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Deep Neural Networks for Image-Based Dietary Assessment
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Banana and Guava dataset for machine learning and deep learning-based quality classification.

Abiban Kumari1, Jaswinder Singh1

  • 1Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar 125001, Haryana, India.

Data in Brief
|November 11, 2024
PubMed
Summary

A new dataset for classifying banana and guava quality using machine learning is presented. This resource addresses the challenge of limited data, enabling the development of advanced fruit classification models.

Keywords:
Computer visionDeep learningFruit classificationImage processingMachine learning

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Area of Science:

  • Agricultural Science
  • Computer Science

Background:

  • Accurate fruit identification and classification are crucial for sustainable agriculture and horticulture.
  • Machine learning (ML) offers advanced techniques for fruit classification, but requires comprehensive datasets.
  • Limited availability of fruit datasets is a significant challenge for developing robust ML models.

Purpose of the Study:

  • To provide a comprehensive dataset for fruit classification, specifically for bananas and guavas.
  • To classify fruits based on quality using non-destructive methods.
  • To support the development of ML models for fruit quality assessment.

Main Methods:

  • A dataset of banana and guava images was collected using a Redmi Note 10-Pro mobile camera.
  • Images were captured in natural sunlight from various angles.
  • The dataset was classified into three categories: Class A, Class B, and Defect, based on physiological changes.

Main Results:

  • A comprehensive dataset for banana and guava quality classification was successfully created.
  • The dataset includes images categorized by quality (Class A, Class B) and defects.
  • The data is suitable for training and validating machine learning models.

Conclusions:

  • The developed dataset facilitates the creation of effective ML models for fruit quality classification.
  • This resource can aid the fruit storage, processing, and export industries through rapid and precise classification.
  • Availability of this dataset promotes advancements in non-destructive fruit quality assessment techniques.