S. Sunoj

759 total citations
20 papers, 583 citations indexed

About

S. Sunoj is a scholar working on Ecology, Plant Science and Agronomy and Crop Science. According to data from OpenAlex, S. Sunoj has authored 20 papers receiving a total of 583 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Ecology, 6 papers in Plant Science and 4 papers in Agronomy and Crop Science. Recurrent topics in S. Sunoj's work include Remote Sensing in Agriculture (6 papers), Spectroscopy and Chemometric Analyses (4 papers) and Remote Sensing and LiDAR Applications (3 papers). S. Sunoj is often cited by papers focused on Remote Sensing in Agriculture (6 papers), Spectroscopy and Chemometric Analyses (4 papers) and Remote Sensing and LiDAR Applications (3 papers). S. Sunoj collaborates with scholars based in United States, India and Canada. S. Sunoj's co-authors include C. Igathinathane, R. Pandiselvam, M. R. Manikantan, Anjineyulu Kothakota, K. B. Hebbar, Jun Xue, Ganesh C. Bora, R. Visvanathan, David W. Archer and Quirine M. Ketterings and has published in prestigious journals such as SHILAP Revista de lepidopterología, Renewable Energy and Remote Sensing.

In The Last Decade

S. Sunoj

20 papers receiving 567 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
S. Sunoj United States 13 218 141 108 81 74 20 583
Aline Schneider Teixeira Argentina 15 231 1.1× 142 1.0× 56 0.5× 75 0.9× 59 0.8× 37 697
Tiziana Amoriello Italy 16 181 0.8× 149 1.1× 99 0.9× 74 0.9× 41 0.6× 37 531
Grzegorz Zaguła Poland 16 229 1.1× 202 1.4× 86 0.8× 153 1.9× 27 0.4× 91 965
Richard Agneessens Belgium 18 238 1.1× 117 0.8× 175 1.6× 258 3.2× 93 1.3× 33 897
Agnieszka Piotrowska‐Cyplik Poland 17 106 0.5× 78 0.6× 67 0.6× 123 1.5× 165 2.2× 58 977
G.S.V. Raghavan Canada 18 264 1.2× 294 2.1× 262 2.4× 131 1.6× 35 0.5× 47 978
Annette Naumann Germany 13 289 1.3× 108 0.8× 105 1.0× 83 1.0× 19 0.3× 19 663
Animesh Sarkar Bangladesh 16 270 1.2× 209 1.5× 52 0.5× 52 0.6× 35 0.5× 58 677
Alexandre Soares dos Santos Brazil 17 147 0.7× 99 0.7× 47 0.4× 251 3.1× 34 0.5× 78 816
Lene Pedersen Denmark 10 102 0.5× 152 1.1× 41 0.4× 138 1.7× 26 0.4× 18 495

Countries citing papers authored by S. Sunoj

Since Specialization
Citations

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

Fields of papers citing papers by S. Sunoj

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of S. Sunoj

This figure shows the co-authorship network connecting the top 25 collaborators of S. Sunoj. A scholar is included among the top collaborators of S. Sunoj 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 S. Sunoj. S. Sunoj 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.
Sunoj, S., et al.. (2024). Corn grain and silage yield class prediction for zone delineation using high-resolution satellite imagery. Agricultural Systems. 218. 104009–104009. 2 indexed citations
2.
Sunoj, S., et al.. (2023). Maize grain and silage yield prediction of commercial fields using high-resolution UAS imagery. Biosystems Engineering. 235. 137–149. 7 indexed citations
3.
Sunoj, S., et al.. (2021). Digital image analysis estimates of biomass, carbon, and nitrogen uptake of winter cereal cover crops. Computers and Electronics in Agriculture. 184. 106093–106093. 14 indexed citations
4.
Sunoj, S., et al.. (2021). Nitrogen and Phosphorus Balances Vary at the Whole-Farm, Field, and Within-Field Scales. SHILAP Revista de lepidopterología. 2. 5 indexed citations
5.
Guinness, Joseph, et al.. (2021). Spatial estimation methods for mapping corn silage and grain yield monitor data. Precision Agriculture. 22(5). 1501–1520. 14 indexed citations
6.
Sunoj, S., Ademola Hammed, C. Igathinathane, Sulaymon Eshkabilov, & Halis Şimşek. (2021). Identification, quantification, and growth profiling of eight different microalgae species using image analysis. Algal Research. 60. 102487–102487. 15 indexed citations
7.
Sunoj, S., et al.. (2021). Corn Grain Yield Prediction and Mapping from Unmanned Aerial System (UAS) Multispectral Imagery. Remote Sensing. 13(19). 3948–3948. 23 indexed citations
8.
Leite, José Marcos, Pavithra S. Pitumpe Arachchige, Ignacio A. Ciampitti, et al.. (2020). Co-addition of humic substances and humic acids with urea enhances foliar nitrogen use efficiency in sugarcane (Saccharum officinarum L.). Heliyon. 6(10). e05100–e05100. 25 indexed citations
9.
Sunoj, S., et al.. (2020). Kinetic studies of alkaline-pretreated corn stover co-digested with upset dairy manure under solid-state. Renewable Energy. 163. 2198–2207. 8 indexed citations
10.
Rahman, Shafiqur, et al.. (2020). Impact of corn stover particle size and C/N ratio on reactor performance in solid-state anaerobic co-digestion with dairy manure. Journal of the Air & Waste Management Association. 70(4). 436–454. 42 indexed citations
11.
Sunoj, S., et al.. (2020). Impact of headland area on whole field and farm corn silage and grain yield. Agronomy Journal. 113(1). 147–158. 8 indexed citations
12.
Sunoj, S., Sukeerthi Dharani, C. Igathinathane, et al.. (2018). Sunflower floral dimension measurements using digital image processing. Computers and Electronics in Agriculture. 151. 403–415. 20 indexed citations
13.
Pandiselvam, R., et al.. (2018). Modeling of coconut milk residue incorporated rice‐corn extrudates properties using multiple linear regression and artificial neural network. Journal of Food Process Engineering. 42(2). 41 indexed citations
14.
Sunoj, S., C. Igathinathane, Nicanor Z. Saliendra, John Hendrickson, & David W. Archer. (2018). Color calibration of digital images for agriculture and other applications. ISPRS Journal of Photogrammetry and Remote Sensing. 146. 221–234. 55 indexed citations
15.
Pandiselvam, R., et al.. (2017). Engineering Properties of Jackfruit (Artocarpus heterophyllus L.). Agricultural Engineering Today. 41(1). 56–60. 1 indexed citations
16.
Sunoj, S., et al.. (2017). Quantification of browning in apples using colour and textural features by image analysis. Food Quality and Safety. 1(3). 221–226. 82 indexed citations
17.
Sunoj, S., et al.. (2017). Cashews whole and splits classification using a novel machine vision approach. Postharvest Biology and Technology. 138. 19–30. 18 indexed citations
18.
Pandiselvam, R., S. Sunoj, M. R. Manikantan, Anjineyulu Kothakota, & K. B. Hebbar. (2016). Application and Kinetics of Ozone in Food Preservation. Ozone Science and Engineering. 39(2). 115–126. 138 indexed citations
19.
Sunoj, S., C. Igathinathane, & R. Visvanathan. (2016). Nondestructive determination of cocoa bean quality using FT-NIR spectroscopy. Computers and Electronics in Agriculture. 124. 234–242. 61 indexed citations
20.
Pandiselvam, R., et al.. (2016). DEVELOPMENT OF MULTIVARIATE REGRESSION MODEL FOR QUANTIFICATION OF PROXIMATE CONTENT IN VIGNA RADIATA USING FOURIER TRANSFORM –NIR SPECTROSCOPY. CaSA NaRA DSpace (CaSA NaRA). 4 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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