Manually annotated and curated Dataset of diverse Weed Species in Maize and Sorghum for Computer Vision.

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 manuall

SUBMITTER: Genze N 

PROVIDER: S-EPMC10805845 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature