ArticleData in brief2025
Drone imagery dataset for early-season weed classification in maize and tomato crops.
Article in Data in brief, 2025. 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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3 citing papers in PubMed.
- A dataset of aligned RGB and multispectral UAV imagery for semantic segmentation of weedy rice.Data in brief · 2025Review
- Dataset for weed detection in fruit orchards.Data in brief · 2025Article
- Drone imagery dataset for early-season weed classification in maize and tomato crops.Data in brief · 2025Article
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4 authors.
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Abstract
Identifying weed species at early-growth stages is critical for precision agriculture. Accurate classification at the species-level enables targeted control measures, significantly reducing pesticide use. This paper presents a dataset of RGB images captured with a Sony ILCE-6300L camera mounted on an unmanned aerial vehicle (UAV) flying at an altitude of 11 m above ground level. The dataset covers various agricultural fields in Spain, focusing on two summer crops: maize and tomato. It is designed to enhance early-season weed classification accuracy by including images from two phenological stages. Specifically, the dataset contains 31,002 labeled images from the early-growth stage-maize with four unfolded leaves (BBCH14) and tomato with the first flower bud visible (BBCH501)-as well as 36,556 images from a more advanced-growth stage-maize with seven unfolded leaves (BBCH17) and tomato with the ninth flower bud visible (BBCH509). In maize, the weed species include
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