Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods
This provides a more robust benchmark for computer vision methods in agricultural science, though it is incremental as it builds on an existing dataset.
The authors tackled the problem of limited diversity and size in wheat head localization datasets by expanding the Global Wheat Head Detection (GWHD) dataset from 2020, adding 1,722 images and 81,553 wheat heads from 5 additional countries, resulting in a bigger, more diverse, and less noisy 2021 version.
The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4,700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience in 2020, a few avenues for improvements have been identified, especially from the perspective of data size, head diversity and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and augmented by adding 1,722 images from 5 additional countries, allowing for 81,553 additional wheat heads to be added. We now release a new version of the Global Wheat Head Detection (GWHD) dataset in 2021, which is bigger, more diverse, and less noisy than the 2020 version. The GWHD 2021 is now publicly available at http://www.global-wheat.com/ and a new data challenge has been organized on AIcrowd to make use of this updated dataset.