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

Đã lưu trong:

Chi tiết về thư mục
Xuất bản năm:	The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings (2025)
Tác giả chính:	Adla Aryan
Tác giả khác:	Abdul Aleem Mohammed, Chabra, Manish, Rasheed, Syed Saarib, Mohammed, Adnan, Mohammed Abdul Raoof
Được phát hành:	The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Những chủ đề:	Feature extraction Algorithms Heuristic methods Mangoes Economic
Truy cập trực tuyến:	Citation/Abstract
Các nhãn:	Thêm thẻ Không có thẻ, Là người đầu tiên thẻ bản ghi này!

Miêu tả
Bài tóm tắt:	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
Nguồn:	Science Database