Global Wheat Head Detection 2021: an improved dataset for benchmarking wheat head detection methods

[en] 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.

Disciplines :

Agriculture & agronomy
Engineering, computing & technology: Multidisciplinary, general & others

Author, co-author :

David, Etienne

Serouart, Mario

Smith, Daniel

Madec, Simon

Velumani, Kaaviya

Liu, Shouyang

Wang, Xu

Pinto, Francisco

Shafiee, Shahameh

Tahir, Izzat S. A.

More authors (27 more)

Language :

English

Title :

Global Wheat Head Detection 2021: an improved dataset for benchmarking wheat head detection methods

Publication date :

2021

Journal title :

Plant Phenomics

eISSN :

2643-6515

Publisher :

AAAS

Volume :

2021

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://spj.sciencemag.org/journals/plantphenomics/2021/9846158/

Available on ORBi :

since 02 December 2021

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Bibliography

N. Sambasivan, S. Kapania, H. Highfill, D. Akrong, P. Paritosh, and L. M. Aroyo, Everyone wants to do the model work, not the data work: Data cascades in high-stakes AI, New York, NY, USA, 2021.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "ImageNet: A large-scale hierarchical image database, " in 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248-255, 2009.
T.-Y. Lin et al., "Microsoft coco: Common objects in context, " European conference on computer vision, pp. 740-755, 2014.
J. A. Cruz, X. Yin, X. Liu et al., "Multi-modality imagery database for plant phenotyping, " Machine Vision and Applications, vol. 27, no. 5, pp. 735-749, 2016.
W. Guo, B. Zheng, A. B. Potgieter et al., "Aerial imagery analysis - quantifying appearance and number of sorghum heads for applications in breeding and agronomy, " Frontiers in Plant Science, vol. 9, p. 1544, 2018.
D. P. Hughes andM. Salathé, "An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowdsourcing, " CoRR, 2015, http://arxiv.org/abs/1511.08060.
D. LeBauer et al., "Data from: TERRA-REF, an open reference data set from high resolution genomics, phenomics, and imaging sensors, " Dryad, p. 800302508 bytes, 2020.
S. Leminen Madsen, S. K. Mathiassen, M. Dyrmann, M. S. Laursen, L. C. Paz, and R. N. Jørgensen, "Open plant phenotype database of common weeds in Denmark, " Remote Sensing, vol. 12, no. 8, p. 1246, 2020.
H. Lu, Z. Cao, Y. Xiao, B. Zhuang, and C. Shen, "TasselNet: Counting maize tassels in the wild via local counts regression network, " Plant Methods, vol. 13, no. 1, p. 79, 2017.
S. Madec, K. Irfan, E. David et al., The P2S2 segmentation dataset: Annotated in-field multi-crop RGB images acquired under various conditions, Lyon, France, 2019https://hal.inrae.fr/hal-03140124.
H. Scharr, M. Minervini, A. P. French et al., "Leaf segmentation in plant phenotyping: A collation study, " Machine Vision and Applications, vol. 27, no. 4, pp. 585-606, 2016.
R. Thapa, K. Zhang, N. Snavely, S. Belongie, and A. Khan, "The Plant Pathology challenge 2020 data set to classify foliar disease of apples, " Applications in Plant Sciences, vol. 8, no. 9, article e11390, 2020.
T. Wiesner-Hanks, E. L. Stewart, N. Kaczmar et al., "Image set for deep learning: Field images of maize annotated with disease symptoms, " BMC Research Notes, vol. 11, no. 1, p. 440, 2018.
E. David, F. Ogidi, W. Guo, F. Baret, and I. Stavness, Global Wheat Challenge 2020: Analysis of the competition design and winning models, 2021.
N. Hani, P. Roy, and V. Isler, "MinneApple: A benchmark dataset for apple detection and segmentation, " IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 852-858, 2020.
M. Minervini, A. Fischbach, H. Scharr, and S. A. Tsaftaris, "Finely-grained annotated datasets for image-based plant phenotyping, " Pattern Recognition Letters, vol. 81, pp. 80-89, 2016.
S. A. Tsaftaris and H. Scharr, "Sharing the right data right: A symbiosis with machine learning, " Trends in Plant Science, vol. 24, no. 2, pp. 99-102, 2019.
E. David, S. Madec, P. Sadeghi-Tehran et al., "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, " Plant Phenomics, vol. 2020, article 3521852, 12 pages, 2020.
G. Yu, Y. Wu, J. Xiao, and Y. Cao, "A novel pyramid network with feature fusion and disentanglement for object detection, " Computational Intelligence and Neuroscience, vol. 2021, Article ID 6685954, 13 pages, 2021.
T. W. Ayalew, J. R. Ubbens, and I. Stavness, "Unsupervised domain adaptation for plant organ counting, " European conference on computer vision, pp. 330-346, 2020.
M. N. Datta, Y. Rathi, and M. Eliazer, "Wheat heads detection using deep learning algorithms, " Annals of the Romanian Society for Cell Biology, pp. 5641-5654, 2021.
F. Fourati, W. S. Mseddi, and R. Attia, "Wheat head detection using deep, semi-supervised and ensemble learning, " Canadian Journal of Remote Sensing, vol. 47, no. 2, pp. 198-208, 2021.
F. Fourati, W. Souidene, and R. Attia, "An original framework for wheat head detection using deep, semi-supervised and ensemble learning within Global Wheat Head Detection (GWHD) dataset, " 2020, https://arxiv.org/abs/2009.11977.
A. S. Gomez, E. Aptoula, S. Parsons, and P. Bosilj, "Deep regression versus detection for counting in robotic phenotyping, " IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2902-2907, 2021.
B. Gong, D. Ergu, Y. Cai, and B. Ma, "Real-time detection for wheat head applying deep neural network, " Sensors, vol. 21, no. 1, p. 191, 2021.
M.-X. He, P. Hao, and Y. Z. Xin, "A robust method for wheatear detection using UAV in natural scenes, " IEEE Access, vol. 8, pp. 189043-189053, 2020.
Y. Jiang, C. Li, R. Xu, S. Sun, J. S. Robertson, and A. H. Paterson, "DeepFlower: A deep learning-based approach to characterize flowering patterns of cotton plants in the field, " Plant Methods, vol. 16, no. 1, p. 156, 2020.
B. Jiang, J. Xia, and S. Li, "Few training data for objection detection, " in Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering, pp. 579-584, November 2020.
A. Karwande, P. Kulkarni, P. Marathe, T. Kolhe, M. Wyawahare, and P. Kulkarni, "Computer vision-based wheat grading and breed classification system: A design approach, " Machine Learning and Information Processing: Proceedings of ICMLIP 2020, Springer, p. 403, 2021.
T. Kattenborn, J. Leitloff, F. Schiefer, and S. Hinz, "Review on convolutional neural networks (CNN) in vegetation remote sensing, " ISPRS Journal of Photogrammetry and Remote Sensing, vol. 173, pp. 24-49, 2021.
S. Khaki, N. Safaei, H. Pham, and L. Wang, "WheatNet: A lightweight convolutional neural network for high-throughput image-based wheat head detection and counting, " 2021, https://arxiv.org/abs/2103.09408.
S. U. Kolhar and J. Jagtap, Bibliometric Review on Image Based Plant Phenotyping16.
J. Li, C. Li, S. Fei et al., "Wheat ear recognition based on RetinaNet and transfer learning, " Sensors, vol. 21, no. 14, p. 4845, 2021.
L. Lucks, L. Haraké, and L. Klingbeil, Detektion von Weizenähren mithilfe neuronaler Netze und synthetisch erzeugter Trainingsdaten, tm-Technisches Messen, 2021.
T. Misra, A. Arora, S. Marwaha et al., "Web-SpikeSegNet: Deep learning framework for recognition and counting of spikes from visual images of wheat plants, " IEEE Access, vol. 9, pp. 76235-76247, 2021.
L. G. Riera, M. E. Carroll, Z. Zhang et al., "Deep multiview image fusion for soybean yield estimation in breeding applications, " Plant Phenomics, vol. 2021, article 9846470, 12 pages, 2021.
D. T. Smith, A. B. Potgieter, and S. C. Chapman, "Scaling up high-throughput phenotyping for abiotic stress selection in the field, " Theoretical and Applied Genetics, vol. 134, no. 6, pp. 1845-1866, 2021.
Y. Suzuki, D. Kuyoshi, and S. Yamane, "Transfer learning algorithm for object detection, " Bulletin of Networking, Computing, Systems, and Software, vol. 10, no. 1, pp. 1-3, 2021.
R. Trevisan, O. Pérez, N. Schmitz, B. Diers, and N. Martin, "High-throughput phenotyping of soybean maturity using time series UAV imagery and convolutional neural networks, " Remote Sensing, vol. 12, no. 21, p. 3617, 2020.
K. Velumani, R. Lopez-Lozano, S. Madec et al., "Estimates of maize plant density from UAV RGB images using Faster-RCNN detection model: Impact of the spatial resolution, " 2021, https://arxiv.org/abs/2105.11857.
Y. Wu, Y. Hu, and L. Li, "BTWD: Bag of tricks for wheat detection, " in European Conference on Computer Vision, pp. 450-460, Springer, 2020.
H. Wang, Y. Duan, Y. Shi, Y. Kato, S. Ninomiya, and W. Guo, "EasyIDP: A Python package for intermediate data processing in UAV-based plant phenotyping, " Remote Sensing, vol. 13, no. 13, p. 2622, 2021.
Y. Wang, Y. Qin, and J. Cui, "Occlusion robust wheat ear counting algorithm based on deep learning, " Frontiers in Plant Science, vol. 12, p. 1139, 2021.
B. Yang, Z. Gao, Y. Gao, and Y. Zhu, "Rapid detection and counting of wheat ears in the field using YOLOv4 with attention module, " Agronomy, vol. 11, no. 6, p. 1202, 2021.
H. Lu, L. Liu, Y. N. Li, X. M. Zhao, X. Q. Wang, and Z. G. Cao, "TasselNetV3: Explainable Plant Counting With Guided Upsampling and Background Suppression, " IEEE Transactions on Geoscience and Remote Sensing, pp. 1-15, 2021.
S. Dandrifosse, A. Carlier, B. Dumont, and B. Mercatoris, "Registration and fusion of close-range multimodal wheat images in field conditions, " Remote Sensing, vol. 13, no. 7, p. 1380, 2021.
X. Wang, H. Xuan, B. Evers, S. Shrestha, R. Pless, and J. Poland, "High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat, " GigaScience, vol. 8, no. giz120, 2019.
G. Tolias, R. Sicre, and H. Jégou, "Particular object retrieval with integral max-pooling of CNN activations, " 2015, https://arxiv.org/abs/1511.05879.
L. McInnes, J. Healy, and J. Melville, UMAP: Uuniform manifold approximation and projection for dimension reduction., 2020.
P. W. Koh, S. Sagawa, H. Marklund et al., "WILDS: A benchmark of in-the-wild distribution shifts, " 2021, April 2021, https://arxiv.org/abs/2012.07421.