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Plant Phenomics logoLink to Plant Phenomics
. 2021 Sep 22;2021:9846158. doi: 10.34133/2021/9846158

Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods

Etienne David 1,2,, Mario Serouart 1,2, Daniel Smith 3, Simon Madec 1,3, Kaaviya Velumani 2,4, Shouyang Liu 5, Xu Wang 6, Francisco Pinto 7, Shahameh Shafiee 8, Izzat S A Tahir 9, Hisashi Tsujimoto 10, Shuhei Nasuda 11, Bangyou Zheng 12, Norbert Kirchgessner 13, Helge Aasen 13, Andreas Hund 13, Pouria Sadhegi-Tehran 14, Koichi Nagasawa 15, Goro Ishikawa 16, Sébastien Dandrifosse 17, Alexis Carlier 17, Benjamin Dumont 18, Benoit Mercatoris 17, Byron Evers 6, Ken Kuroki 19, Haozhou Wang 19, Masanori Ishii 19, Minhajul A Badhon 20, Curtis Pozniak 21, David Shaner LeBauer 22, Morten Lillemo 8, Jesse Poland 6, Scott Chapman 3,12, Benoit de Solan 1, Frédéric Baret 2, Ian Stavness 20, Wei Guo 19
PMCID: PMC8548052  PMID: 34778804

Abstract

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version.

1. Introduction

Quality training data is essential for the deployment of deep learning (DL) techniques to get a general model that can scale on all the possible cases. Increasing dataset size, diversity, and quality is expected to be more efficient than increasing network complexity and depth [1]. Datasets like ImageNet [2] for classification or MS COCO [3] for instance detection are crucial for researchers to develop and rigorously benchmark new DL methods. Similarly, the importance of getting plant- or crop-specific datasets is recognized within the plant phenotyping community ([410], p. 2, [1113]). These datasets allow benchmarking the algorithm performances used to estimate phenotyping traits while encouraging computer vision experts to further improvement ([10], p. 2, [1417]). The emergence of affordable RGB cameras and platforms, including UAVs and smartphones, makes in-field image acquisition easily accessible. These high-throughput methods are progressively replacing manual measurement of important traits such as wheat head density. Wheat is a crop grown worldwide, and the number of heads per unit area is one of the main components of yield potential. Creating a robust deep learning model performing over all the situations requires a dataset of images covering a wide range of genotypes, sowing density and pattern, plant state and stage, and acquisition conditions. To answer this need for a large and diverse wheat head dataset with consistent and quality labeling, we developed in 2020 the Global Wheat Head Detection (GWHD_2020) [18] that was used to benchmark methods proposed in the computer vision community and recommend best practices to acquire images and keep track of the metadata.

The GWHD_2020 dataset results from the harmonization of several datasets coming from nine different institutions across seven countries and three continents. There are already 27 publications [1945] (accessed July 2021) that have reported their wheat head detection model using the GWHD_2020 dataset as the standard for training/testing data. A “Global Wheat Detection” competition hosted by Kaggle was also organized, attracting 2245 teams across the world [14], leading to improvements in wheat head detection models [23, 25, 31, 41]. However, issues with the GWHD_2020 dataset were detected during the competition, including labeling noise and an unbalanced test dataset.

To provide a better benchmark dataset for the community, the GWHD_2021 dataset was organized with the following improvements: (1) the GWHD_2020 dataset was checked again to eliminate few poor-quality images, (2) images were re-labeled to avoid consistency issues, (3) a wider range of developmental stages from the GWHD_2020 sites was included, and (4) datasets from 5 new countries (the USA, Mexico, Republic of Sudan, Norway, and Belgium) were added. The resulting GWHD_2021 dataset contains 275,187 wheat heads from 16 institutions distributed across 12 countries.

2. Materials and Methods

The first version of GWHD_2020, used for the Kaggle competition, was divided into several subdatasets. Each subdataset represented all images from one location, acquired with one sensor while mixing several stages. However, wheat head detection models may be sensitive to the developmental stage and acquisition conditions: at the beginning of head emergence, a part of the head is barely visible because it is still not fully out from the last leaf sheath and possibly masked by the awns. Further, during ripening, wheat heads tend to bend and overlap, leading to more erratic labeling. A redefinition of the subdataset was hence necessary to help investigate the effect of the developmental stage on model performances. The new definition of a subdataset was then formulated as “a consistent set of images acquired over the same experimental unit, during the same acquisition session with the same vector and sensor.” A subdataset defines therefore a domain. This new definition forced to split the original GWHD_2020 subdatasets into several smaller ones. The UQ_1 was split into 6 much smaller subdatasets, Arvalis_1 was split into 3 subdatasets, Arvalis_3 into 2 subdatasets, and utokyo_1 into 2 subdatasets. However, in the case of utokyo_2 which was a collection of images taken by farmers at different stages and in different fields, the original subdataset was kept. Overall, the 11 original subdatasets in GWHD_2020 were distributed into 19 subdatasets for GWHD_2021.

Almost 2000 new images were added to GWHD_2020, constituting a major improvement. A part of the new images comes from the institutions already contributing to GWHD_2020 and was collected during a different year and/or at a different location. This was the case for Arvalis (Arvalis_7 to Arvalis_12), University of Queensland (UQ_7 to UQ_11), Nanjing Agricultural University (NAU_2 and NAU_3), and University of Tokyo (Utokyo_1). In addition, 14 new subdatasets were included, coming from 5 new countries: Norway (NMBU), Belgium (Université of Liège [46]), United States of America (Kansas State University [47], TERRA-REF [7]), Mexico (CIMMYT), and Republic of Sudan (Agricultural Research Council). All these images were acquired at a ground sampling distance between 0.2 and 0.4 mm, i.e., similar to that of the images in GWHD_2020. Because none of them was already labeled, a sample was selected by taking no more than one image per microplot, which was randomly cropped to 1024 × 1024 px patches that will be called images in the following for the sake of simplicity.

With the addition of 1722 images and 86,000 wheat heads, the GWHD_2021 dataset contains 6500 images and 275,000 wheat heads. The increase in the number of subdatasets from 18 to 47 leads to a larger diversity between them which can be observed on Figure 1. The subdatasets are described in Table 1. However, the new definition of a subdataset led also to more unbalanced subdatasets: the smallest (Arvalis_8) contains only 20 images, while the biggest (ETHZ_1) contains 747 images. This provides the opportunity to possibly take advantage of the data distribution to improve model training. Each subdataset has been visually assigned to several development stage classes depending on the respective color of leaves and heads (Figure 2): postflowering, filling, filling-ripening, and ripening. Examples of the different stages are presented in Figure 2. While being approximative, this metadata is expected to improve model training.

Figure 1.

Figure 1

Sample images of the Global Wheat Head Detection 2021. The blue boxes correspond to the interactively labeled heads.

Table 1.

The subdatasets for GWHD_2020 and GWHD_2021. The column “2020 name” indicates the name given to the subdatasets for GWHD_2020, which were split into several new subdatasets.

GWHD_2021 subdataset name GWHD_2020 subdataset name Owner Country Location Acquisition date Platform Development stage Number of images Number of wheat head
Ethz_1 ethz_1 ETHZ Switzerland Usask 06/06/2018 Spidercam Filling 747 49603
Rres_1 rres_1 Rothamsted UK Rothamsted 13/07/2015 Gantry Filling-ripening 432 19210
ULiège-GxABT_1 Uliège/Gembloux Belgium Gembloux 28/07/2020 Cart Ripening 30 1847
NMBU_1 NMBU Norway NMBU 24/07/2020 Cart Filling 82 7345
NMBU_2 NMBU Norway NMBU 07/08/2020 Cart Ripening 98 5211
Arvalis_1 arvalis_1 Arvalis France Gréoux 02/06/2018 Handheld Postflowering 66 2935
Arvalis_2 arvalis_1 Arvalis France Gréoux 16/06/2018 Handheld Filling 401 21003
Arvalis_3 arvalis_1 Arvalis France Gréoux 07/2018 Handheld Filling-ripening 588 21893
Arvalis_4 arvalis_2 Arvalis France Gréoux 27/05/2019 Handheld Filling 204 4270
Arvalis_5 arvalis_3 Arvalis France VLB 06/06/2019 Handheld Filling 448 8180
Arvalis_6 arvalis_3 Arvalis France VSC 26/06/2019 Handheld Filling-ripening 160 8698
Arvalis_7 Arvalis France VLB 06/2019 Handheld Filling-ripening 24 1247
Arvalis_8 Arvalis France VLB 06/2019 Handheld Filling-ripening 20 1062
Arvalis_9 Arvalis France VLB 06/2020 Handheld Ripening 32 1894
Arvalis_10 Arvalis France Mons 10/06/2020 Handheld Filling 60 1563
Arvalis_11 Arvalis France VLB 18/06/2020 Handheld Filling 60 2818
Arvalis_12 Arvalis France Gréoux 15/06/2020 Handheld Filling 29 1277
Inrae_1 inrae_1 INRAe France Toulouse 28/05/2019 Handheld Filling-ripening 176 3634
Usask_1 usask_1 USaskatchewan Canada Saskatchewan 06/06/2018 Tractor Filling-ripening 200 5985
KSU_1 Kansas State University US KSU 19/05/2016 Tractor Postflowering 100 6435
KSU_2 Kansas State University US KSU 12/05/2017 Tractor Postflowering 100 5302
KSU_3 Kansas State University US KSU 25/05/2017 Tractor Filling 95 5217
KSU_4 Kansas State University US KSU 25/05/2017 Tractor Ripening 60 3285
Terraref_1 TERRA-REF project US Maricopa, AZ 02/04/2020 Gantry Ripening 144 3360
Terraref_2 TERRA-REF project US Maricopa, AZ 20/03/2020 Gantry Filling 106 1274
CIMMYT_1 CIMMYT Mexico Ciudad Obregon 24/03/2020 Cart Postflowering 69 2843
CIMMYT_2 CIMMYT Mexico Ciudad Obregon 19/03/2020 Cart Postflowering 77 2771
CIMMYT_3 CIMMYT Mexico Ciudad Obregon 23/03/2020 Cart Postflowering 60 1561
Utokyo_1 utokyo_1 UTokyo Japan NARO-Tsukuba 22/05/2018 Cart ¯ Ripening 538 14185
Utokyo_2 utokyo_1 UTokyo Japan NARO-Tsukuba 22/05/2018 Cart ¯ Ripening 456 13010
Utokyo_3 utokyo_2 UTokyo Japan NARO-Hokkaido Multi-years ¯ Handheld Multiple 120 3085
Ukyoto_1 UKyoto Japan Kyoto 30/04/2020 Handheld Postflowering 60 2670
NAU_1 NAU_1 NAU China Baima n.a Handheld Postflowering 20 1240
NAU_2 NAU China Baima 02/05/2020 Cart Postflowering 100 4918
NAU_3 NAU China Baima 09/05/2020 Cart Filling 100 4596
UQ_1 uq_1 UQueensland Australia Gatton 12/08/2015 Tractor Postflowering 22 640
UQ_2 uq_1 UQueensland Australia Gatton 08/09/2015 Tractor Postflowering 16 39
UQ_3 uq_1 UQueensland Australia Gatton 15/09/2015 Tractor Filling 14 297
UQ_4 uq_1 UQueensland Australia Gatton 01/10/2015 Tractor Filling 30 1039
UQ_5 uq_1 UQueensland Australia Gatton 09/10/2015 Tractor Filling-ripening 30 3680
UQ_6 uq_1 UQueensland Australia Gatton 14/10/2015 Tractor Filling-ripening 30 1147
UQ_7 UQueensland Australia Gatton 06/10/2020 Handheld Ripening 17 1335
UQ_8 UQueensland Australia McAllister 09/10/2020 Handheld Ripening 41 4835
UQ_9 UQueensland Australia Brookstead 16/10/2020 Handheld Filling-ripening 33 2886
UQ_10 UQueensland Australia Gatton 22/09/2020 Handheld Filling-ripening 53 8629
UQ_11 UQueensland Australia Gatton 31/08/2020 Handheld Postflowering 42 4345
ARC_1 ARC Sudan Wad Medani 03/2021 Handheld Filling 30 888
Total 6515 275187

VLB: Villiers le Bâcle; VSC: Villers-Saint-Christophe. ∗∗Utokyo_1 and Utokyo_2 were taken at the same location with different sensors. ∗∗∗Utokyo_3 is a special subdataset made from images coming from a large variety of farmers in Hokaido between 2016 and 2019. Italic: Europe: bold: North America; underline: Asia; bold italic: Oceania; bold underline: Africa.

Figure 2.

Figure 2

Distribution of the development stage. The x-axis presents the number of subdataset per development stage.

3. Dataset Diversity Analysis

In comparison to GWHD_2020, the GWHD_2021 dataset puts emphasis on metadata documentation of the different subdatasets, as described in the discussion section of David et al. [18]. Alongside the acquisition platform, each subdataset has been reviewed and a development stage was assigned to each, except for Utokyo_3 (formerly utokyo_2) as it is a collection of images from various farmer fields and development stages. Globally, the GWHD_2021 dataset covers well all development stages ranging from postanthesis to ripening (Figure 2).

The diversity between images within the GWHD_2021 dataset was documented using the method proposed by Tolias et al. [48]. The deep learning image features were first extracted from the VGG-16 deep network pretrained on the ImageNet dataset that is considered representing well the general features of RGB images. We then selected the last layer which has a size of 14 × 14 × 512 and summed it into a unique vector of 512 channels, which is then normalized. Then, the UMAP dimentionality reduction algorithm [49] was used to project representations into a 2D space. The UMAP algorithm is used to keep the existing clusters during the projection to a low-dimension space. This 2D space is expected to capture the main features of the images. Results (Figure 3) demonstrate that the test dataset used for GWHD_2020 was biased in comparison to the training dataset. The subdatasets added in 2021 populate more evenly the 2D space which is expected to improve the robustness of the models.

Figure 3.

Figure 3

Distribution of the images in the two first dimensions defined by the UMAP algorithm for the GWHD 2021 dataset. The additional subdatasets as well as the training and test datasets from GWHD_2020 are represented by colors.

4. Presentation of Global Wheat Challenge 2021 (GWC 2021)

The results from the Kaggle challenge based on GWHD_2020 have been analyzed by the authors [14]. The findings emphasize that the design of a competition is critical to enable solutions that improve the robustness of the wheat head detection models. The Kaggle competition was based on a metric that was averaged across all test images, without distinction for the subdatasets, and it was biased toward a strict match of the labelling. This artificially enhances the influence on the global score of the largest datasets such as utokyo_1 (now split into Utokyo_1 and Utokyo_2). Further, the metrics used to score the agreement with the labeled heads and largely used for big datasets, such as MS COCO, appear to be less efficient when some heads are labeled in a more uncertain way as it was the case in several situations depending on the development stage, illumination conditions, and head density. As a result, the weighted domain accuracy is proposed as a new metric [14]. The accuracy computed over image i belonging to domain d, AId(i), is classically defined as

AIdi=TPTP+FN+FP, (1)

where TP, FN, and FP are, respectively, the number of true positive, false negative, and false positive found in image i. The weighted domain accuracy (WDA) is the weighted average of all domain accuracies:

WDA=1Dd=1D1ndi=1ndAIdi, (2)

where D is the number of domains (subdatasets) and nd is the number of images in domain d. The training, validation, and test datasets used are presented in Section 5.

The results of the Global Wheat Challenge 2021 are summarized in Table 2. The reference method is a faster-RCN with the same parameters than in the research paper GWHD_2020 [18] and trained on the GWHD_2021 (Global Wheat Challenge 2021 split) training dataset. The full leaderboard can be found at https://www.aicrowd.com/challenges/global-wheat-challenge-2021/leaderboards.

Table 2.

Presentation of the Global Wheat Challenge 2021 results.

Solution name WDA
randomTeamName (1st place) 0.700
David_jeon (2nd place) 0.695
SMART (2nd place) 0.695
Reference (faster-RCNN) 0.492

5. How to Use/FAQ

  1. How to download? The dataset can be download on Zenodo: https://zenodo.org/record/5092309

  2. What is the license of the dataset? The dataset is under the MIT license, allowing for reuse without restriction

  3. How to cite the dataset? The present paper can be cited when using the GWHD_2021 dataset. However, cite preferentially [18] for wheat head detection challenges or when discussing the difficulty to constitute a large datasets

  4. How to benchmark? Depending on the objectives of the study, we recommend two sets of training, validation, and test (Table 3):

  1. The Global Wheat Challenge 2021 split when the dataset is used for phenotyping purpose, to allow direct comparison with the winning solutions

  2. The “GlobalWheat-WILDS” split is the one used for the WILDS paper [50]. We recommand to use the GlobalWheat-WILDS split when working on out-of-domain distribution shift problems

Table 3.

Presentation of the different splits which can be used with the GWHD_2021.

Splits Training Validation Test
Global Wheat Challenge 2021 Ethz_1, Rres_1, Inrae_1, Arvalis (all), NMBU (all), ULiège-GxABT (all) UQ_1 to UQ_6, Utokyo (all), NAU_1, Usask_1 UQ_7 to UQ_12, Ukyoto_1, NAU_2 and NAU_3, ARC_1, CIMMYT (all), KSU (all), Terraref (all)
GlobalWheat-WILDS Ethz_1, Rres_1, Inrae_1, Arvalis (all), NMBU (all), ULiège-GxABT (all) UQ (all), Utokyo (all), Ukyoto_1, NAU (all) CIMMYT (all), KSU (all); Terraref (all), Usask_1, ARC_1

It is further recommended to keep the weighted domain accuracy for comparison with previous works.

6. Conclusion

The second edition of the Global Wheat Head Detection, GWHD_2021, alongside the organization of a second Global Wheat Challenge is an important step for illustrating the usefulness of open and shared data across organizations to further improve high-throughput phenotyping methods. In comparison to the GWHD_2020 dataset, it represents five new countries, 22 new subdatasets, 1200 new images, and 120,000 new-labeled wheat heads. Its revised organization and additional diversity are more representative of the type of images researchers and agronomists can acquire across the world. The revised metrics used to evaluate the models during the Global Wheat Challenge 2021 can help researchers to benchmark one-class localization models on a large range of acquisition conditions. GWHD_2021 is expected to accelerate the building of robust solutions. However, progress on the representation of developing countries is still expected and we are open to new contributions from South America, Africa, and South Asia. We started to include nadir view photos from smartphones, to get a more comprehensive dataset and train reliable models for such affordable devices. Additional works are required to adapt such an approach to other vectors such as a camera mounted on unmanned aerial vehicle, or other high-resolution cameras working in other spectral domains. Further, it is planned to release wheat head masks alongside the bounding box given the very large number of boxes that already exists and provides more associated metadata.

Acknowledgments

We would like to thank the company “Human in the loop”, which corrected and labeled the new datasets. The help of Frederic Venault (INRAe Avignon) was also precious to check the labelled images. The work received support from ANRT for the CIFRE grant of Etienne David, cofunded by Arvalis for the project management. The labelling work was supported by several companies and projects, including Canada: The Global Institute Food Security, University of Saskatchewan which supported the organization of the competition. France: This work was supported by the French National Research Agency under the Investments for the Future Program, referred as ANR-16-CONV-0004 PIA #Digitag. Institut Convergences Agriculture Numérique, Hiphen supported the organization of the competition. Japan: Kubota supported the organization of the competition. Australia: Grains Research and Development Corporation (UOQ2002-008RTX machine learning applied to high-throughput feature extraction from imagery to map spatial variability and UOQ2003-011RTX INVITA—a technology and analytics platform for improving variety selection) supported competition.

Data Availability

The dataset is available on Zenodo (https://zenodo.org/record/5092309).

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this article.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The dataset is available on Zenodo (https://zenodo.org/record/5092309).


Articles from Plant Phenomics are provided here courtesy of American Association for the Advancement of Science (AAAS) and Nanjing Agricultural University

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