Early Yield Estimation in Viticulture Based on Grapevine Inflorescence Detection and Counting in Videos-Reference-Cited by-同舟云学术

Early Yield Estimation in Viticulture Based on Grapevine Inflorescence Detection and Counting in Videos

Published:2023 Issue: Volume:11 Page:37790-37808
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Khokher Muhammad Rizwan¹^ORCID,Liao Qiyu¹^ORCID,Smith Adam L.²,Sun Changming¹^ORCID,Mackenzie Donald²,Thomas Mark R.²,Wang Dadong¹^ORCID,Edwards Everard J.²

Affiliation:

1. CSIRO Data61, Marsfield, NSW, Australia

2. CSIRO Agriculture and Food, Waite Campus, Urrbrae, SA, Australia

Funder

Department of Agriculture, Fisheries and Forestry, through their Rural Research and Development for Profit Scheme and Wine Australia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10087292.pdf?arnumber=10087292

Reference42 articles.

1. Grapevine flower estimation by applying artificial vision techniques on images with uncontrolled scene and multi-model analysis

2. Multiple Object Tracking Using K-Shortest Paths Optimization

3. A robust automated flower estimation system for grape vines

4. Hybrid Task Cascade for Instance Segmentation

5. Automated grapevine flower detection and quantification method based on computer vision and deep learning from on-the-go imaging using a mobile sensing platform under field conditions

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