Результаты поиска по 'forest insect':
Найдено статей: 2
  1. Soukhovolsky V.G., Kovalev A.V., Palnikova E.N., Tarasova O.V.
    Modelling the risk of insect impacts on forest stands after possible climate changes
    Computer Research and Modeling, 2016, v. 8, no. 2, pp. 241-253

    A model of forest insect population dynamics used to simulate of “forest-insect” interactions and for estimation of possible damages of forest stand by pests. This model represented a population as control system where the input variables characterized the influence of modifier (climatic) factors and the feedback loop describes the effect of regulatory factors (parasites, predators and population interactions). The technique of stress testing on the basis of population dynamics model proposed for assessment of the risks of forest stand damage and destruction after insect impact. The dangerous forest pest pine looper Bupalus piniarius L. considered as the object of analysis. Computer experiments were conducted to assess of outbreak risks with possible climate change in the territory of Central Siberia. Model experiments have shown that risk of insect impact on the forest is not increased significantly in condition of sufficiently moderate warming (not more than 4 °C in summer period). However, a stronger warming in the territory of Central Siberia, combined with a dry summer condition could cause a significant increase in the risk of pine looper outbreaks.

    Keywords: forest insect, population dynamics, models, modified factors, climate, stands, impact, risks, stresstesting.
    Views (last year): 3. Citations: 1 (RSCI).
  2. 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, v. 17, no. 4, pp. 641-663

    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.

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