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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:

Classification of weed using machine learning techniques: a review—challenges, current and future potential techniques
Ahmed Husham Al-Badri, Nor Azman Ismail, Khamael Al-Dulaimi, et al.
Journal of Plant Diseases and Protection (2022) Vol. 129, Iss. 4, pp. 745-768
Closed Access | Times Cited: 39

Showing 1-25 of 39 citing articles:

Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review
Hao-Ran Qu, Wen‐Hao Su
Agronomy (2024) Vol. 14, Iss. 2, pp. 363-363
Open Access | Times Cited: 20
YOLO-WDNet: A lightweight and accurate model for weeds detection in cotton field
Xiangpeng Fan, Tan Sun, Xiujuan Chai, et al.
Computers and Electronics in Agriculture (2024) Vol. 225, pp. 109317-109317
Closed Access | Times Cited: 16
Detection of Invasive Species (Siam Weed) Using Drone-Based Imaging and YOLO Deep Learning Model
Deepak Gautam, Zulfadli Mawardi, Louis Elliott, et al.
Remote Sensing (2025) Vol. 17, Iss. 1, pp. 120-120
Open Access | Times Cited: 2
Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles
Feng Yu, Qian Zhang, Jun Xiao, et al.
Remote Sensing (2023) Vol. 15, Iss. 12, pp. 2988-2988
Open Access | Times Cited: 24
Deep Learning-Based Weed Detection Using UAV Images: A Comparative Study
Tej Bahadur Shahi, Sweekar Dahal, Chiranjibi Sitaula, et al.
Drones (2023) Vol. 7, Iss. 10, pp. 624-624
Open Access | Times Cited: 24
A comprehensive survey on weed and crop classification using machine learning and deep learning
Faisal Dharma Adhinata, Wahyono Wahyono, Raden Sumiharto
Artificial Intelligence in Agriculture (2024) Vol. 13, pp. 45-63
Open Access | Times Cited: 15
Evaluation of YOLOv5 Models for Weed Classification in Crops Maize (Zea Mays L.)
Oscar Leonardo García-Navarrete, Ádela Fábregas García, Luis Manuel Navas Gracia
Springer tracts in nature-inspired computing (2025), pp. 183-209
Closed Access | Times Cited: 1
Research on improved YOLOx weed detection based on lightweight attention module
Huibin Zhu, Yuanyuan Zhang, Danlei Mu, et al.
Crop Protection (2023) Vol. 177, pp. 106563-106563
Open Access | Times Cited: 22
Application of Convolutional Neural Networks in Weed Detection and Identification: A Systematic Review.
Oscar Leonardo García-Navarrete, Adriana Corrêa-Guimarães, Luis Manuel Navas Gracia
(2024)
Open Access | Times Cited: 7
Identifying rice field weeds from unmanned aerial vehicle remote sensing imagery using deep learning
Zhonghui Guo, Dongdong Cai, Yunyi Zhou, et al.
Plant Methods (2024) Vol. 20, Iss. 1
Open Access | Times Cited: 7
Advancements of UAV and Deep Learning Technologies for Weed Management in Farmland
Jinmeng Zhang, Yu Feng, Qian Zhang, et al.
Agronomy (2024) Vol. 14, Iss. 3, pp. 494-494
Open Access | Times Cited: 5
Application of Convolutional Neural Networks in Weed Detection and Identification: A Systematic Review
Oscar Leonardo García-Navarrete, Adriana Corrêa-Guimarães, Luis Manuel Navas Gracia
Agriculture (2024) Vol. 14, Iss. 4, pp. 568-568
Open Access | Times Cited: 5
Optimizing crop nitrogen use efficiency: Integrating root performance and machine learning into nutrient management
Guoqi Wen, B. L.
Advances in agronomy (2024), pp. 311-363
Closed Access | Times Cited: 5
Key Technologies of Intelligent Weeding for Vegetables: A Review
Jinkang Jiao, Ying Zang, Chaowen Chen
Agriculture (2024) Vol. 14, Iss. 8, pp. 1378-1378
Open Access | Times Cited: 5
Hybrid CNN Model for Classification of Rumex Obtusifolius in Grassland
Ahmed Husham Al-Badri, Nor Azman Ismail, Khamael Al-Dulaimi, et al.
IEEE Access (2022) Vol. 10, pp. 90940-90957
Open Access | Times Cited: 19
A Comparative Study of ResNet50, EfficientNetB7, InceptionV3, VGG16 models in Crop and Weed classification
Naman Mishra, Ishrat Jahan, Md. Raghib Nadeem, et al.
(2023), pp. 1-5
Closed Access | Times Cited: 11
SFL-MobileNetV3: A lightweight network for weed recognition in tropical cassava fields in China
Jiali Zi, W. Hu, Guangpeng Fan, et al.
Expert Systems with Applications (2025), pp. 127196-127196
Open Access
Performance Analysis of Real-Time Detection Transformer and You Only Look Once Models for Weed Detection in Maize Cultivation
Oscar Leonardo García-Navarrete, Jesús Hernán Camacho-Tamayo, Aníbal Bregón, et al.
Agronomy (2025) Vol. 15, Iss. 4, pp. 796-796
Open Access
Enhancing Spatial-Spectral Analysis of Hyperspectral and Multispectral Satellite Imagery with Multilevel Thresholding and Multi-Otsu Segmentation
Anindita Das Bhattacharjee, Srijita Sarkar, Snehil Dey
Intelligent systems reference library (2025), pp. 155-177
Closed Access
Adaptive Non-Maximum Suppression for improving performance of Rumex detection
Ahmed Husham Al-Badri, Nor Azman Ismail, Khamael Al-Dulaimi, et al.
Expert Systems with Applications (2023) Vol. 219, pp. 119634-119634
Closed Access | Times Cited: 9
Multilayer feature fusion and attention-based network for crops and weeds segmentation
Haoyu Wang, Haiyu Song, H. Y. Wu, et al.
Journal of Plant Diseases and Protection (2022) Vol. 129, Iss. 6, pp. 1475-1489
Closed Access | Times Cited: 11
Enhancing Agricultural Precision: A Hybrid CNN-SVM Approach for Multidimensional Weed Disease Classification
Deepak Upadhyay, Manika Manwal, P. Gagan Deep, et al.
(2024), pp. 1525-1528
Closed Access | Times Cited: 2
Deep Learning for Weed Detection: Exploring YOLO V8 Algorithm's Performance in Agricultural Environments
Pushpendra Kumar, Upendra Misra
(2024), pp. 255-258
Closed Access | Times Cited: 2
Effect of the Red-Edge Band from Drone Altum Multispectral Camera in Mapping the Canopy Cover of Winter Wheat, Chickweed, and Hairy Buttercup
Clement E. Akumu, Sam Dennis
Drones (2023) Vol. 7, Iss. 4, pp. 277-277
Open Access | Times Cited: 4
Sugar beet farming goes high-tech: a method for automated weed detection using machine learning and deep learning in precision agriculture
Fatma Nur Ortataş, Umut Özkaya, Muhammet Emin Şahin, et al.
Neural Computing and Applications (2023) Vol. 36, Iss. 9, pp. 4603-4622
Closed Access | Times Cited: 4
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