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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:

Machine Learning in Agriculture: A Review
Κωνσταντίνος Λιάκος, Patrizia Busato, Dimitrios Moshou, et al.
Sensors (2018) Vol. 18, Iss. 8, pp. 2674-2674
Open Access | Times Cited: 2148

Showing 26-50 of 2148 citing articles:

County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model
Jie Sun, Liping Di, Ziheng Sun, et al.
Sensors (2019) Vol. 19, Iss. 20, pp. 4363-4363
Open Access | Times Cited: 282
Global Dimensions of Plant Virus Diseases: Current Status and Future Perspectives
Richard A. Jones, R. A. Naidu
Annual Review of Virology (2019) Vol. 6, Iss. 1, pp. 387-409
Open Access | Times Cited: 256
Artificial Intelligence in Education: AIEd for Personalised Learning Pathways
Olga Tapalova, Nadezhda Zhiyenbayeva
The Electronic Journal of e-Learning (2022) Vol. 20, Iss. 5, pp. 639-653
Open Access | Times Cited: 249
Machine learning in agriculture domain: A state-of-art survey
Vishal Meshram, Kailas Patil, Vidula Meshram, et al.
Artificial Intelligence in the Life Sciences (2021) Vol. 1, pp. 100010-100010
Open Access | Times Cited: 242
Machine learning-based approach: global trends, research directions, and regulatory standpoints
Raffaele Pugliese, Stefano Regondi, Riccardo Marini
Data Science and Management (2021) Vol. 4, pp. 19-29
Open Access | Times Cited: 223
Robotics and labour in agriculture. A context consideration
Vasso Marinoudi, Claus Aage Grøn Sørensen, Simon Pearson, et al.
Biosystems Engineering (2019) Vol. 184, pp. 111-121
Closed Access | Times Cited: 222
Integrated phenology and climate in rice yields prediction using machine learning methods
Yahui Guo, Yongshuo H. Fu, Fanghua Hao, et al.
Ecological Indicators (2020) Vol. 120, pp. 106935-106935
Open Access | Times Cited: 218
Machine learning for large-scale crop yield forecasting
Dilli Paudel, Hendrik Boogaard, Allard de Wit, et al.
Agricultural Systems (2020) Vol. 187, pp. 103016-103016
Open Access | Times Cited: 218
The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations
Josh Cowls, Andreas Tsamados, Mariarosaria Taddeo, et al.
AI & Society (2021) Vol. 38, Iss. 1, pp. 283-307
Open Access | Times Cited: 214
Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran
Mostafa Emadi, Ruhollah Taghizadeh‐Mehrjardi, Ali Cherati, et al.
Remote Sensing (2020) Vol. 12, Iss. 14, pp. 2234-2234
Open Access | Times Cited: 213
A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices
Ana Paula Marques Ramos, Lucas Prado Osco, Danielle Elis Garcia Furuya, et al.
Computers and Electronics in Agriculture (2020) Vol. 178, pp. 105791-105791
Closed Access | Times Cited: 211
Water quality prospective in Twenty First Century: Status of water quality in major river basins, contemporary strategies and impediments: A review
Subhasis Giri
Environmental Pollution (2020) Vol. 271, pp. 116332-116332
Closed Access | Times Cited: 210
A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction
Mamunur Rashid, Bifta Sama Bari, Yusri Yusup, et al.
IEEE Access (2021) Vol. 9, pp. 63406-63439
Open Access | Times Cited: 208
An overview of smart irrigation systems using IoT
Khaled Obaideen, Bashria A.A. Yousef, Maryam Nooman AlMallahi, et al.
Energy Nexus (2022) Vol. 7, pp. 100124-100124
Open Access | Times Cited: 208
Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture
Yemeserach Mekonnen, Srikanth Namuduri, L. K. BURTON, et al.
Journal of The Electrochemical Society (2019) Vol. 167, Iss. 3, pp. 037522-037522
Open Access | Times Cited: 206
Review of Weed Detection Methods Based on Computer Vision
Zhangnan Wu, Yajun Chen, Bo Zhao, et al.
Sensors (2021) Vol. 21, Iss. 11, pp. 3647-3647
Open Access | Times Cited: 203
Deep learning for image-based weed detection in turfgrass
Jialin Yu, Shaun M. Sharpe, Arnold W. Schumann, et al.
European Journal of Agronomy (2019) Vol. 104, pp. 78-84
Closed Access | Times Cited: 201
Improving Unmanned Aerial Vehicle Remote Sensing-Based Rice Nitrogen Nutrition Index Prediction with Machine Learning
Hainie Zha, Yuxin Miao, Tiantian Wang, et al.
Remote Sensing (2020) Vol. 12, Iss. 2, pp. 215-215
Open Access | Times Cited: 201
Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV
Orly Enrique Apolo-Apolo, Jorge Martínez-Guanter, Gregorio Egea, et al.
European Journal of Agronomy (2020) Vol. 115, pp. 126030-126030
Closed Access | Times Cited: 200
Characterising the Agriculture 4.0 Landscape—Emerging Trends, Challenges and Opportunities
Sara Oleiro Araújo, Ricardo Silva Peres, José Barata, et al.
Agronomy (2021) Vol. 11, Iss. 4, pp. 667-667
Open Access | Times Cited: 196
Do we really need deep CNN for plant diseases identification?
Yang Li, Jing Nie, Xuewei Chao
Computers and Electronics in Agriculture (2020) Vol. 178, pp. 105803-105803
Closed Access | Times Cited: 193
Artificial Intelligence to Improve the Food and Agriculture Sector
Rayda Ben Ayed, Mohsen Hanana
Journal of Food Quality (2021) Vol. 2021, pp. 1-7
Open Access | Times Cited: 190
Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers
Muhammad Hammad Saleem, Johan Potgieter, Khalid Mahmood Arif
Plants (2020) Vol. 9, Iss. 10, pp. 1319-1319
Open Access | Times Cited: 184
Application of machine learning in intelligent fish aquaculture: A review
Shili Zhao, Song Zhang, Jincun Liu, et al.
Aquaculture (2021) Vol. 540, pp. 736724-736724
Closed Access | Times Cited: 184
Machine learning and deep learning—A review for ecologists
Maximilian Pichler, Florian Härtig
Methods in Ecology and Evolution (2023) Vol. 14, Iss. 4, pp. 994-1016
Open Access | Times Cited: 183

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