ArticlePlant methods2024
Harnessing UAVs and deep learning for accurate grass weed detection in wheat fields: a study on biomass and yield implications.
Article in Plant methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Deep learning-based temporal change detection of broadleaved weed infestation in rice fields using UAV multispectral imagery.Frontiers in plant science · 2025Article
- Vision-language models for zero-shot weed detection and visual reasoning in UAV-based precision agriculture.Frontiers in plant science · 2025Article
- Advancing hyperspectral imaging techniques for root systems: a new pipeline for macro- and microscale image acquisition and classification.Plant methods · 2024Article
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Authors and funding
9 authors.
Funding
Abstract
Weeds are undesired plants competing with crops for light, nutrients, and water, negatively impacting crop growth. Identifying weeds in wheat fields accurately is important for precise pesticide spraying and targeted weed control. Grass weeds in their early growth stages look very similar to wheat seedlings, making them difficult to identify. In this study, we focused on wheat fields with varying levels of grass weed infestation and used unmanned aerial vehicles (UAVs) to obtain images. By utilizing deep learning algorithms and spectral analysis technology, the weeds were identified and extracted accurately from wheat fields. Our results showed that the precision of weed detection in scattered wheat fields was 91.27% and 87.51% in drilled wheat fields. Compared to areas without weeds, the increase in weed density led to a decrease in wheat biomass, with the maximum biomass decreasing by 71%. The effect of weed density on yield was similar, with the maximum yield decreasing by 4320 kg·ha
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