Papers
25
Total Citations
725
H-Index
13
About
Ranjan Sapkota is a prolific researcher at the intersection of computer vision, artificial intelligence, and agricultural automation, whose work is rapidly reshaping how modern orchards are managed and harvested. His research focuses on applying state-of-the-art deep learning frameworks — particularly the YOLO family of models and Mask R-CNN — to real-world agricultural challenges including instance segmentation, object detection, robotic pruning, and crop-load estimation in complex orchard environments. His most-cited work, a 2024 comparative study of YOLOv8 and Mask R-CNN for orchard segmentation (172 citations), has become a key reference for researchers developing vision-guided agricultural robots. Beyond agriculture, Sapkota has made notable strides in AI theory, authoring a highly influential conceptual taxonomy distinguishing AI Agents from Agentic AI, which has already accumulated nearly 220 combined citations since 2025. His innovative exploration of LLM-generated synthetic datasets for training detection models reflects a forward-thinking approach to reducing labeling costs. With over 600 cumulative citations across a concentrated body of recent work, Sapkota has established himself as an emerging leader bridging precision agriculture and next-generation AI systems.
Research Focus
Key Achievements
Top Papers
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- 2AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges120 citations · 2025
- 3AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges99 citations · 2025
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- 10Machine Vision-Based Crop-Load Estimation Using YOLOv817 citations · 2023