IMPROVEMENT OF REMOTE SENSING METHODS OF THE SPREAD OF INVASIVE PLANT SPECIES IN THE CONTEXT OF ASSESSING POTENTIAL DAMAGE TO THE ENVIRONMENT AND AGRICULTURE
DOI:
https://doi.org/10.37332/2309-1533.2023.1.16Keywords:
invasive species, phytoinvasion, augmentation, recognition, remote sensingAbstract
Faifura V.V., Spivak I.Ya., Faifura V.V. IMPROVEMENT OF REMOTE SENSING METHODS OF THE SPREAD OF INVASIVE PLANT SPECIES IN THE CONTEXT OF ASSESSING POTENTIAL DAMAGE TO THE ENVIRONMENT AND AGRICULTURE
Purpose. The aim of the article is to increase the efficiency of methods of spread of invasive plant species remote sensing to assess potential economic damage to the environment and agriculture through the use of intelligent surveillance systems.
Methodology of research. The following methods were used in the study: monographic – for studying literature sources; machine learning method – for model training; three-way data split method for assessing model accuracy and statistical analysis for comparing recognition results; basic (traditional) methods for image enhancement; gradient image processing (Poisson mixing) – for image fusion.
Findings. A model for improving the efficiency of methods for remote recognition of the spread of invasive plant species was developed and proposed. The performance of the proposed model was compared with the model based on traditional image augmentation methods. It was found that the CAIPIS method, which is the basis of the model, achieves significantly higher results in detecting invasive plant species, especially when the data set is limited. In addition, the method helps to increase the reliability and generalization of deep-learning models for detecting the spread of invasive plant species. The CAIPIS method provides means to create more diverse and realistic training samples, which helps to improve the generalization ability of the deep learning model in detecting invasive plants in aerial imagery of agricultural fields and ecosystems obtained using unmanned aerial vehicles.
Originality. A new method for enhancing (augmenting) aerial images of crop fields is presented, which aims to improve the performance of invasive plant detection models by creating diverse and realistic training samples and increasing the variability of the training dataset to improve the performance of such models.
Practical value. The application of the aerial images enhancement (augmentation) method will increase the effectiveness of controlling the spread of invasive pests in ecosystems.
Key words: invasive species, phytoinvasion, augmentation, recognition, remote sensing.References
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