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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:

A review of computer vision technologies for plant phenotyping
Zhenbo Li, Ruohao Guo, Meng Li, et al.
Computers and Electronics in Agriculture (2020) Vol. 176, pp. 105672-105672
Closed Access | Times Cited: 272

Showing 1-25 of 272 citing articles:

Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning
Zhencun Jiang, Zhengxin Dong, Wenping Jiang, et al.
Computers and Electronics in Agriculture (2021) Vol. 186, pp. 106184-106184
Closed Access | Times Cited: 159
A comprehensive review on recent applications of unmanned aerial vehicle remote sensing with various sensors for high-throughput plant phenotyping
Lei Feng, Shuangshuang Chen, Chu Zhang, et al.
Computers and Electronics in Agriculture (2021) Vol. 182, pp. 106033-106033
Closed Access | Times Cited: 146
A CNN-SVM study based on selected deep features for grapevine leaves classification
Murat Köklü, Muhammed Fahri Ünlerşen, İlker Ali Özkan, et al.
Measurement (2021) Vol. 188, pp. 110425-110425
Closed Access | Times Cited: 137
Automatic organ-level point cloud segmentation of maize shoots by integrating high-throughput data acquisition and deep learning
Yinglun Li, Weiliang Wen, Miao Teng, et al.
Computers and Electronics in Agriculture (2022) Vol. 193, pp. 106702-106702
Closed Access | Times Cited: 85
Computer vision and machine learning applied in the mushroom industry: A critical review
Hua Yin, Wenlong Yi, Dian-Ming Hu
Computers and Electronics in Agriculture (2022) Vol. 198, pp. 107015-107015
Closed Access | Times Cited: 72
Deep learning: as the new frontier in high-throughput plant phenotyping
Sunny Arya, Karansher Singh Sandhu, Jagmohan Singh, et al.
Euphytica (2022) Vol. 218, Iss. 4
Closed Access | Times Cited: 71
Plant image recognition with deep learning: A review
Huiling Chen, Yiqi Huang, Zizhao Zhang, et al.
Computers and Electronics in Agriculture (2023) Vol. 212, pp. 108072-108072
Closed Access | Times Cited: 49
Object detection and tracking in Precision Farming: a systematic review
Mar Ariza-Sentís, Sergio Vélez, Raquel Martínez‐Peña, et al.
Computers and Electronics in Agriculture (2024) Vol. 219, pp. 108757-108757
Open Access | Times Cited: 36
Automatic fruit picking technology: a comprehensive review of research advances
Jun Zhang, Ningbo Kang, Qianjin Qu, et al.
Artificial Intelligence Review (2024) Vol. 57, Iss. 3
Open Access | Times Cited: 19
A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture
Jalal Uddin Md Akbar, Syafiq Fauzi Kamarulzaman, Abu Jafar Md Muzahid, et al.
IEEE Access (2024) Vol. 12, pp. 4485-4522
Open Access | Times Cited: 17
Leaf Only SAM: A Segment Anything Pipeline for Zero-Shot Automated Leaf Segmentation
Dominic Williams, Fraser Macfarlane, Avril Britten
Smart Agricultural Technology (2024) Vol. 8, pp. 100515-100515
Open Access | Times Cited: 16
Segment Any Leaf 3D: A Zero-Shot 3D Leaf Instance Segmentation Method Based on Multi-View Images
Yunlong Wang, Zhiyong Zhang
Sensors (2025) Vol. 25, Iss. 2, pp. 526-526
Open Access | Times Cited: 1
Thermal Imaging for Plant Stress Detection and Phenotyping
Mónica Pineda, Matilde Barón, María Luisa Pérez‐Bueno
Remote Sensing (2020) Vol. 13, Iss. 1, pp. 68-68
Open Access | Times Cited: 134
A high-precision detection method of hydroponic lettuce seedlings status based on improved Faster RCNN
Zhenbo Li, Ye Li, Yang Yong-bo, et al.
Computers and Electronics in Agriculture (2021) Vol. 182, pp. 106054-106054
Closed Access | Times Cited: 93
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: 89
Automatic late blight lesion recognition and severity quantification based on field imagery of diverse potato genotypes by deep learning
Junfeng Gao, Jesper Cairo Westergaard, Ea Høegh Riis Sundmark, et al.
Knowledge-Based Systems (2021) Vol. 214, pp. 106723-106723
Open Access | Times Cited: 76
Crop phenotyping in a context of global change: What to measure and how to do it
J. L. Araus, Shawn C. Kefauver, Omar Vergara‐Díaz, et al.
Journal of Integrative Plant Biology (2021) Vol. 64, Iss. 2, pp. 592-618
Open Access | Times Cited: 56
A Hybrid Model for Leaf Diseases Classification Based on the Modified Deep Transfer Learning and Ensemble Approach for Agricultural AIoT-Based Monitoring
Maryam Saberi Anari
Computational Intelligence and Neuroscience (2022) Vol. 2022, pp. 1-15
Open Access | Times Cited: 50
PiTLiD: Identification of Plant Disease From Leaf Images Based on Convolutional Neural Network
Kangchen Liu, Xiujun Zhang
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2022) Vol. 20, Iss. 2, pp. 1278-1288
Closed Access | Times Cited: 43
PSegNet: Simultaneous Semantic and Instance Segmentation for Point Clouds of Plants
Dawei Li, Jinsheng Li, Shiyu Xiang, et al.
Plant Phenomics (2022) Vol. 2022
Open Access | Times Cited: 39
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
Proximal sensing for geometric characterization of vines: A review of the latest advances
Hugo Moreno, Dionisio Andújar
Computers and Electronics in Agriculture (2023) Vol. 210, pp. 107901-107901
Open Access | Times Cited: 27
A High-Precision Detection Method of Apple Leaf Diseases Using Improved Faster R-CNN
Xulu Gong, Shujuan Zhang
Agriculture (2023) Vol. 13, Iss. 2, pp. 240-240
Open Access | Times Cited: 26
How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques
Negin Harandi, Breght Vandenberghe, Joris Vankerschaver, et al.
Plant Methods (2023) Vol. 19, Iss. 1
Open Access | Times Cited: 26
An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture
Danuta Cembrowska-Lech, Adrianna Krzemińska, Tymoteusz Miller, et al.
Biology (2023) Vol. 12, Iss. 10, pp. 1298-1298
Open Access | Times Cited: 25
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