GrapesNet: Indian RGB & RGB-D Vineyard Image Datasets

Tracking #: 744-1724

Authors:



Responsible editor: 

Tobias Kuhn

Submission Type: 

Resource Paper

Abstract: 

In most of the countries, grapes are considered as a cash crop. Currently huge research is going on in development of automated grape harvesting systems. Speedy and reliable grape bunch detection is prime need for various deep learning based automated systems which deals with object detection and object segmentation tasks. But currently very few datasets are available on grape bunches in vineyard, because of which there is restriction to the research in this area. In comparison to the vineyard in outside countries, Indian vineyard structure is more complex, so it becomes hard to work in real-time. To overcome these problems and to make vineyard dataset for suitable for Indian vineyard scenarios, this paper proposed four different datasets on grape bunches in vineyard. For creating all datasets in GrapesNet, natural environmental conditions have been considered. GrapesNet includes total 11000+ images of grape bunches. Proposed datasets can be used for prime tasks like grape bunch detection, grape bunch segmentation, and grape bunch weight estimation etc. of future generation automated vineyard harvesting technologies.

Manuscript: 

Tags: 

  • Reviewed

Data repository URLs: 

Date of Submission: 

Monday, January 23, 2023

Date of Decision: 

Friday, July 21, 2023


Nanopublication URLs:

Decision: 

Reject

Solicited Reviews:


2 Comments

Meta-Review by Editor

The reviewers agree that this is a potentially useful resource worthy of publication, but there are a few things that should be better explained and the presentation of the manuscript needs to be improved. I would therefore like to ask the authors to submit an improved version taking these points into account.

Tobias Kuhn (https://orcid.org/0000-0002-1267-0234)

Withdrawn by the authors

Upon request by the authors, this submission was withdrawn and is thereby marked as Rejected.