A COMPARATIVE STUDY OF MACHINE LEARNING CLASSIFIERS FOR CROP TYPE MAPPING USING VEGETATION INDICES

7 citations as recorded by crossref.

1. Evaluating the performance of random forest, support vector machine, gradient tree boost, and CART for improved crop-type monitoring using greenest pixel composite in Google Earth Engine C. Savitha & R. Talari https://doi.org/10.1007/s10661-025-13880-3
2. A Parallel-Cascaded Ensemble of Machine Learning Models for Crop Type Classification in Google Earth Engine Using Multi-Temporal Sentinel-1/2 and Landsat-8/9 Remote Sensing Data E. Abdali et al. https://doi.org/10.3390/rs16010127
3. Crop mapping using sentinel-1 and sentinel-2 imagery: a systematic review of machine learning classification methods (2015–2025) F. Benzhair et al. https://doi.org/10.1080/10106049.2026.2690363
4. Object-based multi-temporal fusion of optical, radar and texture data with Recursive feature Elimination for improved crop type classification in a heterogeneous irrigation scheme W. Woreket et al. https://doi.org/10.1080/01431161.2026.2736229
5. Analysis of Formal Concepts for Verification of Pests and Diseases of Crops Using Machine Learning Methods J. Tussupov et al. https://doi.org/10.1109/ACCESS.2024.3361046
6. An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower L. Centorame et al. https://doi.org/10.3390/agronomy14040719
7. Exploring Kolmogorov–Arnold networks for hyperspectral crop classification – an evaluative study S. Gupta et al. https://doi.org/10.1016/j.asr.2026.02.053