Anna Chlingaryan

1.8k total citations · 1 hit paper
22 papers, 1.2k citations indexed

About

Anna Chlingaryan is a scholar working on Media Technology, Artificial Intelligence and Ecology. According to data from OpenAlex, Anna Chlingaryan has authored 22 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Media Technology, 8 papers in Artificial Intelligence and 6 papers in Ecology. Recurrent topics in Anna Chlingaryan's work include Remote-Sensing Image Classification (12 papers), Geochemistry and Geologic Mapping (8 papers) and Effects of Environmental Stressors on Livestock (6 papers). Anna Chlingaryan is often cited by papers focused on Remote-Sensing Image Classification (12 papers), Geochemistry and Geologic Mapping (8 papers) and Effects of Environmental Stressors on Livestock (6 papers). Anna Chlingaryan collaborates with scholars based in Australia, Bangladesh and Switzerland. Anna Chlingaryan's co-authors include Salah Sukkarieh, Brett Whelan, Richard J. Murphy, Arman Melkumyan, Lloyd Windrim, Sven Schneider, Cameron Clark, Raymond Leung, Peter C. Thomson and Juan Nieto and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Geoscience and Remote Sensing and IEEE Transactions on Image Processing.

In The Last Decade

Anna Chlingaryan

19 papers receiving 1.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review

2018 940 citations Anna Chlingaryan, Salah Sukkarieh et al. Computers and Electronics in Agriculture profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Anna Chlingaryan Australia	10	682	481	209	176	176	22	1.2k
Yuchi Ma United States	15	454 0.7×	528 1.1×	154 0.7×	96 0.5×	216 1.2×	26	979
Reddy Pullanagari New Zealand	19	304 0.4×	474 1.0×	330 1.6×	126 0.7×	238 1.4×	44	1.0k
Zhou Zhang United States	23	788 1.2×	926 1.9×	275 1.3×	366 2.1×	381 2.2×	77	1.9k
Ana Paula Marques Ramos Brazil	21	796 1.2×	872 1.8×	300 1.4×	129 0.7×	489 2.8×	75	1.7k
R. M. Patel Canada	17	413 0.6×	449 0.9×	236 1.1×	152 0.9×	368 2.1×	37	1.3k
Lucas Prado Osco Brazil	22	856 1.3×	957 2.0×	308 1.5×	158 0.9×	563 3.2×	66	1.8k
Ian J. Yule New Zealand	23	818 1.2×	674 1.4×	283 1.4×	79 0.4×	613 3.5×	87	2.1k
Ittai Herrmann Israel	20	907 1.3×	948 2.0×	452 2.2×	95 0.5×	285 1.6×	48	1.5k
Xiaohe Gu China	18	455 0.7×	617 1.3×	203 1.0×	68 0.4×	318 1.8×	117	1.0k
João Camargo Neto Brazil	9	871 1.3×	760 1.6×	259 1.2×	68 0.4×	393 2.2×	14	1.3k

Countries citing papers authored by Anna Chlingaryan

Since Specialization

Citations

This map shows the geographic impact of Anna Chlingaryan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Anna Chlingaryan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anna Chlingaryan more than expected).

Fields of papers citing papers by Anna Chlingaryan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anna Chlingaryan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Anna Chlingaryan. The network helps show where Anna Chlingaryan may publish in the future.

Co-authorship network of co-authors of Anna Chlingaryan

This figure shows the co-authorship network connecting the top 25 collaborators of Anna Chlingaryan. A scholar is included among the top collaborators of Anna Chlingaryan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Anna Chlingaryan. Anna Chlingaryan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Chlingaryan, Anna, et al.. (2025). Data Augmentation and Interpolation Improves Machine Learning-Based Pasture Biomass Estimation from Sentinel-2 Imagery. Remote Sensing. 17(23). 3787–3787.

Chlingaryan, Anna, Peter C. Thomson, S.C. García, & Cameron Clark. (2025). An AI-based hybrid model for dairy cattle heat tolerance phenotype. Smart Agricultural Technology. 12. 101455–101455.

Thomson, Peter C., et al.. (2025). The diversity in dairy cattle reticulorumen temperature: Identifying water intake events. Computers and Electronics in Agriculture. 235. 110357–110357.

Thomson, Peter C., et al.. (2024). Monitoring cattle liveweight using a mobile, in-paddock weigh platform: Validation, attendance and utility. SHILAP Revista de lepidopterología. 9. 100639–100639. 1 indexed citations

Thomson, Peter C., et al.. (2024). Review: Ruminant heat-stress terminology. animal. 18(9). 101267–101267. 1 indexed citations

Lomax, Sabrina, et al.. (2024). The impact of rainfall on beef cattle growth across diverse climate zones. animal. 19. 101336–101336. 2 indexed citations

Chlingaryan, Anna, Raymond Leung, & Arman Melkumyan. (2023). Augmenting Stationary Covariance Functions with a Smoothness Hyperparameter and Improving Gaussian Process Regression Using a Structural Similarity Index. Mathematical Geosciences. 56(3). 605–637. 2 indexed citations

Chlingaryan, Anna, et al.. (2023). A deep learning model to forecast cattle heat stress. Computers and Electronics in Agriculture. 211. 107932–107932. 13 indexed citations

Windrim, Lloyd, Arman Melkumyan, Richard J. Murphy, Anna Chlingaryan, & Raymond Leung. (2023). Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data. Geoscience Frontiers. 14(4). 101562–101562. 11 indexed citations

10.

Chlingaryan, Anna, et al.. (2022). Creating large scale probabilistic boundaries using Gaussian Processes. Expert Systems with Applications. 199. 116959–116959. 3 indexed citations

11.

Windrim, Lloyd, et al.. (2018). Pretraining for Hyperspectral Convolutional Neural Network Classification. IEEE Transactions on Geoscience and Remote Sensing. 56(5). 2798–2810. 52 indexed citations

12.

Chlingaryan, Anna, Salah Sukkarieh, & Brett Whelan. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and Electronics in Agriculture. 151. 61–69. 940 indexed citations breakdown →

13.

Murphy, Richard J., Brett Whelan, Anna Chlingaryan, & Salah Sukkarieh. (2018). Quantifying leaf-scale variations in water absorption in lettuce from hyperspectral imagery: a laboratory study with implications for measuring leaf water content in the context of precision agriculture. Precision Agriculture. 20(4). 767–787. 31 indexed citations

14.

Murphy, Richard J., et al.. (2016). Incorporating Spatial Information and Endmember Variability Into Unmixing Analyses to Improve Abundance Estimates. IEEE Transactions on Image Processing. 25(12). 5563–5575. 13 indexed citations

15.

Windrim, Lloyd, Arman Melkumyan, Richard J. Murphy, Anna Chlingaryan, & Juan Nieto. (2016). Unsupervised feature learning for illumination robustness. 4453–4457. 8 indexed citations

16.

Chlingaryan, Anna, Arman Melkumyan, Richard J. Murphy, & Sven Schneider. (2015). Automated Multi-class Classification of Remotely Sensed Hyperspectral Imagery Via Gaussian Processes with a Non-stationary Covariance Function. Mathematical Geosciences. 48(5). 537–558. 7 indexed citations

17.

Murphy, Richard J., et al.. (2015). A Novel Spectral Unmixing Method Incorporating Spectral Variability Within Endmember Classes. IEEE Transactions on Geoscience and Remote Sensing. 54(5). 2812–2831. 27 indexed citations

18.

Murphy, Richard J., et al.. (2015). Spectral curve-based endmember extraction method. 11. 1–4. 1 indexed citations

19.

Murphy, Richard J., Anna Chlingaryan, & Arman Melkumyan. (2014). Gaussian Processes for Estimating Wavelength Position of the Ferric Iron Crystal Field Feature at $\sim$900 nm From Hyperspectral Imagery Acquired in the Short-Wave Infrared (1002–1355 nm). IEEE Transactions on Geoscience and Remote Sensing. 53(4). 1907–1920. 9 indexed citations

20.

Murphy, Richard J., et al.. (2014). Multiple endmember spectral unmixing within a multi-task framework. 19. 3454–3457. 6 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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