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
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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.
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Since their 2016 publication, numerous disciplines are striving to apply FAIR Principles, including genomics, agriculture, climate science, and medicine.
“Since their 2016 publication, numerous disciplines are striving to apply FAIR Principles, including genomics, agriculture (Arnaud et al., 2020; David et al., 2020), climate science, and medicine.”
From Sysrev: A FAIR Platform for Data Curation and Systematic Evidence Review (2021) · cites this paper as David et al., 2020
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
YOLOv3: An Incremental Improvement
Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141 · see citing works
A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)
Khanam, Z., Achari, V. P. S., Boukhennoufa, I., et al. (2024) · Durham Research Online (Durham University) · doi:10.4230/oasics.ng-res.2024.2 · Cited by 14,048 · see citing works
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2844175 · Cited by 3,457 · see citing works
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., et al. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01316-z · Cited by 2,460 · see citing works
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00982 · Cited by 2,194 · see citing works
A State-of-the-Art Survey on Deep Learning Theory and Architectures
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S. K. E., Sidike, P., Nasrin, M. S., Hasan, M., Essen, B. C. V., Awwal, A. A. S., & Asari, V. K. (2019) · Electronics · doi:10.3390/electronics8030292 · Cited by 1,643 · see citing works
Why are wheat yields stagnating in Europe? A comprehensive data analysis for France
Brisson, N., Gate, P., Gouache, D., Charmet, G., Oury, F., & Huard, F. (2010) · Field Crops Research · doi:10.1016/j.fcr.2010.07.012 · Cited by 768 · see citing works
Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives
Singh, A. K., Ganapathysubramanian, B., Sarkar, S., & Singh, A. (2018) · Trends in Plant Science · doi:10.1016/j.tplants.2018.07.004 · Cited by 649 · see citing works
Drone-Based Object Counting by Spatially Regularized Regional Proposal Network
Hsieh, M.-R., Lin, Y.-L., & Hsu, W. H. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.446 · Cited by 547 · see citing works
Milioto, A., Lottes, P., & Stachniss, C. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8460962 · Cited by 509 · see citing works
Plant phenotyping: from bean weighing to image analysis
Walter, A., Liebisch, F., & Hund, A. (2015) · Plant Methods · doi:10.1186/s13007-015-0056-8 · Cited by 449 · see citing works
Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks
Ubbens, J. R., & Stavness, I. (2017) · Frontiers in Plant Science · doi:10.3389/fpls.2017.01190 · Cited by 376 · see citing works
PKLot—A robust dataset for parking lot classification
de Almeida, P. R. L., Oliveira, L. S., Britto, A. S., Silva, E. J., & Koerich, A. L. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.02.009 · Cited by 312 · see citing works
Ear density estimation from high resolution RGB imagery using deep learning technique
Madec, S., Jin, X., Lu, H., De Solan, B., Liu, S., Duyme, F., Heritier, E., & Baret, F. (2019) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2018.10.013 · Cited by 312 · see citing works
Sa, I., Popović, M., Khanna, R., Chen, Z., Lottes, P., Liebisch, F., Nieto, J., Stachniss, C., Walter, A., & Siegwart, R. (2018) · Remote Sensing · doi:10.3390/rs10091423 · Cited by 305 · see citing works
Subcategory-Aware Convolutional Neural Networks for Object Proposals and Detection
Yu, X., Choi, W., Lin, Y., & Savarese, S. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.108 · Cited by 303 · see citing works
Detection and analysis of wheat spikes using Convolutional Neural Networks
Hasan, M. M., Chopin, J. P., Laga, H., & Miklavcic, S. J. (2018) · Plant Methods · doi:10.1186/s13007-018-0366-8 · Cited by 275 · see citing works
Crain, J. L., Mondal, S., Rutkoski, J. E., Singh, R. P., & Poland, J. (2018) · The Plant Genome · doi:10.3835/plantgenome2017.05.0043 · Cited by 271 · see citing works
Reynolds, M. P., Chapman, S. C., Crespo‐Herrera, L., Molero, G., Mondal, S., Pequeno, D. N. L., Pinto, F., Piñera‐Chávez, F. J., Poland, J., Rivera‐Amado, C., et al. (2020) · Plant Science · doi:10.1016/j.plantsci.2019.110396 · Cited by 254 · see citing works
The use of plant models in deep learning: an application to leaf counting in rosette plants
Ubbens, J. R., Cieslak, M., Prusinkiewicz, P., & Stavness, I. (2018) · Plant Methods · doi:10.1186/s13007-018-0273-z · Cited by 253 · see citing works
Precise Detection in Densely Packed Scenes
Goldman, E., Herzig, R., Eisenschtat, A., Goldberger, J., & Hassner, T. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00537 · Cited by 247 · see citing works
From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer
Xiong, H., Lü, H., Liu, C., Liu, L., Cao, Z., & Shen, C. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00845 · Cited by 205 · see citing works
Deep Learning for Multi-task Plant Phenotyping
Pound, M. P., Atkinson, J. A., Wells, D. M., Pridmore, T., & French, A. P. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.241 · Cited by 187 · see citing works
A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting
Ghosal, S., Zheng, B., Chapman, S. C., Potgieter, A. B., Jordan, D. R., Wang, X. M., Singh, A. K., Singh, A., Hirafuji, M., Ninomiya, S., et al. (2019) · Plant Phenomics · doi:10.34133/2019/1525874 · Cited by 186 · see citing works
MinneApple: A Benchmark Dataset for Apple Detection and Segmentation
Hani, N., Roy, P., & Isler, V. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2965061 · Cited by 178 · see citing works
Sadeghi‐Tehran, P., Virlet, N., Ampe, E. M., Reyns, P., & Hawkesford, M. J. (2019) · Frontiers in Plant Science · doi:10.3389/fpls.2019.01176 · Cited by 159 · see citing works
TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks
Xiong, H., Cao, Z., Lu, H., Madec, S., Liu, L., & Shen, C. (2019) · Plant Methods · doi:10.1186/s13007-019-0537-2 · Cited by 157 · see citing works
Spectral Vegetation Indices to Track Senescence Dynamics in Diverse Wheat Germplasm
Anderegg, J., Yu, K., Aasen, H., Walter, A., Liebisch, F., & Hund, A. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2019.01749 · Cited by 151 · see citing works
Yield trends, variability and stagnation analysis of major crops in France over more than a century
Schauberger, B., Ari, T. B., Makowski, D., Kato, T., Kato, H., & Ciais, P. (2018) · Scientific Reports · doi:10.1038/s41598-018-35351-1 · Cited by 146 · see citing works
Measures for interoperability of phenotypic data: minimum information requirements and formatting
Ćwiek‐Kupczyńska, H., Altmann, T., Arend, D., Arnaud, E., Chen, D., Cornut, G., Fiorani, F., Frohmberg, W., Junker, A., Klukas, C., et al. (2016) · Plant Methods · doi:10.1186/s13007-016-0144-4 · Cited by 131 · see citing works
Weed Growth Stage Estimator Using Deep Convolutional Neural Networks
Teimouri, N., Dyrmann, M., Nielsen, P. R., Mathiassen, S. K., Somerville, G. J., & Jørgensen, R. N. (2018) · Sensors · doi:10.3390/s18051580 · Cited by 117 · see citing works
Guo, W., Zheng, B., Potgieter, A. B., Diot, J., Watanabe, K., Noshita, K., Jordan, D. R., Wang, X. M., Watson, J., Ninomiya, S., et al. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2018.01544 · Cited by 99 · see citing works
Applying innovations and new technologies for international collaborative wheat improvement
Reynolds, M. P., & Borlaug, N. E. (2006) · The Journal of Agricultural Science · doi:10.1017/s0021859606005879 · Cited by 88 · see citing works
An Easy-to-Setup 3D Phenotyping Platform for KOMATSUNA Dataset
Uchiyama, H., Sakurai, S., Mishima, M., Arita, D., Okayasu, T., Shimada, A., & Taniguchi, R. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.239 · Cited by 75 · see citing works
Annotated Image Datasets of Rosette Plants
Scharr, H., Minervini, M., Fischbach, A., & Tsaftaris, S. A. (2014) · Cited by 73 · see citing works
The GrassClover Image Dataset for Semantic and Hierarchical Species Understanding in Agriculture
Skovsen, S., Dyrmann, M., Mortensen, A. K., Laursen, M. S., Gislum, R., Eriksen, J., Farkhani, S., Karstoft, H., & Jørgensen, R. N. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00325 · Cited by 63 · see citing works
Applying FAIR Principles to Plant Phenotypic Data Management in GnpIS
Pommier, C., Michotey, C., Cornut, G., Roumet, P., Duchêne, É., Flores, R., Lebreton, A., Alaux, M., Durand, S., Kimmel, E., et al. (2019) · Plant Phenomics · doi:10.34133/2019/1671403 · Cited by 58 · see citing works
Jin, X., Madec, S., Dutartre, D., de Solan, B., Comar, A., & Baret, F. (2019) · Plant Phenomics · doi:10.34133/2019/4820305 · Cited by 48 · see citing works
Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large ImagesPreprint
Aich, S., & Stavness, I. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.11123 · Cited by 32 · see citing works
Non-invasive field phenotyping of cereal development
ETH Zurich, S., Hund, A., Kronenberg, L., ETH Zurich, S., Anderegg, J., ETH Zurich, S., Yu, K., KU Leuven, B., Walter, A., & ETH Zurich, S. (2019) · Burleigh Dodds series in agricultural science · doi:10.19103/as.2019.0051.13 · Cited by 26 · see citing works
Gibbs, J. A., Burgess, A. J., Pound, M. P., Pridmore, T., & Murchie, E. H. (2019) · PLANT PHYSIOLOGY · doi:10.1104/pp.19.00141 · Cited by 25 · see citing works
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Review on Convolutional Neural Networks (CNN) in vegetation remote sensing
Kattenborn, T., Jens Leitloff, A., Schiefer, F., & Hinz, S. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.12.010 · Cited by 1,871 · see citing works
Generalizing to unseen domains: A survey on domain generalization
Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., & Yu, P. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3178128 · Cited by 1,046 · see citing works
Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review
Lu, Y., Chen, D., Olaniyi, E. O., & Huang, Y. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107208 · Cited by 397 · see citing works
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518 · see citing works
Self-citationTowards the fully automated monitoring of ecological communities
Besson, M., Alison, J., Bjerge, K., Gorochowski, T. E., Høye, T. T., Jucker, T., Mann, H. M. R., & Clements, C. F. (2022) · Ecology Letters · doi:10.1111/ele.14123 · Cited by 279 · see citing works
Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods
David, É., Serouart, M., Smith, D., Madec, S., Velumani, K., Liu, S., Wang, X., Pinto, F., Shafiee, S., Tahir, I. S. A., et al. (2021) · Plant Phenomics · doi:10.34133/2021/9846158 · Cited by 162 · see citing works
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Janni, M., Maestri, E., Gullì, M., Marmiroli, M., & Marmiroli, N. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1297569 · Cited by 185 · see citing works
Deep Learning in Image-Based Plant Phenotyping
Murphy, K. M., Ludwig, E., Gutierrez, J., & Gehan, M. A. (2024) · Annual Review of Plant Biology · doi:10.1146/annurev-arplant-070523-042828 · Cited by 155 · see citing works
Khaki, S., Safaei, N., Pham, H., & Wang, L. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.03.017 · Cited by 131 · see citing works
Rapid Detection and Counting of Wheat Ears in the Field Using YOLOv4 with Attention Module
Yang, B., Gao, Z., Gao, Y., & Zhu, Y. (2021) · Agronomy · doi:10.3390/agronomy11061202 · Cited by 117 · see citing works
Tanabe, R., Matsui, T., & Tanaka, T. S. T. (2022) · Field Crops Research · doi:10.1016/j.fcr.2022.108786 · Cited by 112 · see citing works
Sysrev: A FAIR Platform for Data Curation and Systematic Evidence Review
Bozada, T., Borden, J., Workman, J., Del Cid, M., Malinowski, J., & Luechtefeld, T. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.685298 · Cited by 118 · see citing works
Improved YOLO v5 Wheat Ear Detection Algorithm Based on Attention Mechanism
Li, R., & Wu, Y. (2022) · Electronics · doi:10.3390/electronics11111673 · Cited by 100 · see citing works
High-throughput field crop phenotyping: current status and challenges
Ninomiya, S. (2022) · Ikushugaku zasshi · doi:10.1270/jsbbs.21069 · Cited by 95 · see citing works
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Huang, Y., Qian, Y., Wei, H., Lu, Y., Ling, B., & Qin, Y. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.108425 · Cited by 93 · see citing works
Velumani, K., López‐Lozano, R., Madec, S., Guo, W., Gillet, J., Comar, A., & Baret, F. (2021) · Plant Phenomics · doi:10.34133/2021/9824843 · Cited by 85 · see citing works
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Lü, H., Liu, L., Li, Y., Zhao, X., Wang, X., & Cao, Z. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3058962 · Cited by 82 · see citing works
Scaling up high-throughput phenotyping for abiotic stress selection in the field
Smith, D., Potgieter, A. B., & Chapman, S. C. (2021) · Theoretical and Applied Genetics · doi:10.1007/s00122-021-03864-5 · Cited by 79 · see citing works
Label-efficient learning in agriculture: A comprehensive review
Li, J., Chen, D., Qi, X., Li, Z., Huang, Y., Morris, D., & Tan, X. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.108412 · Cited by 77 · see citing works
Real-Time Detection for Wheat Head Applying Deep Neural Network
Gong, B., Ergu, D., Cai, Y., & Ma, B. (2020) · Sensors · doi:10.3390/s21010191 · Cited by 76 · see citing works
Advancing precision agriculture: The potential of deep learning for cereal plant head detection
Sanaeifar, A., Guindo, M. L., Bakhshipour, A., Fazayeli, H., Li, X., & Yang, C. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.107875 · Cited by 76 · see citing works
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Li, J., Li, C., Fei, S., Ma, C., Chen, W., Ding, F., Wang, Y., Li, Y., Shi, J., & Xiao, Z. Y. (2021) · Sensors · doi:10.3390/s21144845 · Cited by 75 · see citing works
Paheding, S., Saleem, A. I., Siddiqui, M. F. H., Rawashdeh, N. A., Essa, A. E., & Reyes, A. A. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10165-7 · Cited by 75 · see citing works
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Wang, Y., Qin, Y., & Cui, J. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.645899 · Cited by 74 · see citing works
Jiang, Y., Li, C., Xu, R. Y., Sun, S., Robertson, J. S., & Paterson, A. H. (2020) · Plant Methods · doi:10.1186/s13007-020-00698-y · Cited by 72 · see citing works
Okura, F. (2022) · Ikushugaku zasshi · doi:10.1270/jsbbs.21074 · Cited by 69 · see citing works
Zenkl, R., Timofte, R., Kirchgeßner, N., Roth, L., Hund, A., Gool, L. V., Walter, A., & Aasen, H. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2021.774068 · Cited by 67 · see citing works
Li, L., Hassan, M. A., Yang, S., Jing, F., Yang, M., Rasheed, A., Wang, J., Xia, X., He, Z., & Xiao, Y. (2022) · The Crop Journal · doi:10.1016/j.cj.2022.07.007 · Cited by 64 · see citing works
Anderegg, J., Tschurr, F., Kirchgeßner, N., Treier, S., Schmucki, M., Streit, B., & Walter, A. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107558 · Cited by 61 · see citing works
Trevisan, R., Pérez, O. M., Schmitz, N., Diers, B. W., & Martin, N. F. (2020) · Remote Sensing · doi:10.3390/rs12213617 · Cited by 60 · see citing works
Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications
Riera, L. G., Carroll, M. E., Zhang, Z., Shook, J. M., Ghosal, S., Gao, T., Singh, A., Bhattacharya, S., Ganapathysubramanian, B., Singh, A. K., et al. (2021) · Plant Phenomics · doi:10.34133/2021/9846470 · Cited by 59 · see citing works
Resources for image-based high-throughput phenotyping in crops and data sharing challenges
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Sun, Y., Zhang, Z., Sun, K., Li, S., Yu, J., Miao, L., Zhang, Z., Li, Y., Zhao, H., Hu, Z., et al. (2023) · Agriculture · doi:10.3390/agriculture13071321 · Cited by 51 · see citing works
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Aslahishahri, M., Stanley, K. G., Duddu, H., Shirtliffe, S. J., Vail, S. L., Bett, K. E., Pozniak, C. J., & Stavness, I. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00152 · Cited by 47 · see citing works
Camenzind, M. P., & Yu, K. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1214931 · Cited by 46 · see citing works
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Madec, S., Irfan, K., Velumani, K., Baret, F., David, É., Daubige, G., Samatan, L. B., Serouart, M., Smith, D., James, C., et al. (2023) · Scientific Data · doi:10.1038/s41597-023-02098-y · Cited by 44 · see citing works
Self-citationRobust Object Detection with Inaccurate Bounding Boxes
Liu, C., Wang, K., Lü, H., Cao, Z., & Zhang, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20080-9_4 · Cited by 42 · see citing works
Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB Images
Tanaka, Y., Watanabe, T., Katsura, K., Tsujimoto, Y., Takai, T., Tanaka, T. S. T., Kawamura, K., Saito, H., Homma, K., Mairoua, S. G., et al. (2023) · Plant Phenomics · doi:10.34133/plantphenomics.0073 · Cited by 39 · see citing works
Attribute-Modulated Generative Meta Learning for Zero-Shot Learning
Li, Y., Liu, Z., Yao, L., & Chang, X. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3139211 · Cited by 40 · see citing works
Misra, T., Arora, A., Marwaha, S., Jha, R. R., Ray, M., Jain, R., Rao, A. R., Varghese, E., Kumar, S., Kumar, S., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3080836 · Cited by 38 · see citing works
Domain Adaptation of Synthetic Images for Wheat Head Detection
Hartley, Z. K. J., & French, A. (2021) · Plants · doi:10.3390/plants10122633 · Cited by 33 · see citing works
Zhu, J., Yang, G., Feng, X., Li, X., Fang, H., Zhang, J., Bai, X., Tao, M., & He, Y. (2022) · Remote Sensing · doi:10.3390/rs14205141 · Cited by 38 · see citing works
EasyIDP: A Python Package for Intermediate Data Processing in UAV-Based Plant Phenotyping
Wang, H., Duan, Y., Shi, Y., Kato, Y., Ninomiya, S., & Guo, W. (2021) · Remote Sensing · doi:10.3390/rs13132622 · Cited by 37 · see citing works
Ye, J., Yu, Z., Wang, Y., Lu, D., & Zhou, H. (2023) · Plant Methods · doi:10.1186/s13007-023-01079-x · Cited by 35 · see citing works
Wheat Head Detection using Deep, Semi-Supervised and Ensemble Learning
Fourati, F., Mseddi, W. S., & Attia, R. (2021) · Canadian Journal of Remote Sensing · doi:10.1080/07038992.2021.1906213 · Cited by 36 · see citing works
YOLOv7-MA: Improved YOLOv7-Based Wheat Head Detection and Counting
Meng, X., Li, C., Li, J., Li, X., Guo, F., & Xiao, Z. Y. (2023) · Remote Sensing · doi:10.3390/rs15153770 · Cited by 33 · see citing works
Anderegg, J., Zenkl, R., Walter, A., Hund, A., & McDonald, B. A. (2023) · Plant Phenomics · doi:10.34133/plantphenomics.0053 · Cited by 32 · see citing works
Unsupervised Domain Adaptation for Plant Organ Counting
Ayalew, T. W., Ubbens, J. R., & Stavness, I. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-65414-6_23 · Cited by 31 · see citing works
Plant Leaf Disease Detection Using Efficient Image Processing and Machine Learning Algorithms
Kiran, S. M., & Chandrappa, D. N. (2023) · Journal of Robotics and Control (JRC) · doi:10.18196/jrc.v4i6.20342 · Cited by 30 · see citing works
Wheat spike localization and counting via hybrid UNet architectures
Zaji, A., Liu, Z., Xiao, G. G., Bhowmik, P., Sangha, J. S., & Ruan, Y. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107439 · Cited by 30 · see citing works
A Review of Artificial Intelligence Techniques for Wheat Crop Monitoring and Management
Barbedo, J. G. A. (2025) · Agronomy · doi:10.3390/agronomy15051157 · Cited by 28 · see citing works
Deep Regression Versus Detection for Counting in Robotic Phenotyping
Salazar-Gomez, A., Aptoula, E., Parsons, S., & Bosilj, P. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3062586 · Cited by 28 · see citing works
Lightweight YOLOv8 for Wheat Head Detection
Fang, C., & Yang, X. (2024) · IEEE Access · doi:10.1109/access.2024.3397556 · Cited by 27 · see citing works
Gao, C., He, B., Guo, W. F., Qu, Y., Wang, Q., & Dong, W. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110794 · Cited by 26 · see citing works
Deep learning for three-dimensional (3D) plant phenomics
Jin, S., Li, D., Yun, T., Tang, J., Wang, K., Li, S., Yang, H., Si, Y., Xu, S., Cao, L., et al. (2025) · Plant Phenomics · doi:10.1016/j.plaphe.2025.100107 · Cited by 29 · see citing works
Classification of wheat varieties with image-based deep learning
Ceyhan, M., Kartal, Y., Özkan, K., & Seke, E. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16075-5 · Cited by 26 · see citing works
Deep learning in plant phenotyping: the first ten years
Ubbens, J. R., Stavness, I., Pound, M. P., & Guo, W. (2025) · Plant Phenomics · doi:10.1016/j.plaphe.2025.100062 · Cited by 25 · see citing works
Najafian, K., Ghanbari, A., Stavness, I., Jin, L., Shirdel, G. H., & Maleki, F. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00155 · Cited by 25 · see citing works
Dynamic Color Transform Networks for Wheat Head Detection
Liu, C., Wang, K., Lü, H., & Cao, Z. (2022) · Plant Phenomics · doi:10.34133/2022/9818452 · Cited by 25 · see citing works
Lightweight and efficient neural network with SPSA attention for wheat ear detection
Dong, Y. X., Liu, Y., Kang, H., Li, C., Liu, P., & Liu, Z. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.931 · Cited by 25 · see citing works
Tan, G. D., Chaudhuri, U., Varela, S., Ahuja, N., & Leakey, A. D. B. (2024) · Journal of Experimental Botany · doi:10.1093/jxb/erae395 · Cited by 26 · see citing works
AutoOLA: Automatic object level augmentation for wheat spikes counting
Zaji, A., Liu, Z., Xiao, G. G., Bhowmik, P., Sangha, J. S., & Ruan, Y. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.107623 · Cited by 24 · see citing works
Method for wheat ear counting based on frequency domain decomposition of MSVF-ISCT
Bao, W., Lin, Z., Hu, G., Liang, D., Huang, L., & Zhang, X. (2022) · Information Processing in Agriculture · doi:10.1016/j.inpa.2022.01.001 · Cited by 22 · see citing works
Semi-Self-Supervised Learning for Semantic Segmentation in Images with Dense Patterns
Najafian, K., Ghanbari, A., Kish, M. S., Eramian, M., Shirdel, G. H., Stavness, I., Jin, L., & Maleki, F. (2023) · Plant Phenomics · doi:10.34133/plantphenomics.0025 · Cited by 22 · see citing works
FLTrans-Net: Transformer-based feature learning network for wheat head detection
Yousafzai, S. N., Nasir, I. M., Tehsin, S., Fitriyani, N. L., & Syafrudin, M. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109706 · Cited by 22 · see citing works
Coleman, G., Kutugata, M., Walsh, M., & Bagavathiannan, M. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.108622 · Cited by 21 · see citing works
Shen, L., Ding, G., Jackson, R. J., Ali, M. K., Liu, S., Mitchell, A., Shi, Y., Lu, X., Dai, J., Deakin, G., et al. (2024) · Plant Phenomics · doi:10.34133/plantphenomics.0255 · Cited by 21 · see citing works
Breeding 4.0 vis‐à‐vis application of artificial intelligence (AI) in crop improvement: an overview
Ansari, R., Manna, A., Hazra, S., Bose, S., Chatterjee, A., & Sen, P. (2024) · New Zealand Journal of Crop and Horticultural Science · doi:10.1080/01140671.2024.2409775 · Cited by 21 · see citing works
The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset
Huang, Z. H., Zenkl, R., Greche, L., de Solan, B., Samatan, L. B., Ouahid, S., Visioni, A., Robles‐Zazueta, C. A., Pinto, F., Perez-Olivera, I., et al. (2025) · Plant Phenomics · doi:10.1016/j.plaphe.2025.100084 · Cited by 21 · see citing works
Self-citationZhang, J., Min, A., Steffenson, B. J., Su, W., Hirsch, C. D., Anderson, J. A., Wei, J., Ma, Q., & Yang, C. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.834938 · Cited by 21 · see citing works
Artificial intelligence in plant science: from image-based phenotyping to yield and trait prediction
Wang, T., Tong, R., Xu, T., Li, Y. S., & Chen, Y. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1732979 · Cited by 22 · see citing works
Plant Leaf Disease Detection Using Computer Vision Techniques and Machine Learning
Joshi, K., Awale, R., Ahmad, S., Patil, S., & Pisal, V. (2022) · ITM Web of Conferences · doi:10.1051/itmconf/20224403002 · Cited by 20 · see citing works
Xu, X., Qiao, H., Ma, X., Yin, G., Wang, Y., Zhao, J., & Li, H. (2023) · Measurement · doi:10.1016/j.measurement.2023.112849 · Cited by 20 · see citing works
Bai, B., Wang, J., Li, J., Yu, L., Jiangtao, W., & Han, Y. (2024) · Journal of the Science of Food and Agriculture · doi:10.1002/jsfa.13396 · Cited by 20 · see citing works
Low-light wheat image enhancement using an explicit inter-channel sparse transformer
Wang, Y., Wang, F., Li, K., Feng, X., Hou, W., Liu, L., He, Y., He, Y., & Wang, Y. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109169 · Cited by 20 · see citing works
Franco, V. R., Hott, M. C., Andrade, R. G., & Goliatt, L. (2022) · Evolutionary Intelligence · doi:10.1007/s12065-022-00736-9 · Cited by 20 · see citing works
Chen, J., Zhou, J., Li, Q., Li, H., Xia, Y., Jackson, R. J., Sun, G., Zhou, G., Deakin, G., Jiang, D., et al. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1219983 · Cited by 19 · see citing works
Computer Vision and Machine Learning in Agriculture
Raval, M. S., Chaudhary, S., & Adinarayana, J. (2021) · Studies in big data · doi:10.1007/978-981-16-5847-1_5 · Cited by 18 · see citing works
RetinaNet and Vision Transformer-Based Model for Wheat Head Detection
Sunitha, G., Sudeepthi, A., Sreedhar, B., Shaik, A. B., & Farooq, C. (2023) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca57980.2023.10220614 · Cited by 18 · see citing works
A survey on deep learning applications in wheat phenotyping
Zaji, A., Liu, Z., Xiao, G. G., Sangha, J. S., & Ruan, Y. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109761 · Cited by 17 · see citing works
WH-DETR: An Efficient Network Architecture for Wheat Spike Detection in Complex Backgrounds
Yang, Z., Yang, W., Yi, J., & Liu, R. (2024) · Agriculture · doi:10.3390/agriculture14060961 · Cited by 18 · see citing works
CSNet: A Count-Supervised Network via Multiscale MLP-Mixer for Wheat Ear Counting
Li, Y., Wu, X., Wang, Q., Pei, Z., Zhao, K., Chen, P., & Hao, G. (2024) · Plant Phenomics · doi:10.34133/plantphenomics.0236 · Cited by 17 · see citing works
CTWheatNet: Accurate detection model of wheat ears in field
Guan, Y., Pan, J., Fan, Q., Yang, L., Yin, X., & Jia, W. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109272 · Cited by 17 · see citing works
Qian, Y., Qin, Y., Wei, H., Lu, Y., Huang, Y., Liu, P., & Fan, Y. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109342 · Cited by 17 · see citing works
A Method for Wheat Head Detection Based on Yolov4Preprint
Gong, B., Ergu, D., Cai, Y., & Ma, B. (2020) · Research Square · doi:10.21203/rs.3.rs-86158/v1 · Cited by 17 · see citing works
Zenkl, R., McDonald, B. A., Walter, A., & Anderegg, J. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109854 · Cited by 16 · see citing works
Zhu, R., Wang, X., Yan, Z., Qiao, Y., Tian, H., Hu, Z., Zhang, Z., Li, Y., Zhao, H., Xin, D., et al. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.922030 · Cited by 16 · see citing works
Benchmarking Self-Supervised Contrastive Learning Methods for Image-Based Plant Phenotyping
Ogidi, F., Eramian, M., & Stavness, I. (2023) · Plant Phenomics · doi:10.34133/plantphenomics.0037 · Cited by 16 · see citing works
GranoScan: an AI-powered mobile app for in-field identification of biotic threats of wheat
Dainelli, R., Bruno, A., Martinelli, M., Moroni, D., Rocchi, L., Morelli, S., Ferrari, E., Silvestri, M., Agostinelli, S., Cava, P. L., et al. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1298791 · Cited by 15 · see citing works
VM-YOLO: YOLO with VMamba for Strawberry Flowers Detection
Wang, Y., Lin, X., Xiang, Z., & Su, W. (2025) · Plants · doi:10.3390/plants14030468 · Cited by 14 · see citing works
James, C., Chandra, S. S., & Chapman, S. C. (2025) · Plant Phenomics · doi:10.1016/j.plaphe.2025.100050 · Cited by 14 · see citing works
- Authors
- David, E., Madec, S., Sadeghi-Tehran, P., Aasen, H., Zheng, B., Liu, S., Kirchgessner, N., Ishikawa, G., Nagasawa, K., Badhon, M. A., Pozniak, C., de Solan, B., Hund, A., Chapman, S. C., Baret, F., Stavness, I., & Guo, W.
- Affiliations
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR)
- ETH Zurich (CH)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Nanjing Agricultural University (CN)
- National Agriculture and Food Research Organization (JP)
- University of Saskatchewan (CA)
- The University of Tokyo (JP)
- Corresponding author
- Stavness, Ian
- Guo, Wei
- ORCID
- Venue
- Plant Phenomics
- Publisher
- Elsevier BV
- Published
- 2020
- Volume
- 2020
- Pages
- 3521852
- ISSN
- 2097-0374 (ISSN-L)
- 2643-6515
- PMC
- PMC7706323
- OpenAlex
- W3064678530
- Cited by
- 277
- References
- 45
- Citations by year
- 2026: 39 · 2025: 46 · 2024: 44 · 2023: 42 · 2022: 54 · 2021: 43 · 2020: 9
- FWCI
- 23.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Plant Phenomics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Rothamsted Repository (Rothamsted Repository) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rothamsted Repository (Rothamsted Repository) (submittedVersion)
- HAL (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- FSOV “Plastix”
- CASDAR LITERAL
- ANR BREEDWHEAT
- ANR PHENOME
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Keywords
- wheat head detection, RGB images, head orientation, FAIR principles, image metadata, benchmarking, plant phenotyping
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