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© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM34204

Fresh Fruit Detection Using Deep Learning

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM34204

Volume:

8 |

5 |

May 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM34204

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

DAK

Dr. Aniruddha Kailuke

PAB

Prof. Aparna Bondade

NN

Nayan Naik

RM

Rahul Meshram

RV

Rahul Vishwakarma

RR

Rupesh Rithe

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Abstract

The Fresh Fruit Identification System constitutes a notable advancement in the meals industry. Employing sophisticated technologies along with machine-getting-to-know and computer imagination and prescient, it complements the great evaluation and optimization of perishable items, mainly focusing on culmination and greens. This contemporary system employs advanced gadgets gaining knowledge of models, especially convolutional neural networks (CNNs), to meticulously analyze high-decision pics of processed meals. By rapidly and appropriately identifying objects based totally on attributes that include shade, texture, and shape, the machine guarantees no longer the simplest precision in categorization but also an in-intensity best evaluation. This entails defect detection and the assessment of ripeness, making sure that the most effective rate-satisfactory merchandise reaches customers. The implementation of sturdy and scalable systems has adeptly addressed demanding situations associated with facts, model education, and real-time processing. Consequently, the Fresh Food Identification System emerges as a pivotal contributor to raising the standards of the perishable meals supply chain, embodying a dedication to excellence and performance.

KEYWORDS

Fresh Fruit Detection Machine Learning Computer Vision Fruit Quality Assessment Food Safety Image Processing Classification Algorithms Object Detection Feature Extraction Deep Learning Convolutional Neural Networks (CNN).

Cite This Article

Dr. Aniruddha Kailuke, Prof. Aparna Bondade, Nayan Naik, Rahul Meshram, Rahul Vishwakarma, Rupesh Rithe, "Fresh Fruit Detection Using Deep Learning," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024. https://doi.org/10.55041/IJSREM34204

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Publication Date

12/31/45428

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Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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