title: Synthetic data generation methods for training neural networks in the task of segmenting the level of crop nitrogen status in images of unmanned aerial vehicles in an agricultural field
metadata.name: dc.contributor.author
metadata.value: Molin, Alexander E.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Blekanov, Ivan S.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Mitrofanov, Evgenii P.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Mitrofanova, Olga A.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2024-04-22T20:20:54Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2024-04-22T20:20:54Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2024-03
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Molin A. E., Blekanov I. S., Mitrofanov E. P., Mitrofanova O. A. Synthetic data generation methods for training neural networks in the task of segmenting the level of crop nitrogen status in images of unmanned aerial vehicles in an agricultural field. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2024, vol. 20, iss. 1, pp. 20–33. https://doi.org/10.21638/11701/spbu10.2024.103 (In Russian)
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/11701/spbu10.2024.103
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/45327
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: This study is devoted to the automatization of the image masks’ construction of largesized
  agricultural objects in precision farming tasks for training neural network methods
  for crop’s nitrogen status analysis using georeferenced images. The scientific direction is
  extremely relevant because it allows to automate and replace the manual process of data
  labeling, significantly reducing the cost of preparing training samples. In the paper, four
  new synthetic data generation methods are proposed for training neural networks aimed at UAV image segmentation by the level of crop nitrogen supply on an agricultural field. In
  particular, the paper gives a description of synthetic data generation algorithms based on
  nitrogen covering with lines, parabolas, and areas. Experiments were carried out to test and
  evaluate the quality of these algorithms using eight modern image segmentation methods:
  two classical machine learning methods (Random Forest and XGBoost), four convolutional
  neural network methods based on U-Net architecture, and two transformers (TransUnet and
  UnetR). The results showed that two algorithms based on areas gave the best accuracy
  for convolutional neural networks and transformers — 98–100 %. Classical machine learning
  methods showed very low values for all quality metrics — 27–44 %.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Applied Mathematics. Computer Science. Control Processes;Volume 20; Issue 1
metadata.lang: -
metadata.name: dc.subject
metadata.value: nitrogen level segmentation
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: deep learning
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: machine learning
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: synthetic data generation
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: UAV images
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: remote sensing data labeling
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: smart agriculture
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Synthetic data generation methods for training neural networks in the task of segmenting the level of crop nitrogen status in images of unmanned aerial vehicles in an agricultural field
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: vestnik_spbu10_2024_020.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/45327/1/vestnik_spbu10_2024_020.pdf
resources.format: Adobe PDF
resources.size: 2,77 MB