Mapping spatial distribution of herbicide-resistant weeds using remote sensing technology

Abstract:

In a single winter wheat field near Jena, Germany, herbicide-resistant Amaranthus retroflexus patches expanded from 8% of the cropped area to 23% over just four seasons—yet the problem went unnoticed until harvest losses became obvious. This research tested whether remote sensing could detect and map herbicide-resistant weed populations before they reach economically damaging densities. Three sensing platforms—RGB drone imagery, multispectral drone, and ground-based hyperspectral scanning—were compared across 18 cereal fields in Thuringia between April 2022 and September 2024. Hyperspectral sensing achieved the highest overall classification accuracy (89.4%), correctly distinguishing resistant from susceptible weed biotypes in 84.3-93.2% of ground-truth points depending on species. Multispectral imaging reached 78.8% accuracy, while standard RGB drone imagery managed only 62.7%. Resistant populations were concentrated in intensively managed zones (43.7% of weed patches) compared with rotated fields (18.6%). These findings support integrating hyperspectral or multispectral sensing into precision weed management to target resistant populations before they spread.