Roik M. V., Chernus'kyj V. V.
Pages: 46-53.

Full article: 

---

Abstract

The purpose. To form methodology and procedure of building system of accumulation and analytically verified use of information of matrixes of digital photo for formation of statistical fund of parametric states of selection samples in various conditions of vegetative period for its further complex unified assessment and analysis within the limits of intellectual expert system. Methods. Instrumental-cameral, matrix-digital, fractal, mathematical-statistical. Results. According to innovative scientific concept of the “third” form of variability of plants statistical fund is formed of parameters of ontogenetic states of selection samples of leading crops in the form of regression equations of fractal dimensions of quantity. Conclusions. Methodology is offered of expert assessment of dynamic system of formation of morpho-metric parameters of plants of selection samples of crops in the form of fractal dimensions of quantity of the most valuable productive elements in general plant stand. The verified technique of compression of data of matrix of digital photo of selection object (5-6 mbyte of information) up to the level of generalized analytic function of regression equation is developed. Principle of automation of cameral processing, built on mathematically correct algorithms of programs of segmentation of digital visualized images, and further compression of the gained segments up to the level of invariant fractal initial systems is realized in selection practice. Principles of formation of system of metadata in the form of phase-parametrical portraits, Liapunov’s fractals, cubic splines for building supersystem of description of ontogenetic development of plants in conditions of definite vegetative period are offered as a prospect for further researches. In the given supersystem there is an opportunity of determination emergent-synergetic regularity of formation of productive phenotype under action of epigenetic-trigger mechanisms of VGS. That opens real prospects of long-term forecasting productivity.

---

Key words: epigenetics, selection samples, digital matrix, self-affine transformations, fractal dimensions of quantity.

---

References

1. Maletskyi S.Y., Roik N.V., Drahavtsev V.A. (2013). Tret’ya izmenchivost’. Tipy nasledstvennosti i vosproizvodstva semyan u rasteniy. Sel’skokhozyaystvennaya biologiya. Problemy, obzory. № 5. pp. 3–29. [in Russia].
2. Ysaeva V.V., Kasianov N.V. (2006). Fraktalnost pryrodnykh y arkhytekturnblkh form. Vestnyk DVO RAN. № 5. pp. 119-127. [in Russia].
3. Pleshanov V.S., Napriushkyn A.A., Kybytkyn V.V. (2010). Osobennosty prymenenyia teoryy fraktalov v zadachakh analyza yzobrazhenyi. Avtometryia. T. 46, № 1. pp. 86-97. [in Russia].
4. Antony Jobin, Madhu S. Nair, Rao Tatavarti (2012). Plant Identification Based on Fractal Refinement Technique (FRT). Procedia Technology, 6, 171 – 179.
5. Vizil’ter YU.V., ZHeltov S.YU., Knyaz’ V.A. et al. (2007). Obrabotka i analiz tsifrovykh izobrazheniy s primerami na LabVIEW IMAQ Vision. Moskva: DMK Press. pp. 464. [in Russia].
6. Kruglov A.V., YUgfel’d I.D. (2016). Realizatsiya interaktivnoy segmentatsii dlya sensornykh ustroystv na baze OS ANDROID. Sovremennyye naukoyemkiye tekhnologii. № 2. CH. 2. pp.229-235. [in Russia].
7. Tsevma V. M., Khokhlov O. M. (2013). Stupin vidpovidnosti otsinok oznak zerna riznykh henotypiv pshenytsi pry shyrokoriadnii i sutsilnii skhemakh posivu. Zbirnyk naukovykh prats SHI – NTsNS. vyp. 21 (61). pp. 53-61. [in Ukraine].
8. Chernuskyi V.V. (2013). Pro mozhlyvist unifikatsii ta avtomatyzatsii okovymirnoi otsinky selektsiinykh zrazkiv shliakhom poperednoi pobudovy hrafichnykh modelei steblostoiu zhyta ozymoho i horokhu polovoho. Ahropromyslove vyrobnytstvo Polissia. Vyp. 6, pp. 57-62. [in Ukraine].
9. Chernuskyi V.V. (2017). Pryntsypy avtomatyzatsii i vizualizatsii tekhnolohichnykh protsesiv doboru v systemi selektsii shliakhom afinnoho vidobrazhennia matryts tsyfrovoi fotohrafii na analitychnu ploshchynu. Ahropromyslove vyrobnytstvo Polissia. Vyp. 10. S.9-13. [in Ukraine].
10. Galitskiy V.V. (2016). Fraktal’naya model’ poyavleniya protorasteniya. Matematicheskaya biologiya i bioinformatika. T. 11. No 2. doi: 10.17537/2016.11.225. [in Russia ].
11. Kashtanov N.V., Lyakhov A.F. Fraktal’naya razmernost’ vizual’nogo obraza matematicheskoy matritsy. Komp’yuternyye instrumenty v obrazovanii. №2, 2013. pp. 59-66. [in Russia].