192 citations · 443 across the 13 of their papers we have counts for
7 papers · 1 filter
AgRegNet: A Deep Regression Network for Flower and Fruit Density Estimation, Localization, and Counting in Orchards
Uddhav Bhattarai, Santosh Bhusal, Qin Zhang +1
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 estimati…
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…
Real-time Strawberry Detection Based on Improved YOLOv5s Architecture for Robotic Harvesting in open-field environment
Zixuan He, Salik Ram Khanal, Xin Zhang +2
This study proposed a YOLOv5-based custom object detection model to detect strawberries in an outdoor environment. The original architecture of the YOLOv5s was modified by replacin…
Generative AI in Agriculture: Creating Image Datasets Using DALL.E's Advanced Large Language Model Capabilities
Ranjan Sapkota, Manoj Karkee
The field of agricultural communication is evolving rapidly with the advent of generative artificial intelligence (AI), particularly image generation technologies. As these tools b…
Machine Vision System for Early-stage Apple Flowers and Flower Clusters Detection for Precision Thinning and Pollination
Salik Ram Khanal, Ranjan Sapkota, Dawood Ahmed +2
Early-stage identification of fruit flowers that are in both opened and unopened condition in an orchard environment is significant information to perform crop load management oper…
Swin-transformer-yolov5 For Real-time Wine Grape Bunch Detection
Shenglian Lu, Xiaoyu Liu, Zixaun He +3
In this research, an integrated detection model, Swin-transformer-YOLOv5 or Swin-T-YOLOv5, was proposed for real-time wine grape bunch detection to inherit the advantages from both…