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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:

Estimating and understanding crop yields with explainable deep learning in the Indian Wheat Belt
Aleksandra Wolanin, Gonzalo Mateo‐García, Gustau Camps‐Valls, et al.
Environmental Research Letters (2020) Vol. 15, Iss. 2, pp. 024019-024019
Open Access | Times Cited: 139

Showing 1-25 of 139 citing articles:

Crop yield prediction using machine learning: A systematic literature review
Thomas van Klompenburg, Ayalew Kassahun, Cagatay Catal
Computers and Electronics in Agriculture (2020) Vol. 177, pp. 105709-105709
Open Access | Times Cited: 1097
Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming
Tawseef Ayoub Shaikh, Tabasum Rasool, Faisal Rasheed Lone
Computers and Electronics in Agriculture (2022) Vol. 198, pp. 107119-107119
Closed Access | Times Cited: 415
Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities
Lefei Zhang, Liangpei Zhang
IEEE Geoscience and Remote Sensing Magazine (2022) Vol. 10, Iss. 2, pp. 270-294
Closed Access | Times Cited: 302
Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-Producing Regions of China with Deep Learning Approaches
Xinlei Wang, Jianxi Huang, Quanlong Feng, et al.
Remote Sensing (2020) Vol. 12, Iss. 11, pp. 1744-1744
Open Access | Times Cited: 207
A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing
Priyanga Muruganantham, Santoso Wibowo, Srimannarayana Grandhi, et al.
Remote Sensing (2022) Vol. 14, Iss. 9, pp. 1990-1990
Open Access | Times Cited: 170
Understanding deep learning in land use classification based on Sentinel-2 time series
Manuel Campos‐Taberner, Francisco Javier Garcı́a-Haro, Beatriz Martínez, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 163
Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges
Kashif Ahmad, Majdi Maabreh, Mohamed Ghaly, et al.
Computer Science Review (2021) Vol. 43, pp. 100452-100452
Closed Access | Times Cited: 148
An LSTM neural network for improving wheat yield estimates by integrating remote sensing data and meteorological data in the Guanzhong Plain, PR China
Huiren Tian, Pengxin Wang, Kevin Tansey, et al.
Agricultural and Forest Meteorology (2021) Vol. 310, pp. 108629-108629
Open Access | Times Cited: 136
Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing
Ioannis Kakogeorgiou, Κωνσταντίνος Καράντζαλος
International Journal of Applied Earth Observation and Geoinformation (2021) Vol. 103, pp. 102520-102520
Open Access | Times Cited: 104
Deep Learning and Earth Observation to Support the Sustainable Development Goals: Current approaches, open challenges, and future opportunities
Claudio Persello, Jan Dirk Wegner, Ronny Hänsch, et al.
IEEE Geoscience and Remote Sensing Magazine (2022) Vol. 10, Iss. 2, pp. 172-200
Open Access | Times Cited: 86
Sentiment Analysis of Customer Reviews of Food Delivery Services Using Deep Learning and Explainable Artificial Intelligence: Systematic Review
Anirban Adak, Biswajeet Pradhan, Nagesh Shukla
Foods (2022) Vol. 11, Iss. 10, pp. 1500-1500
Open Access | Times Cited: 81
Forecasting vegetation indices from spatio-temporal remotely sensed data using deep learning-based approaches: A systematic literature review
Aya Ferchichi, Ali Ben Abbes, Vincent Barra, et al.
Ecological Informatics (2022) Vol. 68, pp. 101552-101552
Closed Access | Times Cited: 79
Remote-Sensing Data and Deep-Learning Techniques in Crop Mapping and Yield Prediction: A Systematic Review
Abhasha Joshi, Biswajeet Pradhan, Shilpa Gite, et al.
Remote Sensing (2023) Vol. 15, Iss. 8, pp. 2014-2014
Open Access | Times Cited: 72
Machine Learning for Smart Agriculture and Precision Farming: Towards Making the Fields Talk
Tawseef Ayoub Shaikh, Waseem Ahmad Mir, Tabasum Rasool, et al.
Archives of Computational Methods in Engineering (2022) Vol. 29, Iss. 7, pp. 4557-4597
Closed Access | Times Cited: 71
Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis
Zhe Liu, Shuzhe Wang, Yudong Zhang, et al.
Foods (2023) Vol. 12, Iss. 6, pp. 1242-1242
Open Access | Times Cited: 54
Interpretability of deep learning models for crop yield forecasting
Dilli Paudel, Allard de Wit, Hendrik Boogaard, et al.
Computers and Electronics in Agriculture (2023) Vol. 206, pp. 107663-107663
Open Access | Times Cited: 52
Prospects of microgreens as budding living functional food: Breeding and biofortification through OMICS and other approaches for nutritional security
Astha Gupta, Tripti Sharma, Surendra Pratap Singh, et al.
Frontiers in Genetics (2023) Vol. 14
Open Access | Times Cited: 46
Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield
Tongxi Hu, Xuesong Zhang, Gil Bohrer, et al.
Agricultural and Forest Meteorology (2023) Vol. 336, pp. 109458-109458
Open Access | Times Cited: 44
Predicting Crop Yield Using Deep Learning and Remote Sensing
Jasmin Praful Bharadiya, Nikolaos Tzenios, Manjunath Reddy
Journal of Engineering Research and Reports (2023) Vol. 24, Iss. 12, pp. 29-44
Open Access | Times Cited: 39
Remote sensing revolutionizing agriculture: Toward a new frontier
Xiaoding Wang, Haitao Zeng, Xu Yang, et al.
Future Generation Computer Systems (2025) Vol. 166, pp. 107691-107691
Closed Access | Times Cited: 1
A million kernels of truth: Insights into scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt
Jillian M. Deines, R. N. Patel, Sang-Zi Liang, et al.
Remote Sensing of Environment (2020) Vol. 253, pp. 112174-112174
Open Access | Times Cited: 95
Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea
Seungtaek Jeong, Jonghan Ko, Jong‐Min Yeom
The Science of The Total Environment (2021) Vol. 802, pp. 149726-149726
Open Access | Times Cited: 95
Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey
Thomas Rojat, Raphaël Puget, David Filliat, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 90
Yield forecasting with machine learning and small data: What gains for grains?
Michele Meroni, François Waldner, Lorenzo Seguini, et al.
Agricultural and Forest Meteorology (2021) Vol. 308-309, pp. 108555-108555
Open Access | Times Cited: 76
Benchmarking Deep Learning Models for Cloud Detection in Landsat-8 and Sentinel-2 Images
Dan López-Puigdollers, Gonzalo Mateo‐García, Luis Gómez‐Chova
Remote Sensing (2021) Vol. 13, Iss. 5, pp. 992-992
Open Access | Times Cited: 64
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