Advanced neural network models for UAV-based image analysis in remote pathology monitoring of coniferous forests

Machuca C.R., Markov N.G.
 pdf (13978K)

The key problems of remote forest pathology monitoring for coniferous forests affected by insect pests have been analyzed. It has been demonstrated that addressing these tasks requires the use of multiclass classification results for coniferous trees in high- and ultra-high-resolution images, which are promptly obtained through monitoring via satellites or unmanned aerial vehicles (UAVs). An analytical review of modern models and methods for multiclass classification of coniferous forest images was conducted, leading to the development of three fully convolutional neural network models: Mo-U-Net, At-Mo-U-Net, and Res-Mo-U-Net, all based on the classical U-Net architecture. Additionally, the Segformer transformer model was modified to suit the task. For RGB images of fir trees Abies sibirica affected by the four-eyed bark beetle Polygraphus proximus, captured using a UAV-mounted camera, two datasets were created: the first dataset contains image fragments and their corresponding reference segmentation masks sized 256 × 256 × 3 pixels, while the second dataset contains fragments sized 480 × 480 × 3 pixels. Comprehensive studies were conducted on each trained neural network model to evaluate both classification accuracy for assessing the degree of damage (health status) of Abies sibirica trees and computation speed using test datasets from each set. The results revealed that for fragments sized 256 × 256 × 3 pixels, the At-Mo-U-Net model with an attention mechanism is preferred alongside the Modified Segformer model. For fragments sized 480 × 480 × 3 pixels, the Res-Mo-U-Net hybrid model with residual blocks demonstrated superior performance. Based on classification accuracy and computation speed results for each developed model, it was concluded that, for production-scale multiclass classification of affected fir trees, the Res-Mo-U-Net model is the most suitable choice. This model strikes a balance between high classification accuracy and fast computation speed, meeting conflicting requirements effectively.

Keywords: coniferous forests pathological monitoring, unmanned aerial vehicle, small spruce bark beetle Polygraphus proximus, multiclass classification of Siberian fir trees Abies sibirica images, fully convolutional neural networks, transformers
Citation in English: Machuca C.R., Markov N.G. Advanced neural network models for UAV-based image analysis in remote pathology monitoring of coniferous forests // Computer Research and Modeling, 2025, vol. 17, no. 4, pp. 641-663
Citation in English: Machuca C.R., Markov N.G. Advanced neural network models for UAV-based image analysis in remote pathology monitoring of coniferous forests // Computer Research and Modeling, 2025, vol. 17, no. 4, pp. 641-663
DOI: 10.20537/2076-7633-2025-17-4-641-663
URL: http://crm-en.ics.org.ru/journal/article/3634/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

Copyright © 2025 Machuca C.R., Markov N.G.

Indexed in Scopus

Full-text version of the journal is also available on the web site of the scientific electronic library eLIBRARY.RU

The journal is included in the Russian Science Citation Index

The journal is included in the RSCI

International Interdisciplinary Conference "Mathematics. Computing. Education"

Website Copyright © 2009–2025 Institute of Computer Science