V. Devendran
Papers
2
Total Citations
11
H-Index
2
About
V. Devendran is a researcher focused on computational approaches to agricultural challenges, particularly the automated detection of plant diseases through image analysis. His primary research areas include feature extraction, ensemble classification, and machine learning applications for plant pathology. Devendran’s major contributions center on developing diagnostic systems that can identify disease-infected plant leaves with high accuracy, leveraging ensemble methods to improve classification reliability. His work addresses the critical need for rapid, non-invasive disease detection in agriculture, where plant leaves serve as key indicators of crop health. Among his notable publications, "Ensemble Classification and Feature Extraction Based Plant Leaf Disease Recognition" (2021) has garnered 8 citations, demonstrating its relevance in the field. A related study, "Plant Leaf Disease Diagnostic System Built on Feature Extraction and Ensemble Classification" (2021), further refines these techniques. While his citation counts are still growing, Devendran’s research contributes to the broader effort of using artificial intelligence to support sustainable agriculture and food security. His work is particularly valuable for students and researchers exploring practical applications of machine learning in real-world environmental monitoring.
Research Focus
Key Achievements
Top Papers
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