Assessment of three broadleaf weed species classification in rice field using UAV hyperspectral imaging and machine learning
Nursyazyla Sulaiman, Nik Norasma Che’Ya, Abdul Shukor Juraimi, Nisfariza Mohd Noor, Rhushalshafira Rosle, Muhammad Huzaifah Mohd Roslim · 2025
Read Assessment of three broadleaf weed species classification in rice field using UAV hyperspectral imaging and machine learning by Nursyazyla Sulaiman, Nik Norasma Che’Ya, Abdul Shukor Juraimi, Nisfariza Mohd Noor, Rhushalshafira Rosle, Muhammad Huzaifah Mohd Roslim (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12756349.txt.
Description
Nursyazyla Sulaiman and co-authors test, in a 2025 study, spotting three weed species in rice fields from a drone carrying a special camera. Broadleaf weeds choke young rice exactly when competition hurts most. Finding them early, by species, guides targeted spraying.
Flights over a one-hectare Malaysian plot used a hyperspectral camera, which records many narrow color bands. Machine learning classifiers learned each weed's spectral signature. The three species differ subtly to the eye but more clearly across wavelengths. The drone flew at forty meters over a field known for heavy weed pressure. Species-level maps could cut herbicide use by treating patches, not whole fields.
Catalog record
| CREATOR | Nursyazyla Sulaiman, Nik Norasma Che’Ya, Abdul Shukor Juraimi, Nisfariza Mohd Noor, Rhushalshafira Rosle, Muhammad Huzaifah Mohd Roslim |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 66 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |