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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:

Plant disease recognition in a low data scenario using few-shot learning
Masoud Rezaei, Dean Diepeveen, Hamid Laga, et al.
Computers and Electronics in Agriculture (2024) Vol. 219, pp. 108812-108812
Open Access | Times Cited: 18

Showing 18 citing articles:

Smartphone-Based Citizen Science Tool for Plant Disease and Insect Pest Detection Using Artificial Intelligence
Panagiotis Christakakis, Garyfallia Papadopoulou, Georgios Mikos, et al.
Technologies (2024) Vol. 12, Iss. 7, pp. 101-101
Open Access | Times Cited: 8
Artificial Intelligence Tools for the Agriculture Value Chain: Status and Prospects
Fotis Assimakopoulos, Costas Vassilakis, Dionisis Margaris, et al.
Electronics (2024) Vol. 13, Iss. 22, pp. 4362-4362
Open Access | Times Cited: 5
Evaluation of Different Few-Shot Learning Methods in the Plant Disease Classification Domain
Alexander Uzhinskiy
Biology (2025) Vol. 14, Iss. 1, pp. 99-99
Open Access
Transfer learning in agriculture: a review
Md Ismail Hossen, Mohammad Awrangjeb, Shirui Pan, et al.
Artificial Intelligence Review (2025) Vol. 58, Iss. 4
Open Access
Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review
Shaohua Wang, Dachuan Xu, Haojian Liang, et al.
Remote Sensing (2025) Vol. 17, Iss. 4, pp. 698-698
Open Access
An Efficient Model for Leafy Vegetable Disease Detection and Segmentation Based on Few-Shot Learning Framework and Prototype Attention Mechanism
Tong Hai, Yuxin Shao, Xiyan Zhang, et al.
Plants (2025) Vol. 14, Iss. 5, pp. 760-760
Open Access
Barley disease recognition using deep neural networks
Masoud Rezaei, Sanjiv Gupta, Dean Diepeveen, et al.
European Journal of Agronomy (2024) Vol. 161, pp. 127359-127359
Open Access | Times Cited: 3
Spectral–Spatial transformer-based semantic segmentation for large-scale mapping of individual date palm trees using very high-resolution satellite data
Rami Al‐Ruzouq, Mohamed Barakat A. Gibril, Abdallah Shanableh, et al.
Ecological Indicators (2024) Vol. 163, pp. 112110-112110
Open Access | Times Cited: 2
Nondestructive in-ovo sexing of Hy-Line Sonia eggs by EggFormer using hyperspectral imaging
Chengming Ji, Kechen Song, Zixin Chen, et al.
Computers and Electronics in Agriculture (2024) Vol. 225, pp. 109298-109298
Closed Access | Times Cited: 2
Residual swin transformer for classifying the types of cotton pests in complex background
Ting Zhang, Jikui Zhu, Fengkui Zhang, et al.
Frontiers in Plant Science (2024) Vol. 15
Open Access | Times Cited: 1
From Dataset Creation to Defect Detection: A Proposed Procedure for a Custom CNN Approach for Polishing Applications on Low-Performance PCs
Albin Bajrami, Matteo-Claudio Palpacelli
Machines (2024) Vol. 12, Iss. 7, pp. 453-453
Open Access
Rice Leaf Disease Stages Classification Using Few-shot Learning Techniques
Diana Susan Joseph, Pranav M. Pawar
(2024), pp. 13-18
Closed Access
Transfer Learning for Classification of Horticultural Maladies: Leveraging Pre-Trained Models for Disease Detection in Crop Plants
Raj Gaurang Tiwari, Alok Misra, Krishan Dutt, et al.
(2024), pp. 1-5
Closed Access
Diagnosis of Custard Apple Disease Based on Adaptive Information Entropy Data Augmentation and Multiscale Region Aggregation Interactive Visual Transformers
Kunpeng Cui, Jianbo Huang, Guowei Dai, et al.
Agronomy (2024) Vol. 14, Iss. 11, pp. 2605-2605
Open Access
Morphology-based weed type recognition using Siamese network
A S M Mahmudul Hasan, Dean Diepeveen, Hamid Laga, et al.
European Journal of Agronomy (2024) Vol. 163, pp. 127439-127439
Open Access
A transformer-based few-shot learning pipeline for barley disease detection from field-collected imagery
Masoud Rezaei, Dean Diepeveen, Hamid Laga, et al.
Computers and Electronics in Agriculture (2024) Vol. 229, pp. 109751-109751
Open Access
Deep learning for plant stress detection: A comprehensive review of technologies, challenges, and future directions
Nijhum Paul, G C Sunil, David J. Horvath, et al.
Computers and Electronics in Agriculture (2024) Vol. 229, pp. 109734-109734
Open Access
From laboratory to field: cross-domain few-shot learning for crop disease identification in the field
Sen Yang, Quan Feng, Jianhua Zhang, et al.
Frontiers in Plant Science (2024) Vol. 15
Open Access
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