Konga Upendar
Impact in
- Analytical Chemistry top 10%
- Spectroscopy and Chemometric Analyses
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- Smart Agriculture and AI
- Leaf Properties and Growth Measurement
- Date Palm Research Studies
Papers in
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- Smart Agriculture and AI 8
- Leaf Properties and Growth Measurement 3
- Postharvest Quality and Shelf Life Management 1
- Plant Micronutrient Interactions and Effects 1
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- Spectroscopy and Chemometric Analyses 8
- Co-authors
- Narendra Singh Chandel (3 shared papers)Subir Kumar Chakraborty (4 shared papers)C. Nickhil (6 shared papers)A. Subeesh (2 shared papers)Krishna Pratap Singh (1 shared paper)Ali Salem (1 shared paper)Ahmed Elbeltagi (1 shared paper)B. M. Nandede (1 shared paper)
In The Last Decade
Konga Upendar
13 papers receiving 205 citations
Konga Upendar's Hit Papers
Peers
Comparison fields: 5 of 63
- Analytical Chemistry 39
- Plant Science 125
- Ecology 28
- Food Science 19
- Soil Science 6
Countries citing papers authored by Konga Upendar
This map shows the geographic impact of Konga Upendar'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 Konga Upendar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Konga Upendar more than expected).
Fields of papers citing papers by Konga Upendar
This network shows the impact of papers produced by Konga Upendar. 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 Konga Upendar. The network helps show where Konga Upendar may publish in the future.
Co-authors
The 19 scholars most cited alongside Konga Upendar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture Hit paper breakdown → | 2025 | 90 |
| 2 | 2024 | 25 | |
| 3 | 2024 | 19 | |
| 4 | Nutritional and anti-nutritional factors present in oil seeds: An Overview | 2019 | 13 |
| 5 | 2021 | 13 | |
| 6 | 2024 | 12 | |
| 7 | 2020 | 9 | |
| 8 | 2022 | 6 | |
| 9 | 2024 | 5 | |
| 10 | 2020 | 5 | |
| 11 | 2018 | 5 | |
| 12 | 2024 | 3 | |
| 13 | 2025 | 1 | |
| 14 | 2025 | 0 | |
| 15 | 2024 | 0 |
About Konga Upendar
Konga Upendar is a scholar working on Plant Science, Analytical Chemistry, Biomedical Engineering, Ecology and Industrial and Manufacturing Engineering, having authored 15 papers that have together received 206 indexed citations. Recurring topics across this work include Spectroscopy and Chemometric Analyses (8 papers), Smart Agriculture and AI (8 papers), Leaf Properties and Growth Measurement (3 papers), Advanced Chemical Sensor Technologies (3 papers), Remote Sensing in Agriculture (2 papers), Phytochemicals and Antioxidant Activities (1 paper), Postharvest Quality and Shelf Life Management (1 paper) and Plant Micronutrient Interactions and Effects (1 paper). The work is most often cited by research in Analytical Chemistry (39 citations), Plant Science (125 citations), Ecology (28 citations), Food Science (19 citations) and Soil Science (6 citations). Konga Upendar has collaborated with scholars based in India, Hungary and Egypt. Frequent co-authors include Narendra Singh Chandel, Subir Kumar Chakraborty, C. Nickhil, A. Subeesh, Krishna Pratap Singh, Ali Salem, Ahmed Elbeltagi, B. M. Nandede, Sankar Chandra Deka and R. N. Singh. Their work appears in journals such as Food Engineering Reviews, Measurement, Artificial Intelligence Review, Journal of Food Measurement & Characterization and ACS Agricultural Science & Technology.
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.