Affiliations 

  • 1 Centre for Advanced Data Science, Vellore Institute of Technology, Kelambakkam-vandalur road, Chennai, 600127, India
  • 2 School of Electronics Engineering, Vellore Institute of Technology, Kelambakkam-vandalur road, Chennai, 600127, India
  • 3 Department of Electronics Engineering, MIT, Anna University Chennai, 600044, India
  • 4 Asia Pacific University of Technology and Innovation, Malaysia, 57000
Data Brief, 2024 Aug;55:110645.
PMID: 39015255 DOI: 10.1016/j.dib.2024.110645

Abstract

Okra, renowned for its abundance of essential nutrients, emerges as a promising solution in addressing malnutrition, advocating for sustainable agriculture, and showcasing versatile untapped potentials. Our objective is to enhance the quality, market attractiveness, and culinary adaptability of okra harvests by classifying them into over-matured and adequately matured groups through a non-invasive approach. This dataset is centered on thermal images capturing different maturity levels of okra, categorized into two distinct groups. The thermal imaging device is employed for image capture, and the okra samples are sourced from diverse vegetable vendors and farms. This dataset proves to be a valuable asset for the non-invasive examination and categorization of okras based on their maturity levels.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.