Weed classification in sugarcane fields in Northeast Thailand from multi-temporal Sentinel-1 and Sentinel-2 data together with random forest algorithm

Timely and accurate weed detection is essential for sustainable crop production and management. The integration of multiple satellite data sources with powerful machine learning has transformed precision agriculture by enhancing the accuracy and automation of object classification, enabling large-sc...

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Bibliografiske detaljer

Principais autores:	Savittri Ratanopad Suwanlee, Muhammad Hanif, Kemin Kasa, Surasak Keawsomsee, Jaturong Som-ard, Vorraveerukorn Veerachitt, Phattamon Heawchaiyaphum, Akkawat Puntura, Mohammad D. Hossain, Sarawut Ninsawat
Format:	Artigo
Sprog:	Inglês
Udgivet:	Elsevier 2026-06-01
Serier:	Science of Remote Sensing
Fag:	Weed classification Sugarcane Machine learning Random forest Sentinel Multi-temporal data
Online adgang:	http://www.sciencedirect.com/science/article/pii/S2666017225001580
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