Zobrazeno 1 - 10
of 324
pro vyhledávání: '"P., Karkee"'
Autor:
Sapkota, Ranjan, Karkee, Manoj
This study conducted a comprehensive performance evaluation on YOLO11 and YOLOv8, the latest in the "You Only Look Once" (YOLO) series, focusing on their instance segmentation capabilities for immature green apples in orchard environments. YOLO11n-se
Externí odkaz:
http://arxiv.org/abs/2410.19869
Autor:
Sapkota, Ranjan, Karkee, Manoj
In this study, a robust method for 3D pose estimation of immature green apples (fruitlets) in commercial orchards was developed, utilizing the YOLO11 object detection and pose estimation algorithm alongside Vision Transformers (ViT) for depth estimat
Externí odkaz:
http://arxiv.org/abs/2410.19846
Autor:
Bhattarai, Uddhav, Sapkota, Ranjan, Kshetri, Safal, Mo, Changki, Whiting, Matthew D., Zhang, Qin, Karkee, Manoj
Global food production depends upon successful pollination, a process that relies on natural and managed pollinators. However, natural pollinators are declining due to different factors, including climate change, habitat loss, and pesticide use. Thus
Externí odkaz:
http://arxiv.org/abs/2409.19918
One of the major challenges for the agricultural industry today is the uncertainty in manual labor availability and the associated cost. Automated flower and fruit density estimation, localization, and counting could help streamline harvesting, yield
Externí odkaz:
http://arxiv.org/abs/2409.17400
Autor:
Sapkota, Ranjan, Meng, Zhichao, Churuvija, Martin, Du, Xiaoqiang, Ma, Zenghong, Karkee, Manoj
This study extensively evaluated You Only Look Once (YOLO) object detection algorithms across all configurations (total 22) of YOLOv8, YOLOv9, YOLOv10, and YOLO11 for green fruit detection in commercial orchards. The research also validated in-field
Externí odkaz:
http://arxiv.org/abs/2407.12040
Autor:
Sapkota, Ranjan, Qureshi, Rizwan, Calero, Marco Flores, Badjugar, Chetan, Nepal, Upesh, Poulose, Alwin, Zeno, Peter, Vaddevolu, Uday Bhanu Prakash, Khan, Sheheryar, Shoman, Maged, Yan, Hong, Karkee, Manoj
This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv10. Employing a reverse chronological analysis, this study examines the advancements introduced
Externí odkaz:
http://arxiv.org/abs/2406.19407
Autor:
Karkee, Rijan, Strubbe, David A.
Transition-metal dichalcogenides (TMDs) show unique physical, optical, and electronic properties. The known phases of TMDs are 2H and 3R in bulk form, 1T and associated reconstructions, and 1H in monolayer form. This paper reports a hypothetical phas
Externí odkaz:
http://arxiv.org/abs/2404.17575
Autor:
Paudel, Achyut, Brown, Jostan, Upadhyaya, Priyanka, Asad, Atif Bilal, Kshetri, Safal, Davidson, Joseph R., Grimm, Cindy, Thompson, Ashley, Sallato, Bernardita, Whiting, Matthew D., Karkee, Manoj
Apple trees, being deciduous, shed leaves each year. This process is preceded by the change in leaf color from green to yellow (also known as senescence) during the fall season. The rate and timing of color change are affected by factors including ni
Externí odkaz:
http://arxiv.org/abs/2404.14653
Instance segmentation, an important image processing operation for automation in agriculture, is used to precisely delineate individual objects of interest within images, which provides foundational information for various automated or robotic tasks
Externí odkaz:
http://arxiv.org/abs/2312.07935
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 management, harvest and post-harvest logistics, and marketing. Traditional
Externí odkaz:
http://arxiv.org/abs/2401.08629