Dataset Information

Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods.

ABSTRACT: The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms. However, these methods have generally been calibrated and validated on limited datasets. High variability in observational conditions, genotypic differences, development stages, and head orientation makes wheat head detection a challenge for computer vision. Further, possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex. Through a joint international collaborative effort, we have built a large, diverse, and well-labelled dataset of wheat images, called the Global Wheat Head Detection (GWHD) dataset. It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes. Guidelines for image acquisition, associating minimum metadata to respect FAIR principles, and consistent head labelling methods are proposed when developing new head detection datasets. The GWHD dataset is publicly available at http://www.global-wheat.com/and aimed at developing and benchmarking methods for wheat head detection.

SUBMITTER: David E

PROVIDER: S-EPMC7706323 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods.

David Etienne E Madec Simon S Sadeghi-Tehran Pouria P Aasen Helge H Zheng Bangyou B Liu Shouyang S Kirchgessner Norbert N Ishikawa Goro G Nagasawa Koichi K Badhon Minhajul A MA Pozniak Curtis C de Solan Benoit B Hund Andreas A Chapman Scott C SC Baret Frédéric F Stavness Ian I Guo Wei W

Plant phenomics (Washington, D.C.) 20200820

The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms. However, these methods have generally been calibrated and validated on limited datasets. High variability in observational conditions, gen ...[more]

PMID: 33313551

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Project description:Background:The number of ears per unit ground area (ear density) is one of the main agronomic yield components in determining grain yield in wheat. A fast evaluation of this attribute may contribute to monitoring the efficiency of crop management practices, to an early prediction of grain yield or as a phenotyping trait in breeding programs. Currently the number of ears is counted manually, which is time consuming. Moreover, there is no single standardized protocol for counting the ears. An automatic ear-counting algorithm is proposed to estimate ear density under field conditions based on zenithal color digital images taken from above the crop in natural light conditions. Field trials were carried out at two sites in Spain during the 2014/2015 crop season on a set of 24 varieties of durum wheat with two growing conditions per site. The algorithm for counting uses three steps: (1) a Laplacian frequency filter chosen to remove low and high frequency elements appearing in an image, (2) a Median filter to reduce high noise still present around the ears and (3) segmentation using Find Maxima to segment local peaks and determine the ear count within the image. Results:The results demonstrate high success rate (higher than 90%) between the algorithm counts and the manual (image-based) ear counts, and precision, with a low standard deviation (around 5%). The relationships between algorithm ear counts and grain yield was also significant and greater than the correlation with manual (field-based) ear counts. In this approach, results demonstrate that automatic ear counting performed on data captured around anthesis correlated better with grain yield than with images captured at later stages when the low performance of ear counting at late grain filling stages was associated with the loss of contrast between canopy and ears. Conclusions:Developing robust, low-cost and efficient field methods to assess wheat ear density, as a major agronomic component of yield, is highly relevant for phenotyping efforts towards increases in grain yield. Although the phenological stage of measurements is important, the robust image analysis algorithm presented here appears to be amenable from aerial or other automated platforms.

Dataset Information

Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods.

Publications

Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods.

Similar Datasets

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