Identification of Papaya Fruit Ripening Process Using AI

Year : 2024 | Volume :13 | Issue : 02 | Page : –
By

Jaya M. Pattanshetti1 M. Pattanshetti1,

Ms. Nutan N. Patil,

Ms. Pallavi S. Khanagonka,

Ms. Shivani K. Dharmoji,

Ms. Shradha D. Kurangi,

  1. Project Guide S. G. Balekundri Institute of Technology Belagavi India
  2. S. G. Balekundri Institute of Technology Belagavi India
  3. Student S. G. Balekundri Institute of Technology Belagavi India
  4. Student S. G. Balekundri Institute of Technology Belagavi India
  5. Student S. G. Balekundri Institute of Technology Belagavi India

Abstract

Identifying the ripening process of papaya fruit using artificial intelligence involves employing machine learning algorithms to analyze various features such as color changes, texture alterations and chemical compositions. This model is capable of analyzing visual cues to determine the stage of ripeness. The dataset compares images of papaya at various ripening stages, and our AI model demonstrated high accuracy in classifying these stages. Employing machine learning algorithms and image processing techniques, this project discerns the ripening stages of papaya fruit, distinguishing between natural and artificial ripening through analysis of color and texture features. Leveraging CNN and YOLO models, our approach conducts visual cues analysis to ascertain ripeness stages. Comparative analysis of papaya images at varying ripening stages enriches our database. Demonstrating high accuracy, our AI model proficiently identifies these phases. This study pioneer’s artificial intelligence methodologies for papaya ripening process identification, employing image processing and machine learning algorithms. It innovatively utilizes AI approaches to distinguish between artificially and naturally ripened papaya fruit. The implementation of this AI-based solution holds significant potential for enhancing efficiency and precision in the agricultural industry, facilitating optimal harvesting and post-harvest management practices for papaya farmers, ultimately leading to improved crop quality and reduced waste. The methodology involves the collection of various features such as color, texture, and seeds using image processing and machine learning algorithms. A dataset comprising images of both artificially and naturally ripened papayas will be used to train and validate the AI model. The developed system will contribute to ensuring the quality and safety of papaya fruits in the supply chain, addressing concerns related to artificially ripened fruits. The project aims to contribute to food safety and quality assurance by providing a non-invasive and efficient means of identifying the ripening process of papayas. The project involves the development of a machine learning model trained on a dataset comprising images of papaya fruit at different ripening stages.

Keywords: Artificial Intelligence (AI), Convolution neural network (CNN) Model, YOLO Model, Image preprocessing, Computer Vision, OpenCV library

[This article belongs to Research & Reviews : Journal of Food Science & Technology(rrjofst)]

How to cite this article: Jaya M. Pattanshetti1 M. Pattanshetti1, Ms. Nutan N. Patil, Ms. Pallavi S. Khanagonka, Ms. Shivani K. Dharmoji, Ms. Shradha D. Kurangi. Identification of Papaya Fruit Ripening Process Using AI. Research & Reviews : Journal of Food Science & Technology. 2024; 13(02):-.
How to cite this URL: Jaya M. Pattanshetti1 M. Pattanshetti1, Ms. Nutan N. Patil, Ms. Pallavi S. Khanagonka, Ms. Shivani K. Dharmoji, Ms. Shradha D. Kurangi. Identification of Papaya Fruit Ripening Process Using AI. Research & Reviews : Journal of Food Science & Technology. 2024; 13(02):-. Available from: https://journals.stmjournals.com/rrjofst/article=2024/view=152791

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Regular Issue Subscription Original Research
Volume 13
Issue 02
Received May 4, 2024
Accepted June 28, 2024
Published July 3, 2024
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