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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:

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

Showing 16 citing articles:

Advancing precision agriculture: The potential of deep learning for cereal plant head detection
Alireza Sanaeifar, Mahamed Lamine Guindo, Adel Bakhshipour, et al.
Computers and Electronics in Agriculture (2023) Vol. 209, pp. 107875-107875
Open Access | Times Cited: 32
Advancements in Utilizing Image-Analysis Technology for Crop-Yield Estimation
Yu Feng, Ming Wang, Jun Xiao, et al.
Remote Sensing (2024) Vol. 16, Iss. 6, pp. 1003-1003
Open Access | Times Cited: 7
Segmentation and counting of wheat spike grains based on deep learning and textural feature
Xin Xu, Qing Geng, Feng Gao, et al.
Plant Methods (2023) Vol. 19, Iss. 1
Open Access | Times Cited: 16
Image-based classification of wheat spikes by glume pubescence using convolutional neural networks
N. V. Artemenko, М. А. Генаев, Rostislav Epifanov, et al.
Frontiers in Plant Science (2024) Vol. 14
Open Access | Times Cited: 5
YOLOv5s-T: A Lightweight Small Object Detection Method for Wheat Spikelet Counting
Lei Shi, Jiayue Sun, Yuanbo Dang, et al.
Agriculture (2023) Vol. 13, Iss. 4, pp. 872-872
Open Access | Times Cited: 8
A co-located QTL for seven spike architecture-related traits shows promising breeding use potential in common wheat (Triticum aestivum L.)
Jieguang Zhou, Qian Liu, Rong Tian, et al.
Theoretical and Applied Genetics (2024) Vol. 137, Iss. 1
Closed Access | Times Cited: 2
YOLO SSPD: a small target cotton boll detection model during the boll-spitting period based on space-to-depth convolution
Mengli Zhang, Wei Chen, Pan Gao, et al.
Frontiers in Plant Science (2024) Vol. 15
Open Access | Times Cited: 2
Efficient and Lightweight Automatic Wheat Counting Method with Observation-Centric SORT for Real-Time Unmanned Aerial Vehicle Surveillance
Jie Chen, Xiaochun Hu, Jiahao Lu, et al.
Agriculture (2023) Vol. 13, Iss. 11, pp. 2110-2110
Open Access | Times Cited: 6
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
Machine learning for major food crops breeding: Applications, challenges, and ways forward
Kumanan N. Govaichelvan, Dharini Pathmanathan, Rabiatul‐Adawiah Zainal‐Abidin, et al.
Agronomy Journal (2023) Vol. 116, Iss. 3, pp. 1112-1125
Closed Access | Times Cited: 3
A lightweight model based on YOLOv8n in wheat spike detection
Xuyang Ban, Pan Liu, Lei Xu, et al.
(2023), pp. 1-6
Closed Access | Times Cited: 3
YOLO-LF: a lightweight multi-scale feature fusion algorithm for wheat spike detection
Shuren Zhou, Shengzhen Long
Journal of Real-Time Image Processing (2024) Vol. 21, Iss. 4
Closed Access
Lightweight Wheat Spike Detection Method Based on Activation and Loss Function Enhancements for YOLOv5s
Jingsong Li, Feijie Dai, Haiming Qian, et al.
Agronomy (2024) Vol. 14, Iss. 9, pp. 2036-2036
Open Access
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
PanicleNeRF: low-cost, high-precision in-field phenotyping of rice panicles with smartphone
Xin Yang, Xuqi Lu, Pengyao Xie, et al.
Plant Phenomics (2024) Vol. 6
Closed Access
An Image Analysis-Based Automated Method using Deep Learning for Grain Counting
Ramesh Ajikaran, Ashen Iranga Hewarathna, Vigneshwaran Palanisamy, et al.
(2023), pp. 25-30
Closed Access | Times Cited: 1
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