Exploring Transfer Learning for Enhanced Seed Classification: Pre-trained Xception Model
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer International Publishing Ag
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Seed classification plays a crucial role in various agricultural and industrial applications, such as crop breeding, seed quality assessment, and plant disease identification. This study presents a novel deep-learning model for seed classification. In this study, a dataset of 15 seeds has been created, containing around 3018 RGB images, with the objective of developing an accurate and efficient deep learning-based model capable of classifying seeds with high precision. In this study, we explore the effectiveness of two distinct approaches for seed classification: training the Xception model from scratch and leveraging transfer learning with the Pre-trained Xception model. The experimental results offer a comprehensive comparative analysis of training, validation, and testing outcomes. Notably, the Pre-trained Xception model showcases superior performance across various metrics. It achieves remarkable accuracy, attaining a perfect 1.0000 on both validation and test sets. Additionally, this model demonstrates significantly lower loss values throughout the training phases, highlighting its enhanced predictive capabilities. Impressively, convergence is reached with fewer epochs and in shorter training duration, further underlining the efficiency and effectiveness of the Pre-trained Xception model.
Açıklama
15th International Congress on Agricultural Mechanization and Energy in Agriculture (AnkAgEng) -- OCT 29-NOV 01, 2023 -- Antalya, TURKEY
Anahtar Kelimeler
Seed Classification, Deep Learning, CNN
Kaynak
15th International Congress on Agricultural Mechanization and Energy in Agriculture, Ankageng 2023
WoS Q Değeri
N/A
Scopus Q Değeri
Q4
Cilt
458
