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Enhanced Model for Mango Detection and Quality Classification Using Optimized Feature Extraction Techniques

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Pubblicato in:The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings (2025)
Autore principale: Adla Aryan
Altri autori: Abdul Aleem Mohammed, Chabra, Manish, Rasheed, Syed Saarib, Mohammed, Adnan, Mohammed Abdul Raoof
Pubblicazione:
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Soggetti:
Feature extraction
Algorithms
Heuristic methods
Mangoes
Economic
Accesso online:Citation/Abstract
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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, IndiaThis 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.
DOI:10.1109/SCEECS64059.2025.10941017
Fonte:Science Database