Deep Learning Approaches for Image-Based Classification of Honey Bee (Apis mellifera) Lineages

Abstract

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.

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.