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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

3D Morphological Processing for Wheat Spike Phenotypes Using Computed Tomography Images
Biao Xiong, Bo Wang, Shengwu Xiong, et al.
Remote Sensing (2019) Vol. 11, Iss. 9, pp. 1110-1110
Open Access | Times Cited: 19

Showing 19 citing articles:

High-throughput phenotyping: Breaking through the bottleneck in future crop breeding
Peng Song, Jinglu Wang, Xinyu Guo, et al.
The Crop Journal (2021) Vol. 9, Iss. 3, pp. 633-645
Open Access | Times Cited: 165
Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives
Abbas Atefi, Yufeng Ge, Santosh Pitla, et al.
Frontiers in Plant Science (2021) Vol. 12
Open Access | Times Cited: 87
Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm
Giao N. Nguyen, Sally L. Norton
Plants (2020) Vol. 9, Iss. 7, pp. 817-817
Open Access | Times Cited: 48
Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography
Weijuan Hu, Can Zhang, Yuqiang Jiang, et al.
Plant Phenomics (2020) Vol. 2020
Open Access | Times Cited: 46
Characterization of spring and durum wheat using non-destructive synchrotron phase contrast X-ray microtomography during storage
Navnath Indore, Chithra Karunakaran, Digvir S. Jayas, et al.
npj Science of Food (2024) Vol. 8, Iss. 1
Open Access | Times Cited: 4
High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed Tomography
Weizhen Liu, Chang Liu, Jingyi Jin, et al.
Frontiers in Plant Science (2020) Vol. 11
Open Access | Times Cited: 34
Automatic Detection and Counting of Wheat Spikelet Using Semi-Automatic Labeling and Deep Learning
Ruicheng Qiu, Yong He, Man Zhang
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 16
Unexpected Effects of Sulfate and Sodium Chloride Application on Yield Qualitative Characteristics and Symmetry Indicators of Hard and Soft Wheat Kernels
Tatiana Aniskina, Ekaterina N. Baranova, С. В. Лебедев, et al.
Plants (2023) Vol. 12, Iss. 5, pp. 980-980
Open Access | Times Cited: 5
Wheat grain width: a clue for re-exploring visual indicators of grain weight
Abbas Haghshenas, Y. Emam, Saeid Jafarizadeh
Plant Methods (2022) Vol. 18, Iss. 1
Open Access | Times Cited: 9
Response of Bread Wheat Cultivars to Terminal Water Stress and Cytokinin Application from a Grain Phenotyping Perspective
Afshin Zamani, Y. Emam, Mohsen Edalat
Agronomy (2024) Vol. 14, Iss. 1, pp. 182-182
Open Access | Times Cited: 1
A Novel Method for Wheat Spike Phenotyping Based on Instance Segmentation and Classification
Ziang Niu, Ning Liang, Yiyin He, et al.
Applied Sciences (2024) Vol. 14, Iss. 14, pp. 6031-6031
Open Access | Times Cited: 1
Detection and characterization of spike architecture based on deep learning and X-ray computed tomography in barley
Yimin Ling, Qinlong Zhao, Wenxin Liu, et al.
Plant Methods (2023) Vol. 19, Iss. 1
Open Access | Times Cited: 3
Image facilitated assessment of intra-spike variation in grain size in wheat under high temperature and drought stress
Vidisha Thakur, Jagadish Rane, Girish Chandra Pandey, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 3
Evaluation of the Spike Diversity of Seven Hexaploid Wheat Species and an Artificial Amphidiploid Using a Quadrangle Model Obtained from 2D Images
Е. Г. Комышев, М. А. Генаев, Yuliya V. Kruchinina, et al.
Plants (2024) Vol. 13, Iss. 19, pp. 2736-2736
Open Access
MRI-Seed-Wizard: Combining Deep Learning Algorithms with Magnetic Resonance Imaging Enables Advanced Seed Phenotyping
Iaroslav Plutenko, Volodymyr Radchuk, Simon Mayer, et al.
Journal of Experimental Botany (2024)
Open Access
MSCVI: An improved algorithm for mitigating LiDAR noise and occlusion effects in field wheat tiller number calculation
Tai Guo, Yongqing Wang, Yangyang Gu, et al.
Computers and Electronics in Agriculture (2024) Vol. 229, pp. 109757-109757
Closed Access
Automated extraction of pod phenotype data from micro-computed tomography
Evangeline Corcoran, Laura Siles, Smita Kurup, et al.
Frontiers in Plant Science (2023) Vol. 14
Open Access | Times Cited: 1
Detection and characterization of spike architecture based on deep learning and X-ray computed tomography in barley
Yimin Ling, Qinlong Zhao, Wenxin Liu, et al.
Research Square (Research Square) (2023)
Open Access | Times Cited: 1
Wheat Phenotype Extraction via Adaptive Supervoxel Segmentation
Bo Wang, Chengde Lin, Shengwu Xiong
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2020), pp. 807-814
Closed Access | Times Cited: 3
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