274 citations · 974 across the 43 of their papers we have counts for
28 papers · 1 filter
Fusing Perceptual Vision Experts with Multimodal LLMs for Explainable Plant Disease Diagnosis: From Benchmark Imagery to Real-World Robotic Field Validation
Ranjan Sapkota, Konstantinos I. Roumeliotis, Pengyao Xie +3
Accurate field plant disease diagnosis requires reliable fusion of uncertain and conflicting perceptual evidence. We present the Hybrid Hierarchical Multi-Agent Framework (HM…
A Lightweight Multimodal Vision-Language Framework for Early-Stage Anatomical Green Fruit Classification in Commercial Orchards
Ranjan Sapkota, William Bu, Chen Chen +2
Accurate identification of early-stage apple fruitlet anatomical structures, including the calyx, fruitlet body, and peduncle, is essential for robotic thinning, crop-load manageme…
Optimizing YOLO27, YOLO26, YOLO11, and YOLOv8 for Fine-Grained Small-Object Detection and Segmentation in Complex Orchard Environments
Ranjan Sapkota, Manoj Karkee
This study presents an architectural and experimental cross-generation analysis of Ultralytics YOLO27 (YOLOv27), YOLO26 (YOLOv26), YOLO11 (YOLOv11), and YOLOv8 for fine-grained rob…
YOLOE-26: Integrating YOLO26 with YOLOE for Real-Time Open-Vocabulary Instance Segmentation
Ranjan Sapkota, Manoj Karkee
This paper presents YOLOE-26, a unified framework that integrates the deployment-optimized YOLO26(or YOLOv26) architecture with the open-vocabulary learning paradigm of YOLOE for r…
Generalization vs. Specialization: Evaluating Segment Anything Model (SAM3) Zero-Shot Segmentation Against Fine-Tuned YOLO Detectors
Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee +1
Deep learning has advanced two fundamentally different paradigms for instance segmentation: specialized models optimized through task-specific fine-tuning and generalist foundation…
The SAM2-to-SAM3 Gap in the Segment Anything Model Family: Why Prompt-Based Expertise Fails in Concept-Driven Image Segmentation
Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee
This paper investigates the fundamental discontinuity between the latest two Segment Anything Models: SAM2 and SAM3. We explain why the expertise in prompt-based segmentation of SA…
Ultralytics YOLO Evolution: An Overview of YOLO27, YOLO26, YOLO11, YOLOv8, and YOLOv5 Object Detectors for Computer Vision and Pattern Recognition
Ranjan Sapkota, Manoj Karkee
This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 thro…
Object Detection with Multimodal Large Vision-Language Models: An In-depth Review
Ranjan Sapkota, Manoj Karkee
The fusion of language and vision in large vision-language models (LVLMs) has revolutionized deep learning-based object detection by enhancing adaptability, contextual reasoning, a…
YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detection
Ranjan Sapkota, Rahul Harsha Cheppally, Ajay Sharda +1
This study presents a comprehensive analysis of Ultralytics YOLO26(also called as YOLOv26), highlighting its key architectural enhancements and performance benchmarking for real-ti…
3D Reconstruction and Information Fusion between Dormant and Canopy Seasons in Commercial Orchards Using Deep Learning and Fast GICP
Ranjan Sapkota, Zhichao Meng, Martin Churuvija +3
In orchard automation, dense foliage during the canopy season severely occludes tree structures, minimizing visibility to various canopy parts such as trunks and branches, which li…
Vision-Language-Action (VLA) Models: Concepts, Progress, Applications and Challenges
Ranjan Sapkota, Yang Cao, Konstantinos I. Roumeliotis +1
Vision-Language-Action (VLA) models mark a transformative advancement in artificial intelligence, aiming to unify perception, natural language understanding, and embodied action wi…
Plant Disease Detection through Multimodal Large Language Models and Convolutional Neural Networks
Konstantinos I. Roumeliotis, Ranjan Sapkota, Manoj Karkee +2
Automation in agriculture plays a vital role in addressing challenges related to crop monitoring and disease management, particularly through early detection systems. This study in…
A Review of 3D Object Detection with Vision-Language Models
Ranjan Sapkota, Konstantinos I Roumeliotis, Rahul Harsha Cheppally +2
This review provides a systematic analysis of comprehensive survey of 3D object detection with vision-language models(VLMs) , a rapidly advancing area at the intersection of 3D vis…
RF-DETR Object Detection vs YOLOv12 : A Study of Transformer-based and CNN-based Architectures for Single-Class and Multi-Class Greenfruit Detection in Complex Orchard Environments Under Label Ambiguity
Ranjan Sapkota, Rahul Harsha Cheppally, Ajay Sharda +1
This study conducts a detailed comparison of RF-DETR object detection base model and YOLOv12 object detection model configurations for detecting greenfruits in a complex orchard en…
Improved YOLOv12 with LLM-Generated Synthetic Data for Enhanced Apple Detection and Benchmarking Against YOLOv11 and YOLOv10
Ranjan Sapkota, Manoj Karkee
This study evaluated the performance of the YOLOv12 object detection model, and compared against the performances YOLOv11 and YOLOv10 for apple detection in commercial orchards bas…
Multimodal Large Language Models for Image, Text, and Speech Data Augmentation: A Survey
Ranjan Sapkota, Shaina Raza, Maged Shoman +2
In the past five years, research has shifted from traditional Machine Learning (ML) and Deep Learning (DL) approaches to leveraging Large Language Models (LLMs) , including multimo…
Integrating YOLO11 and Convolution Block Attention Module for Multi-Season Segmentation of Tree Trunks and Branches in Commercial Apple Orchards
Ranjan Sapkota, Manoj Karkee
In this study, we developed a customized instance segmentation model by integrating the Convolutional Block Attention Module (CBAM) with the YOLO11 architecture. This model, traine…
Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development
Ranjan Sapkota, Achyut Paudel, Manoj Karkee
Currently, deep learning-based instance segmentation for various applications (e.g., Agriculture) is predominantly performed using a labor-intensive process involving extensive fie…
Comparing YOLOv11 and YOLOv8 for instance segmentation of occluded and non-occluded immature green fruits in complex orchard environment
Ranjan Sapkota, Manoj Karkee
This study conducted a comprehensive performance evaluation on YOLO11 (or YOLOv11) and YOLOv8, the latest in the "You Only Look Once" (YOLO) series, focusing on their instance segm…
YOLO11 and Vision Transformers based 3D Pose Estimation of Immature Green Fruits in Commercial Apple Orchards for Robotic Thinning
Ranjan Sapkota, Manoj Karkee
In this study, a robust method for 3D pose estimation of immature green apples (fruitlets) in commercial orchards was developed, utilizing the YOLO11(or YOLOv11) object detection a…
Comprehensive Performance Evaluation of YOLOv12, YOLO11, YOLOv10, YOLOv9 and YOLOv8 on Detecting and Counting Fruitlet in Complex Orchard Environments
Ranjan Sapkota, Zhichao Meng, Martin Churuvija +3
This study systematically conducted an extensive real-world evaluation of all configurations of You Only Look Once (YOLO)-based object detection algorithms, including YOLOv8, YOLOv…
YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series
Ranjan Sapkota, Marco Flores Calero, Rizwan Qureshi +9
This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv12. Employing a reverse c…
Immature Green Apple Detection and Sizing in Commercial Orchards using YOLOv8 and Shape Fitting Techniques
Ranjan Sapkota, Dawood Ahmed, Martin Churuvija +1
Detecting and estimating size of apples during the early stages of growth is crucial for predicting yield, pest management, and making informed decisions related to crop-load manag…
Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments
Ranjan Sapkota, Dawood Ahmed, Manoj Karkee
Instance segmentation is an important image processing operation for agricultural automation, providing precise delineation of individual objects within images and enabling tasks s…