Open Access Helper

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:

County-scale crop yield prediction by integrating crop simulation with machine learning models
Saiara Samira Sajid, Mohsen Shahhosseini, Isaiah Huber, et al.
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 21

Showing 21 citing articles:

A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron
Shakeel Ahmed
Sustainability (2023) Vol. 15, Iss. 4, pp. 3017-3017
Open Access | Times Cited: 21
Predicting rice phenology across China by integrating crop phenology model and machine learning
Jinhan Zhang, Xiaomao Lin, Chongya Jiang, et al.
The Science of The Total Environment (2024) Vol. 951, pp. 175585-175585
Closed Access | Times Cited: 6
Crop Yield Prediction in Agriculture: A Comprehensive Review of Machine Learning and Deep Learning Approaches, with Insights for Future Research and Sustainability
Md Abu Jabed, Masrah Azrifah Azmi Murad
Heliyon (2024) Vol. 10, Iss. 24, pp. e40836-e40836
Open Access | Times Cited: 4
The octoPus: An open-source software for supporting farmers in the control of grapevine downy mildew
Simone Bregaglio, Eleonora Del Cavallo, Lorenzo Ascari, et al.
SoftwareX (2025) Vol. 30, pp. 102085-102085
Closed Access
Enhancing precision agriculture through cloud based transformative crop recommendation model
Gurpreet Singh, Sandeep Sharma
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
An efficient diagnosis of misinformation in online networking using logistic regression in comparison with decision tree
Rohith, Malathi Kanagasabai
AIP conference proceedings (2025) Vol. 3270, pp. 020123-020123
Closed Access
An Integrated Analysis of Yield Prediction Models: A Comprehensive Review of Advancements and Challenges
Nidhi Parashar, Prashant Johri, Arfat Ahmad Khan, et al.
Computers, materials & continua/Computers, materials & continua (Print) (2024) Vol. 80, Iss. 1, pp. 389-425
Open Access | Times Cited: 2
The role of phenology in crop yield prediction: Comparison of ground-based phenology and remotely sensed phenology
Jie Pei, Shaofeng Tan, Yaopeng Zou, et al.
Agricultural and Forest Meteorology (2024) Vol. 361, pp. 110340-110340
Closed Access | Times Cited: 2
Investigation of genetic diversity of different spring rapeseed (Brassica napus L.) genotypes and yield prediction using machine learning models
Mohamad Amin Norouzi, Leila Ahangar, Kamal Payghamzadeh, et al.
Genetic Resources and Crop Evolution (2024) Vol. 71, Iss. 8, pp. 4519-4532
Closed Access | Times Cited: 1
Machine Learning-based Extrapolation of Crop Cultivation Cost
Poonam Bari, Lata Ragha
INTELIGENCIA ARTIFICIAL (2024) Vol. 27, Iss. 74, pp. 80-101
Open Access | Times Cited: 1
Ensemble of best linear unbiased predictor, machine learning and deep learning models predict maize yield better than each model alone
Daniel R Kick, Jacob D. Washburn
in silico Plants (2023) Vol. 5, Iss. 2
Open Access | Times Cited: 2
Predicting and Explaining for Rice Phenology Across China by Integrating Crop Model and Interpretable Machine Learning
Jinhan Zhang, Xiaomao Lin, Chongya Jiang, et al.
(2024)
Closed Access
Releasing theoctoPus,an open-source digital tool to promote Integrated Pest Management
Simone Bregaglio, Eugenio Rossi, Lorenzo Ascari, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Crop yield estimation uncertainties at the regional scale for Saxony, Germany
Sebastian Goihl
Agronomy Journal (2024)
Open Access
Corn Yield Prediction Based on Dynamic Integrated Stacked Regression
Xiangjuan Liu, Qiaonan Yang, Runjun Yang, et al.
Agriculture (2024) Vol. 14, Iss. 10, pp. 1829-1829
Open Access
Challenges and opportunities in Machine learning for bioenergy crop yield Prediction: A review
Joseph Lepnaan Dayil, Olugbenga Akande, Alaa El Din Mahmoud, et al.
Sustainable Energy Technologies and Assessments (2024) Vol. 73, pp. 104057-104057
Closed Access
AI-Based Paddy Rice Yield Prediction Using Satellite Images, Meteorological Data, and Digital Elevation Model: Case Study of South Korea, 2000–2023
Jaeung Sim, Jaeil Cho, Kyungdo Lee, et al.
Korean Journal of Remote Sensing (2024) Vol. 40, Iss. 6, pp. 1195-1208
Open Access
Enhanced Deep Learning Satellite-based Model for Yield Forecasting and Quality Assurance Using Metamorphic Testing
Islam Nasr, Lobna Nassar, Fakhri Karray, et al.
2022 International Joint Conference on Neural Networks (IJCNN) (2023), pp. 1-6
Closed Access | Times Cited: 1
Ensemble of BLUP, Machine Learning, and Deep Learning Models Predict Maize Yield Better Than Each Model Alone
Daniel R Kick, Jacob D. Washburn
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access
Ensemble of BLUP, Machine Learning, and Deep Learning Models Predict Maize Yield Better Than Each Model Alone
Daniel R Kick, Jacob D. Washburn
Research Square (Research Square) (2023)
Open Access
Integration of Cloud-Sensor Based Framework for Predictive Analysis for Accelerating Knowledge Gain in Agriculture
N Sneha, Meenakshi Sundaram, Rajeev Ranjan
(2023), pp. 31-36
Closed Access
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