Zobrazeno 1 - 10
of 99
pro vyhledávání: '"Sapkota, Ranjan"'
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
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
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
Autor:
Sapkota, Ranjan, Ahmed, Dawood, Khanal, Salik Ram, Bhattarai, Uddhav, Mo, Changki, Whiting, Matthew D., Karkee, Manoj
This research presents a novel, robotic pollination system designed for targeted pollination of apple flowers in modern fruiting wall orchards. Developed in response to the challenges of global colony collapse disorder, climate change, and the need f
Externí odkaz:
http://arxiv.org/abs/2311.10755
Autor:
Sapkota, Ranjan, Karkee, Manoj
This research investigated the role of artificial intelligence (AI), specifically the DALL.E model by OpenAI, in advancing data generation and visualization techniques in agriculture. DALL.E, an advanced AI image generator, works alongside ChatGPT's
Externí odkaz:
http://arxiv.org/abs/2307.08789
Labor shortages in fruit crop production have prompted the development of mechanized and automated machines as alternatives to labor-intensive orchard operations such as harvesting, pruning, and thinning. Agricultural robots capable of identifying tr
Externí odkaz:
http://arxiv.org/abs/2304.13282