Research Article
Bal Arı Hastalıklarının Sınıflandırılması için ConvMixer, VGG16 ve ResNet101 Tabanlı Topluluk Öğrenme Yaklaşımı
Abstract
Bal arıları birçok etkenden dolayı ekosistemin en önemli bileşenlerinden biridir. Fakat son zamanlarda artan varroa paraziti, iklim değişiklikleri ve böcek istilası gibi etkenlerden dolayı bal arıları tehdit altındadır. Bundan dolayı son zamanlarda gelişmiş yapay zekâ teknikleri ile arılarının analiz edilmesi oldukça önemli bir araştırma konusu olmuştur. Bu çalışmada arı hastalıklarının sınıflandırılması için Evrişimsel sinir ağ mimarileri tabanlı bir topluluk öğrenme yaklaşımı sunulmuştur. ConvMixer, VGG16 ve ResNet101 tabanlı topluluk öğrenme yaklaşımı (CVR-TÖY) olarak adlandırılan bu model temel olarak VGG16, ResNet101 ve ConvMixer sınıflandırıcılarının tahmin skorlarının birleştirmesine dayanmaktadır. Bu sayede farklı yaklaşım teknikleri ile geliştirilen VGG16, ResNet101 ve ConvMixer yapılarının tahmin çıktıları etkili bir şekilde birleştirilerek bal arı hastalık sınıflandırma performansı artırılmıştır. Tahmin skorları birleştirilirken iki yaklaşım denenmiştir. Birinci yaklaşımda modellerin tahmin çıktılarının en yüksek değeri alınarak sınıflandırma tahmini yapılmıştır. İkinci model ise ortalama değer alma yaklaşımıdır. Ortalama değer alma yaklaşımının ortak akıl modeli ile en iyi sonucu ürettiği görülmüştür. Deneysel çalışmalarda 6 farklı kovan probleminden etkilenen arı görüntülerini içeren BeeImage Dataset (BI) veri kümesi kullanılmıştır. Bu deneysel çalışmada önerilen modelden %98.87 F1-skoru elde edilmiştir. Ayrıca yapılan deneysel çalışmada önerilen model son teknolojik modeller ile karşılaştırılmıştır. Karşılaştırma sonucunda önerilen modelin F1-skoru %2.31 daha yüksek performans göstermiştir.
References
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- Berkaya SK, Gunal ES, Gunal S. Deep learning-based classification models for beehive monitoring. Ecol Inf 2021; 64: 101353.
- Bjerge K, Frigaard CE, Mikkelsen PH, Nielsen TH, Misbih M, Kryger P. A computer vision system to monitor the infestation level of Varroa destructor in a honeybee colony. Comput Electron Agric 2019; 164: 104898.
- Kimura T, Ohashi M, Okada R, Ikeno H. A new approach for the simultaneous tracking of multiple honeybees for analysis of hive behavior Apidologie 2011; 42: 607-617.
- Bozek K, Hebert L, Mikheyev AS, and Stephens GJ. Towards dense object tracking in a 2D honeybee hive. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018; 4185-4193.
- Ellis JD. "Small hive beetle (Aethina tumida) contributions to colony losses," in Honey Bee Colony Health, ed: CRC Press, 2011; pp. 135-144.
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- Metlek S, Kayaalp K. Detection of bee diseases with a hybrid deep learning method. Journal of the Faculty of Engineering and Architecture of Gazi University 2021; 36: 1715-1731.
- Calvo J. "Causes and Effects of Losing a Queen Bee," ed, 2020.
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- Yilmaz O, Erturk YE. "Honey bee biology in Turkey," in VII International Scientific Agriculture Symposium," Agrosym 2016", 6-9 October 2016, Jahorina, Bosnia and Herzegovina. Proceedings 2016; 2413-2418.
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- Voudiotis G, Moraiti A, Kontogiannis S, "Deep Learning Beehive Monitoring System for Early Detection of the Varroa Mite," Signals 2022; 3: 506-523.
- Nasser M, Yusof UK. "Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction," Diagnostics 2023; 13: 161.
- He K, Zhang X, Ren S, Sun J. "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; 770-778.
- Rezaei Z. "A review on image-based approaches for breast cancer detection, segmentation, and classification," Expert Syst Appl 2021; 182: 115204.
- Zhuang X, Liu F, Hou J, Hao J, Cai X. "Transformer-based interactive multi-modal attention network for video sentiment detection," Neural Process Lett 2022; 54: 1943-1960.
- Üzen H, Türkoğlu M, Yanikoglu B, Hanbay D. "Swin-MFINet: Swin transformer based multi-feature integration network for detection of pixel-level surface defects," Expert Syst Appl 2022; 209: 118269.
- Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, et al. "Swin-unet: Unet-like pure transformer for medical image segmentation," in European conference on computer vision, 2022; 205-218.
- Yang J, “The BeeImage Dataset: Annotated Honey Bee Images | Kaggle.” [Online]. Available: https://www.kaggle.com/jenny18/honey-bee-annotated-images. [Accessed: 12-Mar-2023].
- Tiryaki VM. "Mass segmentation and classification from film mammograms using cascaded deep transfer learning," Biomed Signal Process Control 2023; 84: 104819.
- Nikzad–Khasmakhia N, Balafara M, Feizi–Derakhshia MR, Motamedb C. "BERTERS: Multimodal Representation Learning for Expert Recommendation System with Transformer," arXiv preprint arXiv:2007.07229, 2020.
- Abdou MA. "Literature review: Efficient deep neural networks techniques for medical image analysis," Neural Comput Appl 2022; 34: 5791-5812.
- Üzen H, Yeroğlu C, Hanbay D. "Development of CNN architecture for Honey Bees disease condition," in 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), 2019; 1-5.
- Rasp N, Rahm E, Lange L. "A Study on the Impact of Class Imbalance on CNNs for Bee Health Detection," 2022.
ConvMixer, VGG16 and ResNet101 Based Ensemble Learning Approach for Classification of Honey Bee Diseases
Abstract
Honey bees are one of the most important components of the ecosystem due to many factors. However, honey bees are under threat due to factors such as varroa parasite, climate change and insect infestation, which have increased recently. Therefore, the analysis of honey bees with advanced artificial intelligence techniques has been a very important research topic recently. In this study, an ensemble learning approach based on convolutional neural network architectures is presented for the classification of bee diseases. This model, called ConvMixer, VGG16 and ResNet101-based ensemble learning approach (CVR-TOY), is basically based on the combination of predictive scores of VGG16, ResNet101 and ConvMixer classifiers. In this way, the prediction outputs of VGG16, ResNet101 and ConvMixer structures developed with different approach techniques were effectively combined to increase honey bee disease classification performance. Two approaches were tried when combining the prediction scores. In the first approach, classification prediction is made by taking the highest value of the prediction outputs of the models. The second model is the averaging approach. It has been seen that the averaging approach produces the best results with the common sense model. In experimental studies, BI dataset, which contains images of bees affected by 6 different hive problems, was used. In this experimental study, an F1-score of 98.87% was obtained from the proposed model. In addition, the proposed model in the experimental study was compared with the latest technological models. As a result of the comparison, the F1-score of the proposed model showed 2.31% higher performance.
References
- Muz MN, Özdemir N, Dilek M. Küresel arı sağlığı ve veteriner hekimlik. Veteriner Farmakoloji ve Toksikoloji Derneği Bülteni 2019; 10: 24-30.
- Öztekin C, Çapa Aydın Y, Yılmaz Tüzün Ö. Biyoloji öğretmen adaylarının genel biyoloji konularındaki kavram yanılgıları, Hacettepe Üniversitesi Eğitim Fakültesi Dergisi 2000; 140–147.
- Huckle J., "British Bee Journal," ed: British Bee Publications, London, England, 1882.
- Berkaya SK, Gunal ES, Gunal S. Deep learning-based classification models for beehive monitoring. Ecol Inf 2021; 64: 101353.
- Bjerge K, Frigaard CE, Mikkelsen PH, Nielsen TH, Misbih M, Kryger P. A computer vision system to monitor the infestation level of Varroa destructor in a honeybee colony. Comput Electron Agric 2019; 164: 104898.
- Kimura T, Ohashi M, Okada R, Ikeno H. A new approach for the simultaneous tracking of multiple honeybees for analysis of hive behavior Apidologie 2011; 42: 607-617.
- Bozek K, Hebert L, Mikheyev AS, and Stephens GJ. Towards dense object tracking in a 2D honeybee hive. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018; 4185-4193.
- Ellis JD. "Small hive beetle (Aethina tumida) contributions to colony losses," in Honey Bee Colony Health, ed: CRC Press, 2011; pp. 135-144.
- Ellis J, Hepburn H. An ecological digest of the small hive beetle (Aethina tumida), a symbiont in honey bee colonies (Apis mellifera). Insectes sociaux 2006; 53: 8-19.
- Metlek S, Kayaalp K. Detection of bee diseases with a hybrid deep learning method. Journal of the Faculty of Engineering and Architecture of Gazi University 2021; 36: 1715-1731.
- Calvo J. "Causes and Effects of Losing a Queen Bee," ed, 2020.
- Payne AN, Shepherd TF, Rangel J.The detection of honey bee (Apis mellifera)-associated viruses in ants. Sci Rep 2020; 10: 2923.
- Yilmaz O, Erturk YE. "Honey bee biology in Turkey," in VII International Scientific Agriculture Symposium," Agrosym 2016", 6-9 October 2016, Jahorina, Bosnia and Herzegovina. Proceedings 2016; 2413-2418.
- Simonyan K, Zisserman A. "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
- Franceschini S, Ambrosanio M, Vitale S, Baselice F, Gifuni A, Grassini G, et al., "Hand gesture recognition via radar sensors and convolutional neural networks," in 2020 IEEE Radar Conference (RadarConf20) 2020; 1-5.
- Trockman A, Kolter JZ. "Patches are all you need?," arXiv preprint arXiv:2201.09792, 2022.
- Mohammed A, Kora R. "A comprehensive review on ensemble deep learning: Opportunities and challenges," Journal of King Saud University-Computer and Information Sciences 2023.
- Matloob F, Ghazal TM, Taleb N, Aftab S, Ahmad M, Khan MA, et al., "Software defect prediction using ensemble learning: A systematic literature review," IEEE Access 2021; 9: 98754-98771.
- Chazette L, Becker M, Szczerbicka H. "Basic algorithms for bee hive monitoring and laser-based mite control," in 2016 IEEE symposium series on computational intelligence (SSCI) 2016; 1-8.
- Tashakkori R, Hernandez NP, Ghadiri A, Ratzloff AP, Crawford MB. "A honeybee hive monitoring system: From surveillance cameras to Raspberry Pis," in SoutheastCon 2017; 1-7.
- Chen C, Yang EC, Jiang JA, Lin TT. "An imaging system for monitoring the in-and-out activity of honey bees," Comput Electron Agric 2012; 89: 100-109.
- Chiron G, Gomez-Krämer P, Ménard M. "Detecting and tracking honeybees in 3D at the beehive entrance using stereo vision," EURASIP Journal on Image and Video Processing 2013; 2013: 1-17.
- Tashakkori R, Ghadiri A. "Image processing for honey bee hive health monitoring," in SoutheastCon 2015, 2015; 1-7.
- Boenisch F, Rosemann B, Wild B, Dormagen D, Wario F, Landgraf T. "Tracking all members of a honey bee colony over their lifetime using learned models of correspondence," Frontiers in Robotics and AI, 2018; 5: 35.
- Magnier B, Ekszterowicz G, Laurent J, Rival M, Pfister F. "Bee hive traffic monitoring by tracking bee flight paths," in 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, January 27-29, 2018, in Funchal, Madeira, Portugal, 2018; 563-571.
- Rodriguez IF, Megret R, Acuna E, Agosto-Rivera JL, Giray T. "Recognition of pollen-bearing bees from video using convolutional neural network," in 2018 IEEE winter conference on applications of computer vision (WACV) 2018; 314-322.
- Yang J. "The beeimage dataset: Annotated honey bee images," Accessed: Aug 2018; 13: 2019.
- Yoo J, Siddiqua R, Liu X, Ahmed KA, Hossain MZ. "BeeNet: An End-To-End Deep Network For Bee Surveillance," Procedia Comput Sci 2023; 222: 415-424.
- Voudiotis G, Moraiti A, Kontogiannis S, "Deep Learning Beehive Monitoring System for Early Detection of the Varroa Mite," Signals 2022; 3: 506-523.
- Nasser M, Yusof UK. "Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction," Diagnostics 2023; 13: 161.
- He K, Zhang X, Ren S, Sun J. "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; 770-778.
- Rezaei Z. "A review on image-based approaches for breast cancer detection, segmentation, and classification," Expert Syst Appl 2021; 182: 115204.
- Zhuang X, Liu F, Hou J, Hao J, Cai X. "Transformer-based interactive multi-modal attention network for video sentiment detection," Neural Process Lett 2022; 54: 1943-1960.
- Üzen H, Türkoğlu M, Yanikoglu B, Hanbay D. "Swin-MFINet: Swin transformer based multi-feature integration network for detection of pixel-level surface defects," Expert Syst Appl 2022; 209: 118269.
- Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, et al. "Swin-unet: Unet-like pure transformer for medical image segmentation," in European conference on computer vision, 2022; 205-218.
- Yang J, “The BeeImage Dataset: Annotated Honey Bee Images | Kaggle.” [Online]. Available: https://www.kaggle.com/jenny18/honey-bee-annotated-images. [Accessed: 12-Mar-2023].
- Tiryaki VM. "Mass segmentation and classification from film mammograms using cascaded deep transfer learning," Biomed Signal Process Control 2023; 84: 104819.
- Nikzad–Khasmakhia N, Balafara M, Feizi–Derakhshia MR, Motamedb C. "BERTERS: Multimodal Representation Learning for Expert Recommendation System with Transformer," arXiv preprint arXiv:2007.07229, 2020.
- Abdou MA. "Literature review: Efficient deep neural networks techniques for medical image analysis," Neural Comput Appl 2022; 34: 5791-5812.
- Üzen H, Yeroğlu C, Hanbay D. "Development of CNN architecture for Honey Bees disease condition," in 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), 2019; 1-5.
- Rasp N, Rahm E, Lange L. "A Study on the Impact of Class Imbalance on CNNs for Bee Health Detection," 2022.
There are 41 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Deep Learning |
| Journal Section | MBD |
| Authors | |
| Publication Date | March 28, 2024 |
| Submission Date | July 12, 2023 |
| Published in Issue | Year 2024 Volume: 36 Issue: 1 |
