dimensions of the image of a specific egg. The areas and perimeters are calculated, then the data obtained above for determining the parameters of egg shapes are subjected to static analysis and the data are selected for which the level of significance in selected size group is the lowest. The selected data are used in the machine learning process applicable in solving the problem of binary classification for small samples with a large dimension of the initial set of features. USE - Method for determining sexual dimorphism of embryos of a batch of poultry eggs before their incubation.
IPC 分类号	A01K-043/04 ; A01K-045/00
国家	俄罗斯
专业领域	农业科学
语种	英语
成果类型	专利
文献类型	科技成果
条目标识符	http://119.78.100.226:8889/handle/3KE4DYBR/22197
专题	中国科学院新疆生态与地理研究所
作者单位	AS RUSSIA SIBERIAN FEDERAL RES CENT AGRO (ASSI-Non-standard)
推荐引用方式 GB/T 7714	ALEYNIKOV A F,OSIPENKO I V,YAKOVINA I N,et al. Method for determining sexual dimorphism of embryos of batch of poultry eggs before incubation, involves using selected data in machine learning process applicable in solving problem of binary classification for small samples with large dimension of initial set of features. RU2023105569-A[P]. 2023.

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