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Nalini Kanta Barpanda
ResearcherPublications, citations & collaboration network
Nalini Kanta Barpanda is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 51 works, 1,133 citations, an h-index of 10 and an i10-index of 10.
51
Works
1,133
Citations
10
h-index
10
i10-index
How has Nalini Kanta Barpanda's publication output changed over time?
ScholarIQpublication output · 2012–2024
Output grew0% over the shown period — from 1 works in 2012 to 1 in 2024.
1
2
1
4
1
1
201220172019202020232024
What are the most-cited papers on Nalini Kanta Barpanda?
ScholarIQmost cited works
Deep feature based rice leaf disease identification using support vector machine
Prabira Kumar Sethy, Nalini Kanta Barpanda, Amiya Kumar Rath, Santi Kumari Behera
Computers and Electronics in Agriculture. 2020587 Citations
Image Processing Techniques for Diagnosing Rice Plant Disease: A Survey
Prabira Kumar Sethy, Nalini Kanta Barpanda, Amiya Kumar Rath, Santi Kumari Behera
Procedia Computer Science. 2020161 CitationsOPEN ACCESS
Nitrogen Deficiency Prediction of Rice Crop Based on Convolutional Neural Network
Prabira Kumar Sethy, Nalini Kanta Barpanda, Amiya Kumar Rath, Santi Kumari Behera
S48031226. 2020126 Citations
Network reliability optimization problem of interconnection network under node-edge failure model
Ranjan Kumar Dash, Nalini Kanta Barpanda, Pradyumna Kumar Tripathy, C. R. Tripathy
S140556538. 201241 Citations
Measurement of Disease Severity of Rice Crop Using Machine Learning and Computational Intelligence
Prabira Kumar Sethy, Baishalee Negi, Nalini Kanta Barpanda, Santi Kumari Behera, Amiya Kumar Rath
S4210238217. 201741 Citations
Related on ScholarIQ
Sambalpur University
Institution
Deep feature based rice leaf disease identification using support vector machine
Paper
Image Processing Techniques for Diagnosing Rice Plant Disease: A Survey
Paper
Nitrogen Deficiency Prediction of Rice Crop Based on Convolutional Neural Network
Paper
Network reliability optimization problem of interconnection network under node-edge failure model
Paper
Measurement of Disease Severity of Rice Crop Using Machine Learning and Computational Intelligence
Paper