Browsing by Author "Georgieva, N"

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    CLASSIFICATION OF HEALTHY AND DISEASED VINE LEAVES USING THE FULL SPECTRA OF OBJECT AREA IN IMAGE
    (university of el oued/جامعة الوادي, 2018-09-01) Georgieva, K; Georgieva, N; Zlatev, Z. D; Georgiev, G; Dimitrova, A
    Grape plant diseases cause critical harm and financial loses in crops. In this manner, early identification of diseases is important on the contemporary stage of development of science and technologies. Optical methods have been widely used to solve the task of detecting diseases in vineyards. The determination of diseases on vines by outer indications of the leaves is made by video cameras, the estimations of the colour components of various colour models are used. The disadvantage of direct using of colour components is that there are high classification errors because of complexity of colours on the surface of vine leaves. The use of full spectra of image object areas with healthy and diseased part of leaves is proposed. Lower values of classification errors are comparable with those, obtained by neural network classifier.
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    CLASSIFICATION OF HEALTHY AND DISEASED VINE LEAVES USING THE FULL SPECTRA OF OBJECT AREA IN IMAGE
    (university of el oued/جامعة الوادي, 2018-09-01) Georgieva, K; Georgieva, N; Zlatev, Z. D
    Grape plant diseases cause critical harm and financial loses in crops. In this manner, early identification of diseases is important on the contemporary stage of development of science and technologies. Optical methods have been widely used to solve the task of detecting diseases in vineyards. The determination of diseases on vines by outer indications of the leaves is made by video cameras, the estimations of the colour components of various colour models are used. The disadvantage of direct using of colour components is that there are high classification errors because of complexity of colours on the surface of vine leaves. The use of full spectra of image object areas with healthy and diseased part of leaves is proposed. Lower values of classification errors are comparable with those, obtained by neural network classifier.