Papers
4
Total Citations
69
H-Index
4
About
L. G. Divyanth is a researcher at the forefront of agricultural robotics and deep learning, dedicated to automating high-skill, labor-intensive tasks in specialty crop production. Their work centers on developing advanced computer vision systems for robotic harvesting and sampling, with a particular focus on occlusion handling and depth estimation. Divyanth’s most influential contribution is an attention-guided Faster R-CNN model for detecting coconut clusters under varying occlusion conditions, a critical step toward safe, autonomous harvesting that has garnered 27 citations. They further advanced the field with a two-stage deep-learning model for occlusion-based classification of Kashmiri orchard apples (22 citations), and a method for estimating depth from single RGB images for robotic navigation in apple orchards (12 citations). Most recently, Divyanth has tackled the challenge of high-throughput pathogen sampling in potatoes, developing a deep-learning approach for efficient detection of tuber eyes (8 citations). By bridging the gap between state-of-the-art computer vision and practical agricultural needs, Divyanth’s research is paving the way for a new generation of intelligent, autonomous farm machinery that can reduce human risk and labor dependency.
Research Focus
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