INTELLIGENCE SYSTEMS AND TECHNOLOGIES
MATHEMATICAL FOUNDATIONS OF INFORMATION TECHNOLOGY
M. V. Belyakov, I. Yu. Efremenkov Development of an Algorithm for Controlling a Robotic Grain Sorting System Infected with Fusarium
APPLIED ASPECTS OF COMPUTER SCIENCE
MATHEMATICAL MODELLING
M. V. Belyakov, I. Yu. Efremenkov Development of an Algorithm for Controlling a Robotic Grain Sorting System Infected with Fusarium
Abstract. 

The article presents a control algorithm for a robotic system that sorts grain infected with fusarium. The relevance of the study lies in the fact that every year 15-50% of cereals worldwide become unusable due to infection with fungal spores and, if ingested by animals or humans, can cause serious health problems. Such a seed sorting system can be implemented using the optical photoluminescent method. The developed algorithm includes initialization and verification of systems, obtaining photo signals and grain identification based on reasonable criteria, followed by the launch of actuators. A program-controlled optical monitoring system for wheat, barley, and oat seeds using light and photodiodes is proposed.

Keywords: 

algorithm, diagnostics, fusarium, system, technique, luminescence.

DOI 10.14357/20718632250106 

EDN REULDH

PP. 64-73.

References

1. Alemu K. Detection of Diseases, Identification and Diversity of Viruses // Journal of Biology, Agriculture and Healthcare. 2015. Vol. 5. No. 1. pp. 132–141. 
2. Mohd A. M., Bachik N. A., Muhadi N. A., Yusof T.T., Gomes C. Non-destructive techniques of detecting plant diseases: A review // Physiological and Molecular Plant Pathology. 2019. Vol. 108. DOI: https://doi.org/10.1016/j.pmpp.2019.101426
3. Zudyte B., Luksiene Z. Visible light-activated ZnO nanoparticles for microbial control of wheat crop // Journal of Photochemistry and Photobiology B: Biology. 2021. Vol. 219. DOI: https://doi.org/10.1016/j.jphotobiol.2021.112206.
4. Bilgili A., Bilgili A. V., Tenekeci M. E., Karadag K. Spectral characterization and classification of two different crown root rot and vascular wilt diseases (fusarium oxysporum f.sp. radicis lycopersici and fusarium solani) in tomato plants using different machine learning algorithms // European Journal of Plant Pathology. 2024. Vol. 165, No. 2. pp. 271–286. DOI: 10.1007/s10658-022-02605-8.
5. Ostanin S. A., Ryzhika F., Semenov G. A. Spectral statistical algorithm for data processing of an electronic acoustic separator of sunflower seeds // Proceedings of the St. Petersburg State Agrarian University. 2016. No. 42. pp. 389-393 (In Russ).
6. Lebedev D. V., Rozhkov E. A., Abramov D. S. Application of multifunctional optical-electronic vision technologies for calibration and analysis of seeds // Bulletin of the Kurgan State Agricultural Academy. 2020. No. 2(34). pp. 67-74 (In Russ).
7. Barysheva N. N., Pronin S. P. The choice of an algorithm for filtering experimental data to control the germination of wheat seeds by membrane potentials // Bulletin of the Altai State Agrarian University. 2019. No. 4(174). pp. 150-154 (In Russ).
8. Musaev F. B., Antoshkina M. S., Soldatenko A.V. [et al.] Algorithms for automatic digital analysis of the quality of vegetable seeds // Vegetables of Russia. 2018. No. 3(41). pp. 86-88 (In Russ).
9. Krylovetsky A. A., Sukhodolov D. M. Image recognition of grain mixture elements by deep learning methods using KERAS and TENSORFLOW libraries / // Bulletin of Voronezh State University. Series: System analysis and Information Technology. 2018. No. 2. pp. 139-148 (In Russ).
10. Sharma A., Hazra D., Gupta S., Kumari D. Potato Leaf Disease Classification Using Federated Learning // Recent Trends in Image Processing and Pattern Recognition. 2024. Vol. 1. P. 191–201. DOI: 10.1007/978-3-031-53082-1_16
11. Kaushik I., Prakash N., Jain A. Plant disease detection using a depth-wise separable-based adaptive deep neural network // Multimedia Tools and Applications. 2024. DOI: https://doi.org/10.1007/s11042-024-19047-5
12. Murad M. U., Okatan A. Smart detection and diagnosis of plant disease using deep and machine learning methods // International Research Journal of Modernization in Engineering Technology and Science. 2023. Vol. 5. pp. 1300–1307 DOI: https://www.doi.org/10.56726/IRJMETS33187
13. Moskovskiy M. N., Belyakov M. V., Dorokhov A. S. [et al] Design of Device for Optical Luminescent Diagnostic of the Seeds Infected by Fusarium // Agriculture. 2023. Vol. 13. No. 3. pp. 619. DOI: https://doi.org/10.3390/agriculture13030619
14. Butovchenko A.V. The use of mechanized purification and photoseparation of seed grains and corn cobs in modern technologies // Polythematic network electronic Scientific Journal of the Kuban State Agrarian University. 2017. No.134. pp. 984-994. DOI 10.215/1990-4665-134-080 (In Russ).
15. Moskovsky M. N., Shogenov Yu. H., Lavrov A. V. [et al.] Spectral Photoluminescent Parameters of Barley Seeds (Hordéum vulgáre) Infected with Fusarium ssp // Photochemistry and Photobiology. 2023. Vol. 99. P. 29-34. DOI: https://doi.org/10.1111/php.13645
16. Belyakov M.V., Moskovsky M. N., Efremenkov I.Yu. [et al.] Optical photoluminescent properties of plant seeds during infection with mycopathogens // Engineering technologies and systems. 2024. No. 2(34). pp. 281-294. DOI: 10.15507/2658-4123.034.202402.281-294 (In Russ).
17. ZORKIY photo separator [Electronic resource]. – Access mode: https://csort.ru/photoseparator/zorkiy /, free. – (date of access: 01/20/2025).
18. Photoseparator OPTIMA [Electronic resource]. – Access mode: https://www.agrobase.ru/catalog/machinery/machinery_bbf46488-2e40-43b1-bdb0-a4c6ca6a06b0#section-description, free. – (date of access: 19.01.2025).
19. Smart Sport 1.1 photo separator. [Electronic resource]. – Access mode: https://csort.ru/photoseparator/new/smartsort-1-1 /, free. – (date of request: 19.01.2025). 

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