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Abstract

Conference Title: 2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

Conference Start Date: 2025, Jan. 18

Conference End Date: 2025, Jan. 19

Conference Location: Bhopal, India

This paper introduces an automated grading system for mangoes, enhancing efficiency and accuracy compared to human-based methods. The system uses the Lion Assisted Firefly Algorithm (LA-FF) to extract the best features from multiple highlights, enhancing grading efficiency and accuracy. The LA-FF algorithm is then used to fine-tune the convolutional layers of a deep CNN based on the specific requirements of mango grading. The system integrates the latest algorithms, automation, and adaptation to create an even more effective and precise grading system suitable for rural agricultural contexts. The LA-FF algorithm is used to extract the best features from multiple highlights, resulting in a more accurate and efficient grading process.

Details

10000404
Sustainability pillar
Title
Enhanced Model for Mango Detection and Quality Classification Using Optimized Feature Extraction Techniques
Author
Adla Aryan 1 ; Abdul Aleem Mohammed 2 ; Chabra, Manish 1 ; Rasheed, Syed Saarib 3 ; Mohammed, Adnan 3 ; Mohammed Abdul Raoof 2 

 Vardhaman College of Engineering,Department of Artificial Intelligence & Machine Learning,Hyderabad,Telangana,India,501218 
 Muffakham Jah College of Engineering and Technology,Department of Computer Science and Engineering,Hyderabad,India,500034 
 Methodist College of Engineering and Technology,Department of Artificial Intelligence & Data Science,Hyderabad,Telangana,India,500001 
Source details
2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)
Publication date
2025
Publisher
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Place of publication
Piscataway
Country of publication
United States
Source type
Conference Paper
Language of publication
English
Document type
Conference Proceedings
Publication history
 
 
Online publication date
2025-04-02
Publication history
 
 
   First posting date
02 Apr 2025
ProQuest document ID
3185351642
Document URL
https://www.proquest.com/conference-papers-proceedings/enhanced-model-mango-detection-quality/docview/3185351642/se-2?accountid=208611
Copyright
Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
Last updated
2025-05-27
Database
2 databases
  • ProQuest One Academic
  • ProQuest One Academic