TY  - JOUR
T1  - Deep Learning Approaches for Image-Based Classification of  Honey Bee (Apis mellifera) Lineages
TT  - Deep Learning Approaches for Image-Based Classification of  Honey Bee (Apis mellifera) Lineages
AU  - Karabağ, Kemal
AU  - Yıldız, Berkant İsmail
PY  - 2025
DA  - June
Y2  - 2025
DO  - 10.19159/tutad.1696120
JF  - Türkiye Tarımsal Araştırmalar Dergisi
JO  - TÜTAD
PB  - Siirt Üniversitesi
WT  - DergiPark
SN  - 2148-2306
SP  - 224
EP  - 230
VL  - 12
IS  - 2
LA  - en
AB  - Honey bees (Apis mellifera) play a vital role in maintaining ecosystem balance and supporting the sustainability of agricultural production. Accurate classification of these insects at the species and subspecies levels is essential for biodiversity monitoring, understanding local adaptation, and developing effective conservation strategies. In recent years, deep learning algorithms have emerged as powerful tools for automatic classification based on visual data. This review presents a comprehensive synthesis of studies utilizing deep learning-particularly convolutional neural networks (CNNs), transfer learning approaches, and hybrid models-for the image-based identification of honey bee lineages. The reviewed methods are evaluated in terms of their performance in image analysis and morphological differentiation. While the results demonstrate the high accuracy and rapid classification potential of deep learning models, current limitations such as dataset size, labeling challenges, and environmental variability are also discussed. By examining these strengths and constraints, this review aims to provide an in-depth perspective on the applicability of deep learning in honey bee research and outlines promising directions for future studies in this rapidly advancing field.
KW  - Honey bee
KW  - Deep learning
KW  - Automatic classification
KW  - Image processing
KW  - Lineage classification
KW  - Biodiversity
N2  - Honey bees (Apis mellifera) play a vital role in maintaining ecosystem balance and supporting the sustainability of agricultural production. Accurate classification of these insects at the species and subspecies levels is essential for biodiversity monitoring, understanding local adaptation, and developing effective conservation strategies. In recent years, deep learning algorithms have emerged as powerful tools for automatic classification based on visual data. This review presents a comprehensive synthesis of studies utilizing deep learning-particularly convolutional neural networks (CNNs), transfer learning approaches, and hybrid models-for the image-based identification of honey bee lineages. The reviewed methods are evaluated in terms of their performance in image analysis and morphological differentiation. While the results demonstrate the high accuracy and rapid classification potential of deep learning models, current limitations such as dataset size, labeling challenges, and environmental variability are also discussed. By examining these strengths and constraints, this review aims to provide an in-depth perspective on the applicability of deep learning in honey bee research and outlines promising directions for future studies in this rapidly advancing field.
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UR  - https://doi.org/10.19159/tutad.1696120
L1  - https://dergipark.org.tr/tr/download/article-file/4855729
ER  -