A System-Level Approach to Pixel-Based Crop Segmentation from Ultra-High-Resolution UAV Imagery

Ismailova A. Yessenova M. Murzabekova G. Tussupov J. Abdikerimova G.
January 2026 Multidisciplinary Digital Publishing Institute (MDPI)

Applied System Innovation
2026 #9 Issue 1

This paper proposed a two-level hybrid stacking model for the classification of crops—wheat, soybean, and barley—based on multispectral orthomosaics obtained from uncrewed aerial vehicles. The proposed method unites gradient boosting algorithms (LightGBM, XGBoost, CatBoost) and tree ensembles (RandomForest, ExtraTrees, Attention-MLP deep neural network), whose predictions fuse at the meta-level using ExtraTreesClassifier. Spectral channels, along with a wide range of vegetation indices and their statistical characteristics, are used to construct the feature space. Experiments on an open dataset showed that the proposed model achieves high classification accuracy (Accuracy ≈ 95%, macro-F1 ≈ 0.95) and significantly outperforms individual algorithms across all key metrics. An analysis of the seasonal dynamics of vegetation indices confirmed the feasibility of monitoring phenological phases and early detection of stress factors. Furthermore, spatial segmentation of orthomosaics achieved approximately 99% accuracy in constructing crop maps, making the developed approach a promising tool for precision farming. The study’s results showed the high potential of hybrid ensembles for scaling to other crops and regions, as well as for integrating them into digital agricultural information systems.

crop classification , multispectral orthomosaics , unmanned aerial vehicles (UAVs) , vegetation indices

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Department of Information Systems, Kazakh Agrotechnical Research University Named After S. Seifullin, Astana, 010011, Kazakhstan
Department of Information Systems, L. N. Gumilyov Eurasian National University, Astana, 010000, Kazakhstan
Department of Computer Sciences, S. Seifullin Kazakh Agrotechnical University, Astana, 010011, Kazakhstan

Department of Information Systems
Department of Information Systems
Department of Computer Sciences

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