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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:

Hyperspectral imaging with shallow convolutional neural networks (SCNN) predicts the early herbicide stress in wheat cultivars
Hangjian Chu, Chu Zhang, Mengcen Wang, et al.
Journal of Hazardous Materials (2021) Vol. 421, pp. 126706-126706
Closed Access | Times Cited: 42

Showing 1-25 of 42 citing articles:

A review on the combination of deep learning techniques with proximal hyperspectral images in agriculture
Jayme Garcia Arnal Barbedo
Computers and Electronics in Agriculture (2023) Vol. 210, pp. 107920-107920
Closed Access | Times Cited: 68
A research review on deep learning combined with hyperspectral Imaging in multiscale agricultural sensing
Luyu Shuai, Zhiyong Li, Ziao Chen, et al.
Computers and Electronics in Agriculture (2024) Vol. 217, pp. 108577-108577
Closed Access | Times Cited: 40
Machine learning based recommendation of agricultural and horticultural crop farming in India under the regime of NPK, soil pH and three climatic variables
Biplob Dey, Jannatul Ferdous, Romel Ahmed
Heliyon (2024) Vol. 10, Iss. 3, pp. e25112-e25112
Open Access | Times Cited: 21
Proximal hyperspectral sensing of abiotic stresses in plants
Alireza Sanaeifar, Ce Yang, Miguel de la Guárdia, et al.
The Science of The Total Environment (2022) Vol. 861, pp. 160652-160652
Closed Access | Times Cited: 56
Application of hyperspectral imaging assisted with integrated deep learning approaches in identifying geographical origins and predicting nutrient contents of Coix seeds
Youyou Wang, Feng Xiong, Yue Zhang, et al.
Food Chemistry (2022) Vol. 404, pp. 134503-134503
Closed Access | Times Cited: 52
Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics
Mohamed Farag Taha, Alwaseela Abdalla, Gamal ElMasry, et al.
Chemosensors (2022) Vol. 10, Iss. 2, pp. 45-45
Open Access | Times Cited: 46
ResNet incorporating the fusion data of RGB & hyperspectral images improves classification accuracy of vegetable soybean freshness
Yuanpeng Bu, Jinxuan Hu, Cheng Chen, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 13
A necessary considering factor for crop resistance: Precise regulation and effective utilization of beneficial microorganisms
Chenxi Kou, Feiyang Song, Dandan Li, et al.
New Crops (2024) Vol. 1, pp. 100023-100023
Open Access | Times Cited: 12
The application of hyperspectral imaging for wheat biotic and abiotic stress analysis: A review
Kun Zhang, Fangfang Yan, Ping Liu
Computers and Electronics in Agriculture (2024) Vol. 221, pp. 109008-109008
Closed Access | Times Cited: 12
Early detection of nicosulfuron toxicity and physiological prediction in maize using multi-branch deep learning models and hyperspectral imaging
Tianpu Xiao, Yang Li, Dongxing Zhang, et al.
Journal of Hazardous Materials (2024) Vol. 474, pp. 134723-134723
Closed Access | Times Cited: 12
End-to-End Fusion of Hyperspectral and Chlorophyll Fluorescence Imaging to Identify Rice Stresses
Chu Zhang, Lei Zhou, Qinlin Xiao, et al.
Plant Phenomics (2022) Vol. 2022
Open Access | Times Cited: 32
MachIne learning for nutrient recovery in the smart city circular economy – A review
Allan Soo, Li Wang, Chen Wang, et al.
Process Safety and Environmental Protection (2023) Vol. 173, pp. 529-557
Closed Access | Times Cited: 22
Fast prediction of diverse rare ginsenoside contents in Panax ginseng through hyperspectral imaging assisted with the temporal convolutional network-attention mechanism (TCNA) deep learning
Youyou Wang, Siman Wang, Yuwei Yuan, et al.
Food Control (2024) Vol. 162, pp. 110455-110455
Closed Access | Times Cited: 7
Hyperspectral remote sensing to assess weed competitiveness in maize farmland ecosystems
Zhaoxia Lou, Longzhe Quan, Deng Sun, et al.
The Science of The Total Environment (2022) Vol. 844, pp. 157071-157071
Closed Access | Times Cited: 24
Detecting Asymptomatic Infections of Rice Bacterial Leaf Blight Using Hyperspectral Imaging and 3-Dimensional Convolutional Neural Network With Spectral Dilated Convolution
Yifei Cao, Peisen Yuan, Huanliang Xu, et al.
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 24
The Recent Development of Acoustic Sensors as Effective Chemical Detecting Tools for Biological Cells and Their Bioactivities
Mostafa Gouda, Hesham S. Ghazzawy, Nashi K. Alqahtani, et al.
Molecules (2023) Vol. 28, Iss. 12, pp. 4855-4855
Open Access | Times Cited: 14
Rapid assessment of heavy metal accumulation capability of Sedum alfredii using hyperspectral imaging and deep learning
Yi Lu, Linjie Nie, Xinyu Guo, et al.
Ecotoxicology and Environmental Safety (2024) Vol. 282, pp. 116704-116704
Open Access | Times Cited: 5
An enhanced approach for leaf disease identification and classification using deep learning techniques
A. Umamageswari, S. Deepa, Karthik Raja
Measurement Sensors (2022) Vol. 24, pp. 100568-100568
Open Access | Times Cited: 22
Weed resistance assessment through airborne multimodal data fusion and deep learning: A novel approach towards sustainable agriculture
Fulin Xia, Zhaoxia Lou, Deng Sun, et al.
International Journal of Applied Earth Observation and Geoinformation (2023) Vol. 120, pp. 103352-103352
Open Access | Times Cited: 13
Artificial Intelligence for Maximizing Agricultural Input Use Efficiency: Exploring Nutrient, Water and Weed Management Strategies
Sumit Sow, Shivani Ranjan, Mahmoud F. Seleiman, et al.
Phyton (2024) Vol. 93, Iss. 7, pp. 1569-1598
Open Access | Times Cited: 4
Early prediction of maize resistance to nicosulfuron using hyperspectral imaging and deep learning: Method and mechanism
Tianpu Xiao, Yang Li, Dongxing Zhang, et al.
Computers and Electronics in Agriculture (2024) Vol. 227, pp. 109511-109511
Closed Access | Times Cited: 4
Predicting rice diseases using advanced technologies at different scales: present status and future perspectives
Ruyue Li, Sishi Chen, Haruna Matsumoto, et al.
aBIOTECH (2023) Vol. 4, Iss. 4, pp. 359-371
Open Access | Times Cited: 11
Rapid Quality Evaluation of Moutan Cortex (Paeonia suffruticosa Andrews) by Near-infrared Spectroscopy and Bionic Swarm Intelligent Optimization Algorithm
Ying Qiao, Yatong Kang, Ting Long, et al.
Journal of Pharmaceutical and Biomedical Analysis (2025), pp. 116822-116822
Closed Access
A Review of Artificial Intelligence Techniques for Wheat Crop Monitoring and Management
Jayme Garcia Arnal Barbedo
Agronomy (2025) Vol. 15, Iss. 5, pp. 1157-1157
Open Access
A novel labor-free method for isolating crop leaf pixels from RGB imagery: Generating labels via a topological strategy
Xusheng Ji, Zhenjiang Zhou, Mostafa Gouda, et al.
Computers and Electronics in Agriculture (2024) Vol. 218, pp. 108631-108631
Closed Access | Times Cited: 3
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