An innovative voting ensemble learning approach for sorting and classifying date fruit varieties DOI Creative Commons
Sofiane Achiche,

Bendjima Mostefa,

Benkrama Soumia

et al.

STUDIES IN ENGINEERING AND EXACT SCIENCES, Journal Year: 2024, Volume and Issue: 5(3), P. e12378 - e12378

Published: Dec. 18, 2024

Dates are among Algeria's most significant agricultural crops due to their considerable health and financial benefits. Moreover, they constitute an essential export commodity beyond the hydrocarbon sector. The current traditional methods for classifying sorting dates inefficient, time-consuming, labor-intensive, resulting in a disparity between limited exports high production levels. This study proposes Ensemble Learning (EL) model that employs Transfer (TL) techniques address impediments enhance date fruit categorization. We evaluate performance of four classifiers: MobileNetV2, EfficientNet, DenseNet201, EL soft voting classifier uses these TL methods, work on set 1,619 images 20 different varieties Algerian dates. dataset ranks largest benchmarks varietal variety. proposed hybrid has outstanding performance, with validation accuracy 98.67% classification 99.92%. It sets novel standard technology by surpassing all evaluated models precision, recall, F1-score. These findings illustrate approach's capacity entirely revolutionize significantly productivity efficiency.

Language: Английский

Plant diseases classification with Spectral Signature Taxonomy & Analysis Software (SSTAS) DOI Open Access

Jayswal Hardik,

Hetvi Desai,

Hasti Vakani

et al.

Software Impacts, Journal Year: 2025, Volume and Issue: unknown, P. 100744 - 100744

Published: March 1, 2025

Language: Английский

Citations

0
Robust Network Intrusion Detection System Using VGG16, Autoencoder, and Random Forest for Enhanced Cybersecurity in IOT DOI
Jameer Kotwal,

Atharv Kulkarni,

Ashutosh Wagh

et al.

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 3 - 16

Published: Jan. 1, 2025

Language: Английский

Citations

0
An innovative voting ensemble learning approach for sorting and classifying date fruit varieties DOI Creative Commons
Sofiane Achiche,

Bendjima Mostefa,

Benkrama Soumia

et al.

STUDIES IN ENGINEERING AND EXACT SCIENCES, Journal Year: 2024, Volume and Issue: 5(3), P. e12378 - e12378

Published: Dec. 18, 2024

Dates are among Algeria's most significant agricultural crops due to their considerable health and financial benefits. Moreover, they constitute an essential export commodity beyond the hydrocarbon sector. The current traditional methods for classifying sorting dates inefficient, time-consuming, labor-intensive, resulting in a disparity between limited exports high production levels. This study proposes Ensemble Learning (EL) model that employs Transfer (TL) techniques address impediments enhance date fruit categorization. We evaluate performance of four classifiers: MobileNetV2, EfficientNet, DenseNet201, EL soft voting classifier uses these TL methods, work on set 1,619 images 20 different varieties Algerian dates. dataset ranks largest benchmarks varietal variety. proposed hybrid has outstanding performance, with validation accuracy 98.67% classification 99.92%. It sets novel standard technology by surpassing all evaluated models precision, recall, F1-score. These findings illustrate approach's capacity entirely revolutionize significantly productivity efficiency.

Language: Английский

Citations

0