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 Program Systems: 
Theory and Applications
 ISSN 2079-3316
12+ 
Bilingual electronic scientific Electronic scientific journal of the Ailamazyan Program System Institute of the Ailamazyan PSI of PSI of Russian Academy of Science of RAS
Volume 15 (2024) . Issue 3 (62) . Paper No. 3 (450)

Artificial intelligence and machine learning

Research Article
DOIDigital Object Identifier 10.25209/2079-3316-2024-15-3-53-74

Application of Siamese neural networks to classify plant biomass by visual state

Alexander Vladimirovich Smirnov1Correspondent author, Igor Petrovich Tishchenko2

1,2Ailamazyan Program Systems Institute of RAS, Ves'kovo, Russia
1 Alexander Vladimirovich Smirnov — Correspondent author asmirnov_1991@mail.ru

Abstract. This paper proposes a method for classifying plant biomass by visual condition using images captured in a specially designed greenhouse and Siamese architecture artificial neural network technologies. Criteria for various states of plant biomass have been determined. We have generated our own dataset for training Siamese neural networks, containing samples of biomass states in the form of textures. As a result, a training accuracy of 91.6% and an average classification accuracy of individual biomass states of 73.6%. (In Russian).

Keywords: Siamese neural networks, dataset, plant biomass, classification

MSC-20202020 Mathematics Subject Classification 68T10; 68T45,68T07MSC-2020 68-XX: Computer science MSC-2020 68Txx: Artificial intelligence MSC-2020 68T10: Pattern recognition, speech recognition MSC-2020 68T45: Machine vision and scene understanding

For citation: Alexander V. Smirnov, Igor P. Tishchenko. Application of Siamese neural networks to classify plant biomass by visual state. Program Systems: Theory and Applications, 2024, 15:3, pp. 53–74. (In Russ.). https://psta.psiras.ru/2024/3_53-74.

Full text of article (PDF): https://psta.psiras.ru/read/psta2024_3_53-74.pdf.

The article was submitted 24.06.2024; approved after reviewing 11.08.2024; accepted for publication 11.08.2024; published online 10.09.2024.

© Smirnov A. V., Tishchenko I. P.
2024
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