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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
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.
Explainable artificial intelligence, machine learning, image processing, plant biotic stress, diagnostics, segmentation, recursive feature elimination
Publication language: russian,  pages: 12 (p. 728-739)
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About authors:
  • Alibekov M.R.,,  Lobachevsky State University of Nizhny Novgorod (UNN)