Dharmendra Saraswat

1.8k total citations · 1 hit paper
36 papers, 1.3k citations indexed

About

Dharmendra Saraswat is a scholar working on Water Science and Technology, Plant Science and Environmental Chemistry. According to data from OpenAlex, Dharmendra Saraswat has authored 36 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Water Science and Technology, 14 papers in Plant Science and 8 papers in Environmental Chemistry. Recurrent topics in Dharmendra Saraswat's work include Smart Agriculture and AI (14 papers), Hydrology and Watershed Management Studies (12 papers) and Plant Disease Management Techniques (8 papers). Dharmendra Saraswat is often cited by papers focused on Smart Agriculture and AI (14 papers), Hydrology and Watershed Management Studies (12 papers) and Plant Disease Management Techniques (8 papers). Dharmendra Saraswat collaborates with scholars based in United States, India and Singapore. Dharmendra Saraswat's co-authors include Aanis Ahmad, Aly El Gamal, Naresh Pai, Varun Aggarwal, Rebecca W. Zeckoski, Kyle R. Douglas‐Mankin, Prasad Daggupati, Prem B. Parajuli, Jaehak Jeong and Srinivasulu Ale and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Remote Sensing.

In The Last Decade

Dharmendra Saraswat

32 papers receiving 1.2k citations

Hit Papers

A survey on using deep learning techniques for plant dise... 2022 2026 2023 2024 2022 50 100 150
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.

  1. A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools
    2022 193 citations Aanis Ahmad, Dharmendra Saraswat et al. SHILAP Revista de lepidopterología profile →

Peers

Dharmendra Saraswat
Amy L. Kaleita United States
Qi Yang China
Ruixiu Sui United States
Anton Thomsen Denmark
R. Troy Peters United States
Travis J. Esau Canada
R. M. Patel Canada
ElKamil Tola Saudi Arabia
Wang Zhou United States
Alim Pulatov Uzbekistan
Amy L. Kaleita United States
Dharmendra Saraswat
Citations per year, relative to Dharmendra Saraswat Dharmendra Saraswat (= 1×) peers Amy L. Kaleita

Countries citing papers authored by Dharmendra Saraswat

Since Specialization
Citations

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

Fields of papers citing papers by Dharmendra Saraswat

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dharmendra Saraswat

This figure shows the co-authorship network connecting the top 25 collaborators of Dharmendra Saraswat. A scholar is included among the top collaborators of Dharmendra Saraswat 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 Dharmendra Saraswat. Dharmendra Saraswat 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.
Sahany, Sandeep, et al.. (2024). Implications of CMIP6 Models‐Based Climate Biases and Runoff Sensitivity on Runoff Projection Uncertainties Over Central India. International Journal of Climatology. 44(16). 5727–5744.
2.
Sahany, Sandeep, et al.. (2024). Assessing Regional-Scale Heterogeneity in Blue–Green Water Availability under the 1.5°C Global Warming Scenario. Journal of Applied Meteorology and Climatology. 63(4). 553–574.
3.
Ahmad, Aanis, Varun Aggarwal, Dharmendra Saraswat, & Gurmukh S. Johal. (2024). UAS and UGV-Based Disease Management  System for Diagnosing Corn Diseases Above  and Below the Canopy Using Deep Learning. Journal of the ASABE. 67(1). 161–179. 1 indexed citations
4.
Ahmad, Aanis, Varun Aggarwal, & Dharmendra Saraswat. (2023). BNNDC: Branched neural network for plant disease identification. SHILAP Revista de lepidopterología. 5. 100315–100315. 5 indexed citations
5.
Zhang, Xiang, et al.. (2022). Flash Drought: Review of Concept, Prediction and the Potential for Machine Learning, Deep Learning Methods. Earth s Future. 10(11). 52 indexed citations
6.
Ahmad, Aanis, Dharmendra Saraswat, & Aly El Gamal. (2022). A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools. SHILAP Revista de lepidopterología. 3. 100083–100083. 193 indexed citations breakdown →
7.
Ahmad, Aanis, Varun Aggarwal, Dharmendra Saraswat, Aly El Gamal, & Gurmukh S. Johal. (2022). GeoDLS: A Deep Learning-Based Corn Disease Tracking and Location System Using RTK Geolocated UAS Imagery. Remote Sensing. 14(17). 4140–4140. 8 indexed citations
8.
9.
Ahmad, Aanis, et al.. (2022). A two-stage deep-learning based segmentation model for crop disease quantification based on corn field imagery. SHILAP Revista de lepidopterología. 3. 100108–100108. 78 indexed citations
10.
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12.
Saraswat, Dharmendra, et al.. (2020). Comparative Analysis of Bioenergy Crop Impacts on Water Quality Using Static and Dynamic Land Use Change Modeling Approach. Water. 12(2). 410–410. 7 indexed citations
13.
Saraswat, Dharmendra, et al.. (2018). A Sensitivity Analysis of Impacts of Conservation Practices on Water Quality in L’Anguille River Watershed, Arkansas. Water. 10(4). 443–443. 10 indexed citations
14.
Saraswat, Dharmendra, et al.. (2017). Evaluating remotely sensed plant count accuracy with differing unmanned aircraft system altitudes, physical canopy separations, and ground covers. Journal of Applied Remote Sensing. 11(3). 36003–36003. 9 indexed citations
15.
Saraswat, Dharmendra, et al.. (2016). Development and evaluation of targeted marginal land mapping approach in SWAT model for simulating water quality impacts of selected second generation biofeedstock. Environmental Modelling & Software. 81. 26–39. 20 indexed citations
16.
Daggupati, Prasad, Naresh Pai, Srinivasulu Ale, et al.. (2015). A Recommended Calibration and Validation Strategy for Hydrologic and Water Quality Models. Transactions of the ASABE. 58(6). 1705–1719. 180 indexed citations
17.
Banerjee, Nilanjan, et al.. (2015). Smartphone-based hierarchical crowdsourcing for weed identification. Computers and Electronics in Agriculture. 113. 14–23. 28 indexed citations
18.
Pai, Naresh & Dharmendra Saraswat. (2011). SWAT2009_LUC: A Tool to Activate the Land Use Change Module in SWAT 2009. Transactions of the ASABE. 54(5). 1649–1658. 59 indexed citations
19.
Engel, Bernard A., et al.. (2010). Biofuels and water quality: challenges and opportunities for simulation modeling. Biofuels. 1(3). 463–477. 24 indexed citations
20.
Allred, Barry J., Mohammad Reza Ehsani, & Dharmendra Saraswat. (2005). THE IMPACT OF TEMPERATURE AND SHALLOW HYDROLOGIC CONDITIONS ON THE MAGNITUDE AND SPATIAL PATTERN CONSISTENCY OF ELECTROMAGNETIC INDUCTION MEASURED SOIL ELECTRICAL CONDUCTIVITY. Transactions of the ASAE. 48(6). 2123–2135. 21 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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