Nonoy Bandillo

2.1k total citations · 2 hit papers
39 papers, 1.4k citations indexed

About

Nonoy Bandillo is a scholar working on Plant Science, Genetics and Ecology. According to data from OpenAlex, Nonoy Bandillo has authored 39 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Plant Science, 12 papers in Genetics and 8 papers in Ecology. Recurrent topics in Nonoy Bandillo's work include Genetic Mapping and Diversity in Plants and Animals (11 papers), Genetic and Environmental Crop Studies (8 papers) and Genetics and Plant Breeding (8 papers). Nonoy Bandillo is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (11 papers), Genetic and Environmental Crop Studies (8 papers) and Genetics and Plant Breeding (8 papers). Nonoy Bandillo collaborates with scholars based in United States, United Kingdom and Netherlands. Nonoy Bandillo's co-authors include Jiajia Rao, Bingcan Chen, Jae‐Bom Ohm, Aaron J. Lorenz, Yechun Wang, Leqi Cui, Liuyi Chang, Lan Yang, Diego Jarquín and Edward S. Buckler and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Plant Cell and PLANT PHYSIOLOGY.

In The Last Decade

Nonoy Bandillo

36 papers receiving 1.4k citations

Hit Papers

Functionality and structure of yellow pea protein isolate... 2020 2026 2022 2024 2020 2021 50 100 150 200

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nonoy Bandillo United States 16 957 402 388 211 163 39 1.4k
Jian Song China 23 1.1k 1.1× 135 0.3× 176 0.5× 647 3.1× 92 0.6× 55 1.4k
Jichun Tian China 23 1.3k 1.3× 667 1.7× 156 0.4× 120 0.6× 271 1.7× 96 1.5k
Hamid Khazaeı Canada 22 1.3k 1.4× 81 0.2× 198 0.5× 165 0.8× 86 0.5× 66 1.5k
Mohsen Mohammadi United States 20 1.0k 1.1× 380 0.9× 73 0.2× 231 1.1× 33 0.2× 96 1.2k
J. Eglinton Australia 24 1.1k 1.1× 309 0.8× 384 1.0× 178 0.8× 479 2.9× 80 1.6k
Nepolean Thirunavukkarasu India 28 2.1k 2.2× 756 1.9× 111 0.3× 471 2.2× 132 0.8× 73 2.4k
Gongshe Liu China 25 1.3k 1.4× 131 0.3× 80 0.2× 803 3.8× 50 0.3× 70 1.6k
L. Woodrow Canada 17 551 0.6× 41 0.1× 372 1.0× 114 0.5× 223 1.4× 42 944
И. Г. Лоскутов Russia 19 943 1.0× 152 0.4× 240 0.6× 257 1.2× 152 0.9× 122 1.2k
Andrea Mazzucato Italy 22 1.3k 1.3× 148 0.4× 176 0.5× 955 4.5× 71 0.4× 69 1.8k

Countries citing papers authored by Nonoy Bandillo

Since Specialization
Citations

This map shows the geographic impact of Nonoy Bandillo'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 Nonoy Bandillo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nonoy Bandillo more than expected).

Fields of papers citing papers by Nonoy Bandillo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nonoy Bandillo. 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 Nonoy Bandillo. The network helps show where Nonoy Bandillo may publish in the future.

Co-authorship network of co-authors of Nonoy Bandillo

This figure shows the co-authorship network connecting the top 25 collaborators of Nonoy Bandillo. A scholar is included among the top collaborators of Nonoy Bandillo 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 Nonoy Bandillo. Nonoy Bandillo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Bandillo, Nonoy, et al.. (2025). Multispectral data and random forest model outperform deep learning in predicting lentil maturity using UAS imagery. Journal of Agriculture and Food Research. 23. 102202–102202. 1 indexed citations
2.
Bandillo, Nonoy, et al.. (2025). Accurate plant height estimation in pulse crops through integration of LiDAR, multispectral information, and machine learning. Remote Sensing Applications Society and Environment. 37. 101517–101517. 1 indexed citations
3.
Coyne, Clarice J., Ping Zheng, Girish M. Ganjyal, et al.. (2024). Association study of crude seed protein and fat concentration in a USDA pea diversity panel. The Plant Genome. 18(1). e20485–e20485. 5 indexed citations
4.
Piche, Lisa, et al.. (2024). Multi‐trait multi‐environment genomic prediction of preliminary yield trial in pulse crop. The Plant Genome. 17(3). e20496–e20496. 2 indexed citations
6.
Piche, Lisa, Kevin McPhee, Clarice J. Coyne, et al.. (2024). Identification of novel candidate genes for Ascochyta blight resistance in chickpea. Scientific Reports. 14(1). 31415–31415.
7.
Bandillo, Nonoy, et al.. (2024). Assessing dry pea stands using deep learning models in ArcGIS Pro. 2 indexed citations
8.
Bandillo, Nonoy, et al.. (2024). Accelerating genetic gain through strategic speed breeding in spring wheat. Crop Science. 64(6). 3311–3322. 1 indexed citations
9.
Kim, Jeong Hwa, et al.. (2023). Predicting Dry Pea Maturity Using Machine Learning and Advanced Sensor Fusion with Unmanned Aerial Systems (UASs). Remote Sensing. 15(11). 2758–2758. 9 indexed citations
10.
Chang, Liuyi, Zixuan Gu, Nonoy Bandillo, Bingcan Chen, & Jiajia Rao. (2023). Fractionation, Structural Characteristics, Functionality, Aromatic Profile, and In Vitro Digestibility of Lentil (Lens culinaris) Proteins. ACS Food Science & Technology. 3(3). 478–488. 25 indexed citations
11.
Simons, Kristin, Phillip N. Miklas, Stephan Schröder, et al.. (2022). New genomic regions associated with white mold resistance in dry bean using a MAGIC population. The Plant Genome. 15(1). e20190–e20190. 6 indexed citations
12.
Lozano, Roberto, Élodie Gazave, J. Santos, et al.. (2021). Comparative evolutionary genetics of deleterious load in sorghum and maize. Nature Plants. 7(1). 17–24. 55 indexed citations
13.
Pignon, Charles P., Samuel B. Fernandes, Ravi Valluru, et al.. (2021). Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes. PLANT PHYSIOLOGY. 187(4). 2544–2562. 35 indexed citations
14.
Chang, Liuyi, Lan Yang, Nonoy Bandillo, et al.. (2021). Plant proteins from green pea and chickpea: Extraction, fractionation, structural characterization and functional properties. Food Hydrocolloids. 123. 107165–107165. 172 indexed citations breakdown →
15.
Bari, Md Abdullah Al, Ping Zheng, Yu Ma, et al.. (2021). Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction. Frontiers in Genetics. 12. 707754–707754. 20 indexed citations
16.
Kremling, Karl A., Christine Diepenbrock, Michael A. Gore, Edward S. Buckler, & Nonoy Bandillo. (2019). Transcriptome-Wide Association Supplements Genome-Wide Association in Zea mays. G3 Genes Genomes Genetics. 9(9). 3023–3033. 52 indexed citations
17.
Zhou, Shaoqun, Karl A. Kremling, Nonoy Bandillo, et al.. (2019). Metabolome-Scale Genome-Wide Association Studies Reveal Chemical Diversity and Genetic Control of Maize Specialized Metabolites. The Plant Cell. 31(5). 937–955. 68 indexed citations
18.
Campbell, Malachy T., Nonoy Bandillo, Sandeep Sharma, et al.. (2017). Allelic variants of OsHKT1;1 underlie the divergence between indica and japonica subspecies of rice (Oryza sativa) for root sodium content. PLoS Genetics. 13(6). e1006823–e1006823. 89 indexed citations
19.
Bandillo, Nonoy, Justin Anderson, Michael B. Kantar, et al.. (2017). Dissecting the Genetic Basis of Local Adaptation in Soybean. Scientific Reports. 7(1). 17195–17195. 42 indexed citations
20.
Bandillo, Nonoy, Chitra Raghavan, Christine Jade Dilla-Ermita, et al.. (2013). Multi-parent advanced generation inter-cross (MAGIC) populations in rice: progress and potential for genetics research and breeding. Rice. 6(1). 11–11. 265 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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