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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:

DeepFruits: A Fruit Detection System Using Deep Neural Networks
Inkyu Sa, Zongyuan Ge, Feras Dayoub, et al.
Sensors (2016) Vol. 16, Iss. 8, pp. 1222-1222
Open Access | Times Cited: 945

Showing 1-25 of 945 citing articles:

Deep learning in agriculture: A survey
Andreas Kamilaris, Francesc X. Prenafeta‐Boldú
Computers and Electronics in Agriculture (2018) Vol. 147, pp. 70-90
Open Access | Times Cited: 3107
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
Mehdi Mohammadi, Ala Al‐Fuqaha, Sameh Sorour, et al.
IEEE Communications Surveys & Tutorials (2018) Vol. 20, Iss. 4, pp. 2923-2960
Open Access | Times Cited: 1257
A comparative study of fine-tuning deep learning models for plant disease identification
Edna C. Too, Yujian Li, Sam Njuki, et al.
Computers and Electronics in Agriculture (2018) Vol. 161, pp. 272-279
Closed Access | Times Cited: 932
Apple detection during different growth stages in orchards using the improved YOLO-V3 model
Yunong Tian, Guodong Yang, Zhe Wang, et al.
Computers and Electronics in Agriculture (2019) Vol. 157, pp. 417-426
Closed Access | Times Cited: 873
Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN
Yu Yang, Kailiang Zhang, Yang Li, et al.
Computers and Electronics in Agriculture (2019) Vol. 163, pp. 104846-104846
Closed Access | Times Cited: 533
Fruits and vegetables quality evaluation using computer vision: A review
Anuja Bhargava, Atul Bansal
Journal of King Saud University - Computer and Information Sciences (2018) Vol. 33, Iss. 3, pp. 243-257
Open Access | Times Cited: 476
Deep Count: Fruit Counting Based on Deep Simulated Learning
Maryam Rahnemoonfar, Clay Sheppard
Sensors (2017) Vol. 17, Iss. 4, pp. 905-905
Open Access | Times Cited: 471
Deep fruit detection in orchards
Suchet Bargoti, James Underwood
(2017), pp. 3626-3633
Open Access | Times Cited: 460
Deep learning – Method overview and review of use for fruit detection and yield estimation
Anand Koirala, Kerry B. Walsh, Zhenglin Wang, et al.
Computers and Electronics in Agriculture (2019) Vol. 162, pp. 219-234
Open Access | Times Cited: 439
Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review
Yunchao Tang, Mingyou Chen, Chenglin Wang, et al.
Frontiers in Plant Science (2020) Vol. 11
Open Access | Times Cited: 417
Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies
Othmane Friha, Mohamed Amine Ferrag, Lei Shu, et al.
IEEE/CAA Journal of Automatica Sinica (2021) Vol. 8, Iss. 4, pp. 718-752
Closed Access | Times Cited: 410
A review of the use of convolutional neural networks in agriculture
Andreas Kamilaris, Francesc X. Prenafeta‐Boldú
The Journal of Agricultural Science (2018) Vol. 156, Iss. 3, pp. 312-322
Open Access | Times Cited: 396
Fruit recognition from images using deep learning
Horea Mureşan, Mihai Oltean
Acta Universitatis Sapientiae Informatica (2018) Vol. 10, Iss. 1, pp. 26-42
Open Access | Times Cited: 374
Faster R-CNN for multi-class fruit detection using a robotic vision system
Shaohua Wan, Sotirios K. Goudos
Computer Networks (2019) Vol. 168, pp. 107036-107036
Closed Access | Times Cited: 374
Deep learning for real-time fruit detection and orchard fruit load estimation: benchmarking of ‘MangoYOLO’
Anand Koirala, Kerry B. Walsh, Zhenzhen Wang, et al.
Precision Agriculture (2019) Vol. 20, Iss. 6, pp. 1107-1135
Closed Access | Times Cited: 361
Recent advances in image processing techniques for automated leaf pest and disease recognition – A review
Lawrence C. Ngugi, Moataz Abelwahab, Mohammed Abo‐Zahhad
Information Processing in Agriculture (2020) Vol. 8, Iss. 1, pp. 27-51
Open Access | Times Cited: 359
YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3
Guoxu Liu, Nouaze Joseph Christian, Philippe Lyonel Touko Mbouembe, et al.
Sensors (2020) Vol. 20, Iss. 7, pp. 2145-2145
Open Access | Times Cited: 333
An in-field automatic wheat disease diagnosis system
Jiang Lu, Jie Hu, Guannan Zhao, et al.
Computers and Electronics in Agriculture (2017) Vol. 142, pp. 369-379
Open Access | Times Cited: 321
An autonomous strawberry‐harvesting robot: Design, development, integration, and field evaluation
Ya Xiong, Yuanyue Ge, Lars Grimstad, et al.
Journal of Field Robotics (2019) Vol. 37, Iss. 2, pp. 202-224
Open Access | Times Cited: 312
A Review of Convolutional Neural Network Applied to Fruit Image Processing
José Naranjo-Torres, Marco Mora, Ruber Hernández-García, et al.
Applied Sciences (2020) Vol. 10, Iss. 10, pp. 3443-3443
Open Access | Times Cited: 308
Integrating blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges
Mohamed Torky, Aboul Ella Hassanein
Computers and Electronics in Agriculture (2020) Vol. 178, pp. 105476-105476
Closed Access | Times Cited: 305
weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming
Inkyu Sa, Zetao Chen, Marija Popović, et al.
IEEE Robotics and Automation Letters (2017) Vol. 3, Iss. 1, pp. 588-595
Open Access | Times Cited: 300
The digitization of agricultural industry – a systematic literature review on agriculture 4.0
Rabiya Abbasi, Pablo Martı́nez, Rafiq Ahmad
Smart Agricultural Technology (2022) Vol. 2, pp. 100042-100042
Open Access | Times Cited: 299
Detection and segmentation of overlapped fruits based on optimized mask R-CNN application in apple harvesting robot
Weikuan Jia, Yuyu Tian, Rong Luo, et al.
Computers and Electronics in Agriculture (2020) Vol. 172, pp. 105380-105380
Closed Access | Times Cited: 287
Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association
Thiago Teixeira Santos, Leonardo L. de Souza, Andreza Santos, et al.
Computers and Electronics in Agriculture (2020) Vol. 170, pp. 105247-105247
Open Access | Times Cited: 284
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