Karanam Lokesh
Papers
1
Total Citations
11
H-Index
1
About
Karanam Lokesh is a researcher specializing in machine learning applications for agricultural and environmental monitoring. His work focuses on developing advanced algorithms for remote sensing and image analysis, particularly in the classification and assessment of tree crops. His most-cited paper, "Coconut trees classification based on height, inclination, and orientation using MIN-SVM algorithm" (2023), with 11 citations, introduces a novel approach that integrates multiple geometric features—height, inclination, and orientation—with a modified support vector machine (MIN-SVM) to accurately classify coconut trees. This contribution is significant for precision agriculture, enabling automated monitoring of tree health and growth patterns, which can improve yield estimation and resource management. Lokesh’s research demonstrates the potential of combining traditional machine learning techniques with domain-specific feature engineering to solve practical challenges in agriculture. His work has been cited by peers exploring similar applications in crop classification and environmental sensing, highlighting its relevance in the growing field of AI-driven agriculture. Through this study, Lokesh provides a scalable framework that can be adapted for other tree species and geographic regions, underscoring his impact on sustainable farming practices and remote sensing technologies.
Research Focus
Key Achievements
Top Papers
- 1