Email this article Application of Siamese neural networks to classify plant biomass by visual state
- Authors: Smirnov A.V.1, Tishchenko I.P.1
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Affiliations:
- Ailamazyan Program Systems Institute of RAS
- Issue: Vol 15, No 3 (2024)
- Pages: 53-74
- Section: Articles
- URL: https://bakhtiniada.ru/2079-3316/article/view/299206
- DOI: https://doi.org/10.25209/2079-3316-2024-15-3-53-74
- ID: 299206
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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%.
Keywords
About the authors
Alexander Vladimirovich Smirnov
Ailamazyan Program Systems Institute of RAS
Email: asmirnov_1991@mail.ru
Junior researcher, Laboratory of Image Processing and Analysis Methods, A.K. Ailamazyan Program Systems Institute of RAS. Research interests: computer vision; neural networks; robotics; automation and control
Igor Petrovich Tishchenko
Ailamazyan Program Systems Institute of RAS
Email: igor.p.tishchenko@gmail.com
Candidate of Technical Sciences, Head of the Laboratory of Image Processing and Analysis Methods, A.K. Ailamazyan Institute of Software Systems of the Russian Academy of Sciences. Research interests: computer vision; neural networks; robotics; automation and control
References
- Gall G. E. C., Pereira T. D., Jordan A., Meroz Y.. “Fast estimation of plant growth dynamics using deep neural networks”, Plant Methods, 2022, no.1.
- Taewon M., Woo-Joo C., Se-Hun J., Da-Seul C., Myung-Min O.. “Growth Analysis of Plant Factory-Grown Lettuce by Deep Neural Networks Based on Automated Feature Extraction”, Horticulturae, 2022, no.12.
- Savitha P., Mungamuri S.. “Accurate plant species analysis for plant classification using convolutional neural network architecture”, International Journal of Reconfigurable and Embedded Systems (IJRES), 2024, no.1, pp. 160–170.
- Quoc T. N., Hoang V. T.. “VNPlant-200 – A Public and Large-Scale of Vietnamese Medicinal Plant Images Dataset”, Integrated Science in Digital Age 2020, 2020, pp. 406–411.
- Joseph D. S., Pawar P. M., Pramanik R.. “Intelligent plant disease diagnosis using convolutional neural network: a review”, Multimedia Tools and Applications, 2023, no.14.
- Khanna M., Singh L. K., Thawkar S., Goyal M.. “PlaNet: a robust deep convolutional neural network model for plant leaves disease recognition”, Multimedia Tools and Applications, 2024, no.2.
- Faisal M., Leu J. S., Avian C., Prakosa S. W., Köppen M.. “DFNet: Dense fusion convolution neural network for plant leaf disease classification”, Agronomy Journal, 2024, no.3, pp. 826–838.
- Åström O., Hedlund H., Sopasakis A.. “Machine-Learning Approach to Non-Destructive Biomass and Relative Growth Rate Estimation in Aeroponic Cultivation”, Agriculture, 2023, no.13.
- Wang B., Wang D.. “Plant Leaves Classification: A Few-Shot Learning Method Based on Siamese Network”, IEEE Access, 2019, no.7, pp. 151754–151763.
- Figueroa-Mata G., Mata-Montero E.. “Using a Convolutional Siamese Network for Image-Based Plant Species Identification with Small Datasets”, Biomimetics, 2020, no.5.
- Goncharov P., Uzhinskiy A., Ososkov G., Nechaevskiy A., Zudikhina J.. “Deep Siamese Networks for Plant Disease Detection”, Mathematical Modeling and Computational Physics 2019 (MMCP 2019), 2020, no.226.
- Sherly K. K., Sonia A.. “Hybrid CNN Models for Plant Species Recognition and Disease Detection”, Intelligent Computing, 2024, pp. 35–50.
- Du J., Fu W., Zhang Y., Wang Z.. “Advancements in Image Recognition: A Siamese Network Approach”, Information Dynamics and Applications, 2024, no.2, pp. 89–103.
- Bromley J., Guyon I., LeCun Y., Säckinger E., Shah R.. “Signature verification using a ‘Siamese’ time delay neural network”, In Proceedings of the 6th International Conference on Neural Information Processing Systems (NIPS'93), 1993, pp. 737–744.
- Смирнов А. В., Иванов Е. С.. «Анализ изображений растения, полученных с камеры системы автоматизированного ухода, для визуальной оценки изменения его состояния с течением времени», Программные системы: теория и приложения, 2023, №3, с. 37–58.
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