ArticleScientific data2024
Manually annotated and curated Dataset of diverse Weed Species in Maize and Sorghum for Computer Vision.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The trial behind it
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Who cites it
5 citing papers in PubMed.
- A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation.Scientific data · 2026Article
- Predictive modeling, pattern recognition, and spatiotemporal representations of plant growth in simulated and controlled environments: A comprehensive review.Plant phenomics (Washington, D.C.) · 2025Review
- WeedSwin hierarchical vision transformer with SAM-2 for multi-stage weed detection and classification.Scientific reports · 2025Article
- Deep-learning-ready RGB-depth images of seedling development.Plant methods · 2025Article
- Manually annotated and curated Dataset of diverse Weed Species in Maize and Sorghum for Computer Vision.Scientific data · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Sustainable weed management strategies are critical to feeding the world's population while preserving ecosystems and biodiversity. Therefore, site-specific weed control strategies based on automation are needed to reduce the additional time and effort required for weeding. Machine vision-based methods appear to be a promising approach for weed detection, but require high quality data on the species in a specific agricultural area. Here we present a dataset, the Moving Fields Weed Dataset (MFWD), which captures the growth of 28 weed species commonly found in sorghum and maize fields in Germany. A total of 94,321 images were acquired in a fully automated, high-throughput phenotyping facility to track over 5,000 individual plants at high spatial and temporal resolution. A rich set of manually curated ground truth information is also provided, which can be used not only for plant species classification, object detection and instance segmentation tasks, but also for multiple object tracking.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.