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.
Keywords
Honey bee Deep learning Automatic classification Image processing Lineage classification Biodiversity
References
- Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M.A., Al-Amidie, M., Farhan, L., 2021. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1): 1-74.
- Barber, F.B.N., Oueslati, A.E., 2024. Human exons and introns classification using pre-trained Resnet-50 and GoogleNet models and 13-layers CNN model. Journal of Genetic Engineering and Biotechnology, 22(1): 100359.
- Crisci, C., Ghattas, B., Perera, G., 2012. A review of supervised machine learning algorithms and their applications to ecological data. Ecological Modelling, 240: 113-122.
- da Silva, F.L., Sella, M.L.G., Francoy, T.M., Costa, A.H.R., 2015. Evaluating classification and feature selection techniques for honeybee subspecies identification using wing images. Computers and Electronics in Agriculture, 114: 68-77.
- da Silva, I.N., Spatti, D.H., Flauzino, R.A., Liboni, L.H.B, Alves, S.F.R., 2016. Artificial Neural Networks: A Practical Course. Springer, Berlin.
- De Nart, D., Costa, C., Di Prisco, G., Carpana, E., 2022. Image recognition using convolutional neural networks for classification of honey bee subspecies. Apidologie, 53: 5.
- Estrach, J.B., Szlam, A., Le Cun, Y., 2014. Signal recovery from pooling representations. In International Conference on Machine Learning, June 21-26, China, pp. 307-315.
- Garcia, C.A.Y., Rodrigues, P.J., Tofilski, A., Elen, D., McCormak, G.P., Oleksa, A., Henriques, D., Ilyasov, R., Kartashev, A., Bargain, C., Fried, B., Pinto, M.A., 2022. Using the software DeepWings© to classify honey bees across Europe through wing geometric morphometrics. Insects, 13(12): 1132.
- Grzybowski, A., Pawlikowska-Łagód, K., Lambert, W.C., 2024. A history of artificial intelligence. Clinics in Dermatology, 42(3): 221-229.
- Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., Chen, T., 2018. Recent advances in convolutional neural networks. Pattern Recognition, 77: 354-377.
- Hodgkin, A.L., Huxley, A.F., 1952. The dual effect of membrane potential on sodium conductance in the giant axon of Loligo. The Journal of Physiology, 116(4): 497-506.
- Karthiga, M., Sountharrajan, S., Nandhini, S.S., Suganya, E., Sankarananth, S., 2021. A Deep Learning Approach to classify the Honeybee Species and health identification. Seventh International Conference on Bio Signals, Images, and Instrumentation (ICBSII), March 25-27, India, pp. 1-7.
- Khan, M.T., Kaushik, A.C., Ji, L., Malik, S.I., Ali, S., Wei, D.Q., 2019. Artificial neural networks for prediction of tuberculosis disease. Frontiers in Microbiology, 10: 395.
- Khanikar, D., Phookan, A., Gogoi, A., 2022. Artificial neural networks-An introduction and application in animal breeding and production: A review. Agricultural Reviews, 45(3): 480-487.
- Krogh, A., 2008. What are artificial neural networks? Nature Biotechnology, 26(2): 195-197.
- Kubat, M., 2021. An Introduction to Machine Learning. Springer, Berlin.
- Luo, R., Popp, J., Bocklitz, T., 2022. Deep learning for Raman spectroscopy: A review. Analytica, 3(3): 287-301.
- Nair, V., Hinton, G.E., 2010. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, Israel, pp. 807-814.
- Panziera, D., Requier, F., Chantawannakul, P., Pirk, C.W., Blacquière, T., 2022. The diversity decline in wild and managed honey bee populations urges for an integrated conservation approach. Frontiers in Ecology and Evolution, 10: 767950.
- Puig-Arnavat, M., Bruno, J.C., 2015. Artificial neural networks for thermochemical conversion of biomass. In: A. Pandey, T. Bhaskar, M. Stöcker and R. Sukumaran (Eds.), Recent Advances in Thermo-Chemical Conversion of Biomass, Elsevier, Amsterdam, pp. 133-156.
- Rafiq, G., Rafiq, M., Choi, G.S., 2023. Video description: A comprehensive survey of deep learning approaches. Artificial Intelligence Review, 56(11): 13293-13372.
- Rebelo, A.R., Fagundes, J.M., Digiampietri, L.A., Francoy, T.M., Bíscaro, H.H., 2021. A fully automatic classification of bee species from wing images. Apidologie, 52: 1060-1074.
- Rodrigues, P.J., Gomes, W., Pinto, M.A., 2022. DeepWings©: Automatic wing geometric morphometrics classification of honey bee (Apis mellifera) subspecies using deep learning for detecting landmarks. Big Data and Cognitive Computing, 6(3): 70.
- Scabini, L.F.S., Bruno, O.M., 2023. Structure and performance of fully connected neural networks: Emerging complex network properties. Physica A: Statistical Mechanics and its Applications, 615: 128585.
- Tasyurek, M., Arslan, R.S., 2023. RT-Droid: a novel approach for real-time android application analysis with transfer learning-based CNN models. Journal of Real-Time Image Processing, 20(3): 55.
- Traore, B.B., Kamsu-Foguem, B., Tangara, F., 2018. Deep convolution neural network for image recognition. Ecological Informatics, 48: 257-268.
- Wu, Z., Shen, C., Van Den Hengel, A., 2019. Wider or deeper: Revisiting the resnet model for visual recognition. Pattern Recognition, 90: 119-133.
- Zhang, X., Lu, J., Qu, X., Chen, X., 2025. An evaluation of morphometric characteristics of honey bee (Apis cerana) populations in the Qinghai-Tibet Plateau in China. Life, 15(2): 255.
- Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M., Parmar, M., 2024. A review of convolutional neural networks in computer vision. Artificial Intelligence Review, 57: 99.
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.
Keywords
Honey bee deep learning automatic classification image processing lineage classification biodiversity
References
- Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M.A., Al-Amidie, M., Farhan, L., 2021. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1): 1-74.
- Barber, F.B.N., Oueslati, A.E., 2024. Human exons and introns classification using pre-trained Resnet-50 and GoogleNet models and 13-layers CNN model. Journal of Genetic Engineering and Biotechnology, 22(1): 100359.
- Crisci, C., Ghattas, B., Perera, G., 2012. A review of supervised machine learning algorithms and their applications to ecological data. Ecological Modelling, 240: 113-122.
- da Silva, F.L., Sella, M.L.G., Francoy, T.M., Costa, A.H.R., 2015. Evaluating classification and feature selection techniques for honeybee subspecies identification using wing images. Computers and Electronics in Agriculture, 114: 68-77.
- da Silva, I.N., Spatti, D.H., Flauzino, R.A., Liboni, L.H.B, Alves, S.F.R., 2016. Artificial Neural Networks: A Practical Course. Springer, Berlin.
- De Nart, D., Costa, C., Di Prisco, G., Carpana, E., 2022. Image recognition using convolutional neural networks for classification of honey bee subspecies. Apidologie, 53: 5.
- Estrach, J.B., Szlam, A., Le Cun, Y., 2014. Signal recovery from pooling representations. In International Conference on Machine Learning, June 21-26, China, pp. 307-315.
- Garcia, C.A.Y., Rodrigues, P.J., Tofilski, A., Elen, D., McCormak, G.P., Oleksa, A., Henriques, D., Ilyasov, R., Kartashev, A., Bargain, C., Fried, B., Pinto, M.A., 2022. Using the software DeepWings© to classify honey bees across Europe through wing geometric morphometrics. Insects, 13(12): 1132.
- Grzybowski, A., Pawlikowska-Łagód, K., Lambert, W.C., 2024. A history of artificial intelligence. Clinics in Dermatology, 42(3): 221-229.
- Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., Chen, T., 2018. Recent advances in convolutional neural networks. Pattern Recognition, 77: 354-377.
- Hodgkin, A.L., Huxley, A.F., 1952. The dual effect of membrane potential on sodium conductance in the giant axon of Loligo. The Journal of Physiology, 116(4): 497-506.
- Karthiga, M., Sountharrajan, S., Nandhini, S.S., Suganya, E., Sankarananth, S., 2021. A Deep Learning Approach to classify the Honeybee Species and health identification. Seventh International Conference on Bio Signals, Images, and Instrumentation (ICBSII), March 25-27, India, pp. 1-7.
- Khan, M.T., Kaushik, A.C., Ji, L., Malik, S.I., Ali, S., Wei, D.Q., 2019. Artificial neural networks for prediction of tuberculosis disease. Frontiers in Microbiology, 10: 395.
- Khanikar, D., Phookan, A., Gogoi, A., 2022. Artificial neural networks-An introduction and application in animal breeding and production: A review. Agricultural Reviews, 45(3): 480-487.
- Krogh, A., 2008. What are artificial neural networks? Nature Biotechnology, 26(2): 195-197.
- Kubat, M., 2021. An Introduction to Machine Learning. Springer, Berlin.
- Luo, R., Popp, J., Bocklitz, T., 2022. Deep learning for Raman spectroscopy: A review. Analytica, 3(3): 287-301.
- Nair, V., Hinton, G.E., 2010. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, Israel, pp. 807-814.
- Panziera, D., Requier, F., Chantawannakul, P., Pirk, C.W., Blacquière, T., 2022. The diversity decline in wild and managed honey bee populations urges for an integrated conservation approach. Frontiers in Ecology and Evolution, 10: 767950.
- Puig-Arnavat, M., Bruno, J.C., 2015. Artificial neural networks for thermochemical conversion of biomass. In: A. Pandey, T. Bhaskar, M. Stöcker and R. Sukumaran (Eds.), Recent Advances in Thermo-Chemical Conversion of Biomass, Elsevier, Amsterdam, pp. 133-156.
- Rafiq, G., Rafiq, M., Choi, G.S., 2023. Video description: A comprehensive survey of deep learning approaches. Artificial Intelligence Review, 56(11): 13293-13372.
- Rebelo, A.R., Fagundes, J.M., Digiampietri, L.A., Francoy, T.M., Bíscaro, H.H., 2021. A fully automatic classification of bee species from wing images. Apidologie, 52: 1060-1074.
- Rodrigues, P.J., Gomes, W., Pinto, M.A., 2022. DeepWings©: Automatic wing geometric morphometrics classification of honey bee (Apis mellifera) subspecies using deep learning for detecting landmarks. Big Data and Cognitive Computing, 6(3): 70.
- Scabini, L.F.S., Bruno, O.M., 2023. Structure and performance of fully connected neural networks: Emerging complex network properties. Physica A: Statistical Mechanics and its Applications, 615: 128585.
- Tasyurek, M., Arslan, R.S., 2023. RT-Droid: a novel approach for real-time android application analysis with transfer learning-based CNN models. Journal of Real-Time Image Processing, 20(3): 55.
- Traore, B.B., Kamsu-Foguem, B., Tangara, F., 2018. Deep convolution neural network for image recognition. Ecological Informatics, 48: 257-268.
- Wu, Z., Shen, C., Van Den Hengel, A., 2019. Wider or deeper: Revisiting the resnet model for visual recognition. Pattern Recognition, 90: 119-133.
- Zhang, X., Lu, J., Qu, X., Chen, X., 2025. An evaluation of morphometric characteristics of honey bee (Apis cerana) populations in the Qinghai-Tibet Plateau in China. Life, 15(2): 255.
- Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M., Parmar, M., 2024. A review of convolutional neural networks in computer vision. Artificial Intelligence Review, 57: 99.
Details
| Primary Language | English |
|---|---|
| Subjects | Entomology, Bee and Silkworm Breeding and Improvement |
| Journal Section | Review |
| Authors | |
| Publication Date | June 30, 2025 |
| Submission Date | May 9, 2025 |
| Acceptance Date | June 26, 2025 |
| Published in Issue | Year 2025 Volume: 12 Issue: 2 |
Cite
TARANILAN DİZİNLER