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Conference material: "Proceedings of the International Conference on Computer Graphics and Vision Graphicon (19-21 September 2022, Ryazan)"
Authors: Alibekov M.R.
Diagnosis of Plant Biotic Stress by Methods of Explainable Artificial Intelligence
Abstract:
Methods for digital image preprocessing, which significantly increase the efficiency of ML methods, and also a number of ML methods and models as a basis for constructing simple and efficient XAI networks for diagnosing plant biotic stresses, have been studied. A complex solution has been built, which includes the following stages: automatic segmentation; feature extraction; classification by ML models. The best classifiers and feature vectors are selected. The study was carried out on the open dataset PlantVillage Dataset. The single-layer perceptron (SLP) trained on a full vector of 92 features (20 statistical, 72 textural) became the best according to the F1- score=93% criterion. The training time on a PC with an Intel Core i5-8300H CPU took 189 minutes. According to the criterion F1-score/number of features, SLP trained on 7 principal components with F1-score=85% also became the best. Training time - 29 minutes. The criterion F1- score/number+interpretability of features favors the selected 9 features and the random forest model, F1-score=83%. The research software package is made in a modern version of Python using the OpenCV and deep learning model libraries, and is able for using in precision farming.
Keywords:
Explainable artificial intelligence, machine learning, image processing, plant biotic stress, diagnostics, segmentation, recursive feature elimination
Publication language: russian,  pages: 12 (p. 728-739)
Russian source text:
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About authors:
  • Alibekov M.R.,  orcid.org/0000-0002-9201-8878,  Lobachevsky State University of Nizhny Novgorod (UNN)