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: 

File ds-paper-744.docx

Tags: 

  • Under Review

Data repository URLs: 

Direct URL to data:  https://data.mendeley.com/datasets/mhzmzd5cwx/1 

Date of Submission: 

Monday, January 23, 2023


1 Comment

Review the paper and comment.

Submitted by Malik Jawarneh on

Good:

-Provides a comprehensive overview of the current research and challenges in developing automated grape harvesting systems.

-Discusses the need for datasets that can accommodate the complexities of Indian vineyard structures. -Proposes four different datasets of grape bunches in vineyard and provides details on the number of images and tasks that can be used.

Negative: -Does not provide any details on the collection process of the datasets. -

Does not include any experiments or results to demonstrate the effectiveness of the proposed datasets.

 

General Comments

  1. Structure your abstract as follows- 1) Background 2) Aim/Objective 3) Methodology 4) Results 5) Conclusion. Write 2-4 lines for each and merge everything in one paragraph without any subheading
  2. Abstract must contain the motivation and objective of the article. The Abstract must be very clear and the motive of the paper should be represented in a nutshell.
  3. Introduction should be of 5-7 solid paragraphs and provide structure of work at the end of the Introduction section.
  4. Add more contribution to your study field.
  5. The purpose of study not clear.
  6. Make highlight for objectives
  7. Remove any table or figure which is taken from web. Otherwise you have to get approval from publisher and author in a provided form by springer.
  8. Please avoid to write definitions of terms like Artificial intelligence, grape bunch segmentation, vineyard dataset etc., which are already available over web, try to cite work for such information.
  9. In summary, only provide useful content in your work.
  10. These are Title related to your area, you may use.
  11. Add your references section
  12. Please list all abbreviations used in your manuscript under the heading "Abbreviations" after the conclusions section
  • Pallathadka, H., Mustafa, M., Sanchez, D. T., Sajja, G. S., Gour, S., & Naved, M. (2021). Impact of machine learning on management, healthcare and agriculture. Materials Today: Proceedings.
  • Ansari, A.S., Jawarneh, M., Ritonga, M., Jamwal, P., Mohammadi, M.S., Veluri, R.K., Kumar, V. and Shah, M.A., 2022. Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease. Journal of Food Quality, 2022.
  • Pallathadka, H., Jawarneh, M., Sammy, F., Garchar, V., Sanchez, T., & Naved, M. (2022, April). A Review of Using Artificial Intelligence and Machine Learning in Food and Agriculture Industry. In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 2215-2218). IEEE.
  • JAWARNEH, M. (2023). Development of Machine Learning Based Security Model for IoT Network.