Home ›
Entities
› academia
› Exploring machine learning algorithms for mapping crop types in a heterogeneous agriculture landscape using Sentinel-2 data. A case study of Free State Province, South Africa
Exploring machine learning algorithms for mapping crop types in a heterogeneous agriculture landscape using Sentinel-2 data. A case study of Free State Province, South Africa
Research article (South African Journal of Geomatics, 2022) · cited 13× · AI/ML
Exploring machine learning algorithms for mapping crop types in a heterogeneous agriculture landscape using Sentinel-2 data. A case study of Free State Province, South Africa
Summary
Exploring machine learning algorithms for mapping crop types in a heterogeneous agriculture landscape using Sentinel-2 data. A case study of Free State Province, South Africa is a scholarly article[1].
Key Facts
Exploring machine learning algorithms for mapping crop types in a heterogeneous agriculture landscape using Sentinel-2 data. A case study of Free State Province, South Africa's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Exploring machine learning algorithms for mapping crop types in a heterogeneous agriculture landscape using Sentinel-2 data. A case study of Free State Province, South Africa. Retrieved May 24, 2026, from https://4ort.xyz/entity/exploring-machine-learning-algorithms-for-mapping-crop-types-in-a-heterogeneous-agriculture-landscape-using-sentinel-2-d
MLA“Exploring machine learning algorithms for mapping crop types in a heterogeneous agriculture landscape using Sentinel-2 data. A case study of Free State Province, South Africa.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/exploring-machine-learning-algorithms-for-mapping-crop-types-in-a-heterogeneous-agriculture-landscape-using-sentinel-2-d.
BibTeX@misc{4ortxyz_exploring-machine-learning-algorithms-for-mapping-crop-types-in-a-heterogeneous-agriculture-landscape-using-sentinel-2-d_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Exploring machine learning algorithms for mapping crop types in a heterogeneous agriculture landscape using Sentinel-2 data. A case study of Free State Province, South Africa}}, year = {2026}, url = {https://4ort.xyz/entity/exploring-machine-learning-algorithms-for-mapping-crop-types-in-a-heterogeneous-agriculture-landscape-using-sentinel-2-d}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Exploring machine learning algorithms for mapping crop types in a heterogeneous agriculture landscape using Sentinel-2 data. A case study of Free State Province, South Africa — https://4ort.xyz/entity/exploring-machine-learning-algorithms-for-mapping-crop-types-in-a-heterogeneous-agriculture-landscape-using-sentinel-2-d (retrieved 2026-05-24)
