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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 pasture aboveground biomass under an integrated crop-livestock system based on spectral and texture measures derived from UAV images
Rodrigo Greggio de Freitas, Francisco R. da S. Pereira, Aliny Aparecida dos Reis, et al.
Computers and Electronics in Agriculture (2022) Vol. 198, pp. 107122-107122
Closed Access | Times Cited: 35

Showing 1-25 of 35 citing articles:

Estimating potato above-ground biomass based on vegetation indices and texture features constructed from sensitive bands of UAV hyperspectral imagery
Yang Liu, Yiguang Fan, Haikuan Feng, et al.
Computers and Electronics in Agriculture (2024) Vol. 220, pp. 108918-108918
Closed Access | Times Cited: 55
A model suitable for estimating above-ground biomass of potatoes at different regional levels
Yang Liu, Yiguang Fan, Jibo Yue, et al.
Computers and Electronics in Agriculture (2024) Vol. 222, pp. 109081-109081
Closed Access | Times Cited: 34
Utilizing UAV-based hyperspectral remote sensing combined with various agronomic traits to monitor potato growth and estimate yield
Yang Liu, Haikuan Feng, Yiguang Fan, et al.
Computers and Electronics in Agriculture (2025) Vol. 231, pp. 109984-109984
Closed Access | Times Cited: 10
Pasture monitoring using remote sensing and machine learning: A review of methods and applications
Tej Bahadur Shahi, Thirunavukarasu Balasubramaniam, Kenneth Sabir, et al.
Remote Sensing Applications Society and Environment (2025), pp. 101459-101459
Open Access | Times Cited: 2
Crop type mapping in the central part of the North China Plain using Sentinel-2 time series and machine learning
Ke Luo, Linlin Lu, Yanhua Xie, et al.
Computers and Electronics in Agriculture (2022) Vol. 205, pp. 107577-107577
Closed Access | Times Cited: 41
Estimation of winter canola growth parameter from UAV multi-angular spectral-texture information using stacking-based ensemble learning model
Ruiqi Du, Junsheng Lu, Youzhen Xiang, et al.
Computers and Electronics in Agriculture (2024) Vol. 222, pp. 109074-109074
Closed Access | Times Cited: 14
Aboveground biomass retrieval of wetland vegetation at the species level using UAV hyperspectral imagery and machine learning
Wei Zhuo, Wu Nan, Runhe Shi, et al.
Ecological Indicators (2024) Vol. 166, pp. 112365-112365
Open Access | Times Cited: 11
Estimating aboveground biomass of grassland in central Asia mountainous areas using unmanned aerial vehicle vegetation indices and image textures – A case study of typical grassland in Tajikistan
Tianli Pan, Huping Ye, Xinyu Zhang, et al.
Environmental and Sustainability Indicators (2024) Vol. 22, pp. 100345-100345
Open Access | Times Cited: 9
Improving Wheat Leaf Nitrogen Concentration (LNC) Estimation across Multiple Growth Stages Using Feature Combination Indices (FCIs) from UAV Multispectral Imagery
Xiangxiang Su, 櫻井 克年, Yue Hu, et al.
Agronomy (2024) Vol. 14, Iss. 5, pp. 1052-1052
Open Access | Times Cited: 9
Advancing soybean biomass estimation through multi-source UAV data fusion and machine learning algorithms
Haitao Da, Yaxin Li, Le Xu, et al.
Smart Agricultural Technology (2025) Vol. 10, pp. 100778-100778
Open Access | Times Cited: 1
Aboveground biomass estimation in forests with random forest and Monte Carlo-based uncertainty analysis
Zizhao Li, Shoudong Bi, Shuang Hao, et al.
Ecological Indicators (2022) Vol. 142, pp. 109246-109246
Open Access | Times Cited: 31
Multi-scale monitoring of rice aboveground biomass by combining spectral and textural information from UAV hyperspectral images
Tianyue Xu, Fumin Wang, Zhou Shi, et al.
International Journal of Applied Earth Observation and Geoinformation (2024) Vol. 127, pp. 103655-103655
Open Access | Times Cited: 7
Biomass Estimation of Milk Vetch Using UAV Hyperspectral Imagery and Machine Learning
Hao Hu, Hongkui Zhou, Kai Cao, et al.
Remote Sensing (2024) Vol. 16, Iss. 12, pp. 2183-2183
Open Access | Times Cited: 5
Framing Concepts of Agriculture 5.0 via Bipartite Analysis
Ivan Bergier, Jayme Garcia Arnal Barbedo, Édson Luís Bolfe, et al.
Sustainability (2024) Vol. 16, Iss. 24, pp. 10851-10851
Open Access | Times Cited: 4
Precision livestock farming applied to grazingland monitoring and management—A review
Igor L. Bretas, José Carlos Batista Dubeux, Priscila J. R. Cruz, et al.
Agronomy Journal (2023) Vol. 116, Iss. 3, pp. 1164-1186
Closed Access | Times Cited: 11
High-precision estimation of grass quality and quantity using UAS-based VNIR and SWIR hyperspectral cameras and machine learning
Raquel Alves de Oliveira, Roope Näsi, Panu Korhonen, et al.
Precision Agriculture (2023) Vol. 25, Iss. 1, pp. 186-220
Open Access | Times Cited: 10
Prediction of pasture yield using machine learning-based optical sensing: a systematic review
Christoph Stumpe, Joerg Leukel, Tobias Zimpel
Precision Agriculture (2023) Vol. 25, Iss. 1, pp. 430-459
Open Access | Times Cited: 10
Response of spectral vegetation indices to Erannis jacobsoni Djak. damage in larch forests
Siyuan Zhang, Xiaojun Huang, Lei Ma, et al.
Ecological Informatics (2024) Vol. 81, pp. 102605-102605
Open Access | Times Cited: 4
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
Integrating Proximal and Remote Sensing with Machine Learning for Pasture Biomass Estimation
Bernardo Moreira Cândido, Ushasree Mindala, Hamid Ebrahimy, et al.
Sensors (2025) Vol. 25, Iss. 7, pp. 1987-1987
Open Access
Drone RGB Imagery Color and Texture Information Have Varied Importance in Predicting Potato Aboveground Biomass in Different Growth Stages
Hang Yin, Haibo Yang, Yuncai Hu, et al.
Potato Research (2025)
Closed Access
Estimating forage mass in Brazilian pasture-based livestock production systems through satellite and climate data integration
Gustavo Bayma-Silva, S. F. Nogueira, Marcos Adami, et al.
Computers and Electronics in Agriculture (2025) Vol. 237, pp. 110496-110496
Open Access
Advanced Dynamic Monitoring and Precision Analysis of Soil Salinity in Cotton Fields Using CNN‐Attention and UAV Multispectral Imaging Integration
Jiao Tan, Jianli Ding, Jiangtao Li, et al.
Land Degradation and Development (2025)
Closed Access
Predicting on-farm soybean yield variability using texture measures on Sentinel-2 image
Rodrigo Greggio de Freitas, Henrique Oldoni, Lucas Fernando Joaquim, et al.
Precision Agriculture (2024) Vol. 25, Iss. 6, pp. 2977-3000
Closed Access | Times Cited: 3
Diagnosis alfalfa salt stress based on UAV multispectral image texture and vegetation index
Hong Mā, Wenju Zhao, Haiying Yu, et al.
Plant and Soil (2025)
Closed Access
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