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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 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: 52

Showing 1-25 of 52 citing articles:

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: 26
A systematic review of hyperspectral imaging in precision agriculture: Analysis of its current state and future prospects
Billy Graham Ram, Peter G. Oduor, C. Igathinathane, et al.
Computers and Electronics in Agriculture (2024) Vol. 222, pp. 109037-109037
Open Access | Times Cited: 22
Detection of Insect-Damaged Maize Seed Using Hyperspectral Imaging and Hybrid 1D-CNN-BiLSTM Model
Zheli Wang, Shuxiang Fan, Ting An, et al.
Infrared Physics & Technology (2024) Vol. 137, pp. 105208-105208
Closed Access | Times Cited: 10
Hyperspectral Image Analysis and Machine Learning Techniques for Crop Disease Detection and Identification: A Review
Yimy Edisson García Vera, Mauricio Andrés Polochè Arango, Camilo A. Mendivelso-Fajardo, et al.
Sustainability (2024) Vol. 16, Iss. 14, pp. 6064-6064
Open Access | Times Cited: 8
Non-destructive detection of defective maize kernels using hyperspectral imaging and convolutional neural network with attention module
Dong Yang, Yuxing Zhou, Yu Jie, et al.
Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (2024) Vol. 313, pp. 124166-124166
Closed Access | Times Cited: 7
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: 7
Fusion of hyperspectral imaging and electronic nose for identification of green vegetable in egg pancakes
Peipei Gao, Jing Liang, Wenlong Li, et al.
Microchemical Journal (2024) Vol. 199, pp. 110034-110034
Closed Access | Times Cited: 6
Robust Hyperspectral Image Classification Using Generative Adversarial Networks
Ziru Yu, Wei Cui
Information Sciences (2024) Vol. 666, pp. 120452-120452
Closed Access | Times Cited: 6
Aplicação de redes neurais convolucionais para treinamento de inteligência artificial na avaliação de embutidos de pescados
Rafael Pereira Frota, Felipe Costa de Souza, David de Souza Ferreira, et al.
OBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA (2025) Vol. 23, Iss. 1, pp. e8554-e8554
Closed Access
A high-precision spatial and spectral imaging solution for accurate corn nitrogen content level prediction at early vegetative growth stages
Jinnuo Zhang, Xing Wei, Zhihang Song, et al.
Computers and Electronics in Agriculture (2025) Vol. 230, pp. 109940-109940
Closed 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
An efficient nondestructive detection method of rapeseed varieties based on hyperspectral imaging technology
Jian Wang, Xin Zhou, Liu Yang, et al.
Microchemical Journal (2025), pp. 112913-112913
Closed Access
LSCA-net: A lightweight spectral convolution attention network for hyperspectral image processing
Ziru Yu, Wei Cui
Computers and Electronics in Agriculture (2023) Vol. 215, pp. 108382-108382
Closed Access | Times Cited: 11
Understanding the changes in millable oil content and fatty acid composition of infrared radiation treated mustard (Brassica juncea) seeds using Vis-NIR-SWIR hyperspectral imaging
Rajendra Hamad, Subir Kumar Chakraborty, Adinath Kate, et al.
Infrared Physics & Technology (2024) Vol. 137, pp. 105128-105128
Closed Access | Times Cited: 3
Portable Sensors in Precision Agriculture: Assessing Advances and Challenges in Soil Nutrient Determination
Abhishesh Pal, Satish Kumar Dubey, Sanket Goel, et al.
TrAC Trends in Analytical Chemistry (2024), pp. 117981-117981
Closed Access | Times Cited: 3
A Deep Spectral-Spatial Residual Attention Network for Hyperspectral Image Classification
Koushikey Chhapariya, Krishna Mohan Buddhiraju, Anil Kumar
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2024) Vol. 17, pp. 15393-15406
Open Access | Times Cited: 2
Estimating rice flower intensity using flower spectral information from unmanned aerial vehicle (UAV) hyperspectral images
Xiaoping Yao, Qiuxiang Yi, Fumin Wang, et al.
International Journal of Applied Earth Observation and Geoinformation (2023) Vol. 122, pp. 103415-103415
Open Access | Times Cited: 6
Identification of chrysanthemum variety via hyperspectral imaging and wavelength selection based on multitask particle swarm optimization
Yunpeng Wei, Huiqiang Hu, Huaxing Xu, et al.
Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (2024) Vol. 322, pp. 124812-124812
Closed Access | Times Cited: 1
Flexible temperature and humidity sensors of plants for precision agriculture: Current challenges and future roadmap
Muhammad Z. Ikram, Sikander Ameer, Fnu Kulsoom, et al.
Computers and Electronics in Agriculture (2024) Vol. 226, pp. 109449-109449
Closed Access | Times Cited: 1
Detection of Aspergillus flavus in Figs by Means of Hyperspectral Images and Deep Learning Algorithms
Cristian Cruz-Carrasco, Josefa Díaz, Francisco P. Chávez, et al.
AgriEngineering (2024) Vol. 6, Iss. 4, pp. 3969-3988
Open Access | Times Cited: 1
An overview of recent advancements in hyperspectral imaging in the egg and hatchery industry
Md Wadud Ahmed, Alin Khaliduzzaman, J.L. Emmert, et al.
Computers and Electronics in Agriculture (2024) Vol. 230, pp. 109847-109847
Open Access | Times Cited: 1
Spectrum imaging for phenotypic detection of greenhouse vegetables: A review
Xiangquan Gao, Shenghong Li, Yakai He, et al.
Computers and Electronics in Agriculture (2024) Vol. 225, pp. 109346-109346
Closed Access | Times Cited: 1
Non-destructive estimation for Kyoho grape shelf-life using Vis/NIR hyperspectral imaging and deep learning algorithm
Min Xu, Jun Sun, Jiehong Cheng, et al.
Infrared Physics & Technology (2024) Vol. 142, pp. 105532-105532
Closed Access | Times Cited: 1
Advances in Weed Identification Using Hyperspectral Imaging: A Comprehensive Review of Platform Sensors and Deep Learning Techniques
Bright Mensah, Nitin Rai, Kelvin Betitame, et al.
Journal of Agriculture and Food Research (2024) Vol. 18, pp. 101388-101388
Open Access | Times Cited: 1
A multi-verse optimizer-based CNN-BiLSTM pixel-level detection model for peanut aflatoxins
Cong Wang, Hongfei Zhu, Yifan Zhao, et al.
Food Chemistry (2024) Vol. 463, pp. 141393-141393
Closed Access | Times Cited: 1
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