Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)

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Analysis of Fruits and Vegetable Conditions Using Image Processing

Authors
Talupula Jahnavi1, *, M. N. Renuka Devi1, Punith Amilineni1
1Department of CSE, Dayananda Sagar University, Bangalore, India
*Corresponding author. Email: Jahnavitalupula3004@gmail.com
Corresponding Author
Talupula Jahnavi
Available Online 19 April 2025.
DOI
10.2991/978-94-6463-700-7_21How to use a DOI?
Keywords
Image Processing; Computer Vision; Segmentation; Thresholding; Feature Extraction; Otsu thresholding; RGB Masking; K-means Clustering; Defect Detection
Abstract

This research presents a strong framework for automated fruit and vegetable quality inspection through advanced image processing techniques. Such applications range from defect detection to freshness assessment, enabling agriculture supply chains and retailers to classify produce into good and infected layers. It uses RGB masking, Otsu thresholding, and K-means clustering to develop a very efficient segmentation and feature extraction scheme. Feature computations such as defect area, mean intensity, and shape descriptors help isolate and classify the defective area. Apples, mangoes, and potatoes are analyzed in well-controlled lighting conditions for consistent results in this experimental setup. The proposed system embodies major advantages like scalability, accuracy, and quick response while maintaining diversity in product types and lighting conditions. Results state the efficient defect isolation and classification, while visualization through a binary and masked image manifests a clear understanding of the work. Future work includes the proposal of integrating machine-learning-enhanced adaptability and performance over various agricultural datasets to enhance quality assurance methods in food supply systems.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
Series
Advances in Intelligent Systems Research
Publication Date
19 April 2025
ISBN
978-94-6463-700-7
ISSN
1951-6851
DOI
10.2991/978-94-6463-700-7_21How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Talupula Jahnavi
AU  - M. N. Renuka Devi
AU  - Punith Amilineni
PY  - 2025
DA  - 2025/04/19
TI  - Analysis of Fruits and Vegetable Conditions Using Image Processing
BT  - Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
PB  - Atlantis Press
SP  - 263
EP  - 270
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-700-7_21
DO  - 10.2991/978-94-6463-700-7_21
ID  - Jahnavi2025
ER  -