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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:

Deep convolutional neural networks for estimating maize above-ground biomass using multi-source UAV images: a comparison with traditional machine learning algorithms
Danyang Yu, Yuanyuan Zha, Zhigang Sun, et al.
Precision Agriculture (2022) Vol. 24, Iss. 1, pp. 92-113
Closed Access | Times Cited: 46

Showing 1-25 of 46 citing articles:

Improving potato AGB estimation to mitigate phenological stage impacts through depth features from hyperspectral data
Yang Liu, Haikuan Feng, Jibo Yue, et al.
Computers and Electronics in Agriculture (2024) Vol. 219, pp. 108808-108808
Closed Access | Times Cited: 48
A Review of CNN Applications in Smart Agriculture Using Multimodal Data
Mohammad El Sakka, Mihai Ivanovici, Lotfi Chaâri, et al.
Sensors (2025) Vol. 25, Iss. 2, pp. 472-472
Open Access | Times Cited: 5
Improved Estimation of Aboveground Biomass in Rubber Plantations Using Deep Learning on UAV Multispectral Imagery
Hongjian Tan, Weili Kou, Weiheng Xu, et al.
Drones (2025) Vol. 9, Iss. 1, pp. 32-32
Open Access | Times Cited: 2
Improving estimation of maize leaf area index by combining of UAV-based multispectral and thermal infrared data: The potential of new texture index
Ning Yang, Zhitao Zhang, Junrui Zhang, et al.
Computers and Electronics in Agriculture (2023) Vol. 214, pp. 108294-108294
Closed Access | Times Cited: 31
CatBoost algorithm for estimating maize above-ground biomass using unmanned aerial vehicle-based multi-source sensor data and SPAD values
Weiguang Zhai, Changchun Li, Shuaipeng Fei, et al.
Computers and Electronics in Agriculture (2023) Vol. 214, pp. 108306-108306
Closed Access | Times Cited: 27
Advancements in Utilizing Image-Analysis Technology for Crop-Yield Estimation
Yu Feng, Ming Wang, Jun Xiao, et al.
Remote Sensing (2024) Vol. 16, Iss. 6, pp. 1003-1003
Open Access | Times Cited: 9
Enhancing precision of root-zone soil moisture content prediction in a kiwifruit orchard using UAV multi-spectral image features and ensemble learning
Shidan Zhu, Ningbo Cui, Li Guo, et al.
Computers and Electronics in Agriculture (2024) Vol. 221, pp. 108943-108943
Closed Access | Times Cited: 9
A study on optimal input images for rice yield prediction models using CNN with UAV imagery and its reasoning using explainable AI
Tomoaki Yamaguchi, T. TAKAMURA, Takashi Tanaka, et al.
European Journal of Agronomy (2025) Vol. 164, pp. 127512-127512
Open Access | Times Cited: 1
Estimation of Rice Aboveground Biomass by UAV Imagery with Photosynthetic Accumulation Models
Kaili Yang, Jiacai Mo, Shanjun Luo, et al.
Plant Phenomics (2023) Vol. 5
Open Access | Times Cited: 19
Enhancing Wheat Above-Ground Biomass Estimation Using UAV RGB Images and Machine Learning: Multi-Feature Combinations, Flight Height, and Algorithm Implications
Weiguang Zhai, Changchun Li, Qian Cheng, et al.
Remote Sensing (2023) Vol. 15, Iss. 14, pp. 3653-3653
Open Access | Times Cited: 19
UAV Flight Height Impacts on Wheat Biomass Estimation via Machine and Deep Learning
Wanxue Zhu, Ehsan Eyshi Rezaei, Hamideh Nouri, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2023) Vol. 16, pp. 7471-7485
Open Access | Times Cited: 17
Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation
Jingyu Hu, Hao Feng, Qilei Wang, et al.
Remote Sensing (2024) Vol. 16, Iss. 5, pp. 784-784
Open Access | Times Cited: 7
A survey of unmanned aerial vehicles and deep learning in precision agriculture
Dashuai Wang, Minghu Zhao, Zhuolin Li, et al.
European Journal of Agronomy (2024) Vol. 164, pp. 127477-127477
Closed Access | Times Cited: 7
Multi-modal fusion and multi-task deep learning for monitoring the growth of film-mulched winter wheat
Zhikai Cheng, Xiaobo Gu, Yadan Du, et al.
Precision Agriculture (2024) Vol. 25, Iss. 4, pp. 1933-1957
Closed Access | Times Cited: 6
Crop aboveground biomass monitoring model based on UAV spectral index reconstruction and Bayesian model averaging: A case study of film-mulched wheat and maize
Zhikai Cheng, Xiaobo Gu, Zhihui Zhou, et al.
Computers and Electronics in Agriculture (2024) Vol. 224, pp. 109190-109190
Closed Access | Times Cited: 6
Integrating Satellite and UAV Technologies for Maize Plant Height Estimation Using Advanced Machine Learning
Marcelo Araújo Junqueira Ferraz, Thiago Orlando Costa Barboza, Pablo de Sousa Arantes, et al.
AgriEngineering (2024) Vol. 6, Iss. 1, pp. 20-33
Open Access | Times Cited: 5
Combining high-throughput deep learning phenotyping and GWAS to reveal genetic variants of fruit branch angle in upland cotton
Libei Li, Hui Chang, Shuqi Zhao, et al.
Industrial Crops and Products (2024) Vol. 220, pp. 119180-119180
Closed Access | Times Cited: 5
Estimation of Nitrogen Content in Winter Wheat Based on Multi-Source Data Fusion and Machine Learning
Fan Ding, Changchun Li, Weiguang Zhai, et al.
Agriculture (2022) Vol. 12, Iss. 11, pp. 1752-1752
Open Access | Times Cited: 22
Multi-random ensemble on Partial Least Squares regression to predict wheat yield and its losses across water and nitrogen stress with hyperspectral remote sensing
Bohan Mao, Qian Cheng, Li Chen, et al.
Computers and Electronics in Agriculture (2024) Vol. 222, pp. 109046-109046
Closed Access | Times Cited: 4
Monitoring and Prediction of Wild Blueberry Phenology Using a Multispectral Sensor
Kenneth Anku, David Percival, Mathew R. Vankoughnett, et al.
Remote Sensing (2025) Vol. 17, Iss. 2, pp. 334-334
Open Access
Fusion of UAV-based 3D mesh and spectral features improves quinoa biomass and LAI estimation across genotypic and temporal variations
Sayo Sesay, Tomoaki Yamaguchi, Sayako Kushino, et al.
Smart Agricultural Technology (2025), pp. 100818-100818
Open Access
UAV-based rice aboveground biomass estimation using a random forest model with multi-organ feature selection
Jing Shi, Kaili Yang, Ningge Yuan, et al.
European Journal of Agronomy (2025) Vol. 164, pp. 127529-127529
Open Access
Cover Crop Biomass Predictions with Unmanned Aerial Vehicle Remote Sensing and TensorFlow Machine Learning
Aakriti Poudel, D. R. Burns, Rejina Adhikari, et al.
Drones (2025) Vol. 9, Iss. 2, pp. 131-131
Open Access
A High‐Throughput Phenotyping Pipeline for Quinoa (Chenopodium quinoa) Panicles Using Image Analysis With Convolutional Neural Networks
Flavio Lozano‐Isla, Lydia Kienbaum, Bettina I. G. Haussmann, et al.
Plant Breeding (2025)
Open Access
Comparative evaluation of machine learning models for UAV-derived biomass estimation in Miombo Woodlands
Goodluck S. Melitha, Japhet J. Kashaigili, Wilson Ancelm Mugasha
Earth Science Informatics (2025) Vol. 18, Iss. 3
Closed Access
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