Arı hastalıklarının hibrit bir derin öğrenme yöntemi ile tespiti

Yıl 2021, Cilt: 36 Sayı: 3, 1715 - 1732, 24.05.2021

Sedat Metlek , Kiyas Kayaalp

https://doi.org/10.17341/gazimmfd.749443

Cited By: 5

Öz

Canlı türlerinin gelişiminde büyük bir etkisi olan arılar Dünya’da ki en eski canlı türlerinden birisidir. Besin zincirinin en altında bulunan bitkilerin devamlılığı arıların tozlaşma yapmasıyla doğrudan ilgilidir. Arılar bu özelliğinden dolayı küresel bir sigorta konumundadır. Bu nedenle arıların sağlık durumlarının kontrol edilmesi oldukça önemlidir. Günümüzde gelişen teknolojiye bağlı olarak, arıların sağlık durumlarının uzaktan gerçek zamanlı görüntü işleme uygulamaları ile kontrol edilebilmesi mümkün olabilmektedir. Gerçekleştirilen çalışmada derin öğrenmenin güçlü yanlarından olan öznitelik çıkarma yöntemleri iki farklı koldan işletilerek, görüntülerdeki agresif değişiklikler tespit edilmiştir. Sınıflandırma işleminde, olasılık hesabına dayanan ve sınıf sayısı kadar çıkış veren Softmax sınıflandırıcısı ile tek bir çıkış verebilen ve bu çıkışta da sınıf bilgisini sunabilen çok katmanlı geri beslemeli yapay sinir ağı (ÇKGB-YSA) kullanılmıştır. Yapılan deneysel çalışmalar neticesinde, aynı veri seti üzerinde altı farklı arı hastalığı için softmax sınıflandırıcısı ile %92,70 başarım oranı yakalanabilirken, geliştirilen sistem ile %94,25 başarım oranı elde edilmiştir. Bu çalışmada arıların sağlık durumlarının sınıflandırılması için derin öğrenme yöntemlerine dayalı hibrit bir yöntem önerilmiş ve başarılı sonuçlar elde edilmiştir.

Anahtar Kelimeler

Arı hastalıkları, derin öğrenme, evrişimsel sinir ağları, sınıflandırma

Kaynakça

• 1. Muz M.N., Demir N., Dilek M., Küresel Arı Sağlığı ve Veteriner Hekimlik, Veteriner Farmakoloji ve Toksikoloji Derneği Bülteni, 10 (1), 24–30, 2019.
• 2. Tekkaya C., Çapa Y., Yılmaz Ö., Biyoloji öğretmen adaylarının genel biyoloji konularındaki kavram yanılgıları, Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 18 (18), 2000.
• 3. Huckle J., British Bee Journal British Bee Publications, London, 1887.
• 4. D’Ascenzi C., Formato G., Martin P., Chemical hazards in honey, In: Chemical hazards in foods of animal origin, 642–647, Wageningen Academic Publishers 2019.
• 5. Richard F.J., Aubert A., Grozinger C.M., Modulation of social interactions by immune stimulation in honey bee, Apis mellifera, workers, BMC biology, 6 (1), 50, 2008.
• 6. Strauss U., Human H., Gauthier L., Crewe R.M., Dietemann V., Pirk C.W.W., Seasonal prevalence of pathogens and parasites in the savannah honeybee (Apis mellifera scutellata), Journal of Invertebrate Pathology, 114 (1), 45–52, 2013.
• 7. Larsen A., Reynaldi F.J., Guzmán-Novoa E., Fundaments of the honey bee (Apis mellifera) immune system. Review, Rev Mex Cienc Pecu, 10 (3), 705–728, 2019.
• 8. Forsgren E., Locke B., Sircoulomb F., Schäfer M.O., Bacterial diseases in honeybees, Current Clinical Microbiology Reports, 5 (1), 18–25, 2018.
• 9. Yost D.G., Tsourkas P., Amy P.S., Experimental bacteriophage treatment of honeybees (Apis mellifera) infected with Paenibacillus larvae, the causative agent of American foulbrood disease, Bacteriophage, 6 (1), 2016.
• 10. Pirsiavash H., Ramanan D., Detecting activities of daily living in first-person camera views, In: 2012 IEEE conference on computer vision and pattern recognition, 2847–2854, 2012.
• 11. Hammerla N.Y., Halloran S., Plötz T., Deep, convolutional, and recurrent models for human activity recognition using wearables, arXiv preprint arXiv:1604.08880, 2016.
• 12. Cho Y., Nam Y., Choi Y.-J., Cho W.-D., SmartBuckle: human activity recognition using a 3-axis accelerometer and a wearable camera, In: Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments, 1–3. 2008.
• 13. Fathi A., Farhadi A., Rehg J.M., Understanding egocentric activities, In: 2011 international conference on computer vision, 407–414, 2011.
• 14. Aktaş A., Doğan B., Demir Ö., Derin öğrenme yöntemleri ile dokunsal parke yüzeyi tespiti, Journal of the Faculty of Engineering & Architecture of Gazi University, 35 (3), 2020.
• 15. Kamnitsas K., Ledig C., Newcombe V.F.J., Simpson J.P., Kane A.D., Menon D.K., Rueckert D., Glocker B., Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation, Medical image analysis, 36 61–78, 2017.
• 16. Arı A., Hanbay D., Bölgesel evrişimsel sinir ağları tabanlı MR görüntülerinde tümör tespiti, Journal of the Faculty of Engineering & Architecture of Gazi University, 2018.
• 17. Sharma H., Zerbe N., Klempert I., Hellwich O., Hufnagl P., Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology, Computerized Medical Imaging and Graphics, 61, 2–13, 2017.
• 18. Zhao X., Wu Y., Song G., Li Z., Zhang Y., Fan Y., A deep learning model integrating FCNNs and CRFs for brain tumor segmentation, Medical image analysis, 43, 98–111, 2018.
• 19. Peldek S., Becerikli Y., GMACA ile hareket tespiti yapılan video görüntülerde insan hareketlerinin tanınması, Journal of the Faculty of Engineering & Architecture of Gazi University, 2018.
• 20. Cho G., Yim J., Choi Y., Ko J., Lee S.-H., Review of machine learning algorithms for diagnosing mental illness, Psychiatry investigation, 16 (4), 262, 2019.
• 21. Huang H., Zhou H., Yang X., Zhang L., Qi L., Zang A.-Y., Faster R-CNN for marine organisms detection and recognition using data augmentation, Neurocomputing, 3 (37), 372–384, 2019.
• 22. Yilmaz O., Erturk Y.E., Coskun F., Ertugrul M., Honey Bee Biology in Turkey, In: VII International Scientific Agriculture Symposium," Agrosym 2016", 6-9 October 2016, Jahorina, Bosnia and Herzegovina. Proceedings, 2413–2418, University of East Sarajevo, Faculty of Agriculture, 2016.
• 23. Bell C., Back Yard Hive, https://backyardhive.com/blogs/managing-your-top-bar-hive/bees-robbing-a-hive, Erişim Tarihi Mayıs 30, 2020.
• 24. Gillespie C., What Happens when a Queen Bee Dies?, https://sciencing.com/happens-queen-bee-dies-5159216.html, Erişim Tarihi Mayıs 30, 2020.
• 25. Calvo J.A., Causes and Effects of Losing a Queen Bee, https://www.osbeehives.com/blogs/ beekeeping-blog/queenless-statistics-for-the-summer-of-2018, Erişim Tarihi Mayıs 30, 2020.
• 26. Service T.A.I., Small Hive Beetle, https://txbeeinspection.tamu.edu/small-hive-beetle/, Erişim Tarihi Mayıs 30, 2020.
• 27. Uygur Ş.Ö., Bal Arılarının Beslenmesi ve Beslenmede Genel İlkeler, 2020.
• 28. Ramsey S.D., Ochoa R., Bauchan G., Gulbronson C., Mowery J.D., Cohen A., Lim D., Joklik J., Cicero J.M., Ellis J.D., Varroa destructor feeds primarily on honey bee fat body tissue and not hemolymph, Proceedings of the National Academy of Sciences, 116 (5), 1792–1801, 2019.
• 29. Hood W.M., The small hive beetle, Aethina tumida: a review, Bee world, 85 (3), 51–59, 2004.
• 30. Ellis J.D., Small hive beetle (Aethina tumida) contributions to colony losses, Honey bee colony health: Challenges and sustainable solutions, 2012.
• 31. Ellis J.D., Hepburn H.R., An ecological digest of the small hive beetle (Aethina tumida), a symbiont in honey bee colonies (Apis mellifera), Insectes sociaux, 53 (1), 8–19, 2006.
• 32. Lundie A.E., The small hive beetle, Aethina tumida, Science Bulletin Department of Agriculture and Forestry, Union of South Africa, (220), 1940.
• 33. Ritter W., Bee health and veterinarians, OIE (World Organisation for Animal Health), 2014.
• 34. Schmolke M.D., A study of Aethina tumida: the small hive beetle, Project Report, University of Rhodesia, 178, 1974.
• 35. Payne A.N., Shepherd T.F., Rangel J., The detection of honey bee (Apis mellifera)-associated viruses in ants, Scientific reports, 10 (1), 1–8, 2020.
• 36. Sledevič T., The application of convolutional neural network for pollen bearing bee classification, In: 2018 IEEE 6th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE), 1–4, 2018.
• 37. 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), 1–5, 2019.
• 38. König A., IndusBee 4.0–Integrated Intelligent Sensory Systems for Advanced Bee Hive Instrumentation and Hive Keepers’ Assistance Systems, Sensors & Transducers, 237 (9–10), 109–121, 2019.
• 39. Braga A.R., Gomes D.G., Rogers R., Hassler E.E., Freitas B.M., Cazier J.A., A method for mining combined data from in-hive sensors, weather and apiary inspections to forecast the health status of honey bee colonies, Computers and Electronics in Agriculture, 169 -181, 2020.
• 40. Rodriguez I.F.R., Automatic Video Monitoring of Honeybee Foraging Behavior Using Convolutional Neural Networks University of Puerto Rico, Rio Piedras (Puerto Rico), 2019.
• 41. Mohd-Isa W.-N., Nizam A., Ali A., Image Segmentation of Meliponine Bee using Faster R-CNN, In: 2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4), 235–238, 2019.
• 42. Lim S., Kim S., Park S., Kim D., Development of Application for Forest Insect Classification using CNN, In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), 1128–1131, 2018.
• 43. Bjerge K., Frigaard C.E., Mikkelsen P.H., Nielsen T.H., Misbih M., Kryger P., A computer vision system to monitor the infestation level of Varroa destructor in a honeybee colony, Computers and Electronics in Agriculture, 164-198, 2019.
• 44. Dandıl E., Polattimur R., Daha hızlı bölgesel evrişimsel sinir ağları ile köpek davranışlarının tanınması ve takibi, Journal of the Faculty of Engineering & Architecture of Gazi University, 35 (2), 2020.
• 45. Yang J., The BeeImage Dataset: Annotated Honey Bee Images, 2018.
• 46. Lv J.-J., Shao X.-H., Huang J.-S., Zhou X.-D., Zhou X., Data augmentation for face recognition, Neurocomputing, 230 184–196, 2017.
• 47. Öztemel E.,Yapay sinir ağları Papatya, 2012.
• 48. Emrah, Ş., Mohammed, A. S., Çelebi, F. V., New and improved search algorithms and precise analysis of their average case complexity, Future Generation Computer Systems, 95, 743–753, 2019.
• 49. Kilimci Z.H., Borsa tahmini için Derin Topluluk Modellleri (DTM) ile finansal duygu analizi, Journal of the Faculty of Engineering and Architecture of Gazi University, 35 (2), 635–650, 2020.
• 50. Hanbay K., Evrişimsel sinir ağı ve iki-boyutlu karmaşık gabor dönüşümü kullanılarak hiperspektral görüntü sınıflandırma, Journal of the Faculty of Engineering and Architecture of Gazi University, 35 (1), 443–456, 2020.
• 51. Grant-Jacob J.A., Xie Y., Mackay B.S., Praeger M., McDonnell M.D.T., Heath D.J., Loxham M., Eason R.W., Mills B., Particle and salinity sensing for the marine environment via deep learning using a Raspberry Pi, Environmental Research Communications, 1 (3), 2019.
• 52. Grossi A., Vianello E., Sabry M.M., Barlas M., Grenouillet L., Coignus J., Beigne E., Wu T., Le B.Q., Wootters M.K., Resistive RAM endurance: Array-level characterization and correction techniques targeting deep learning applications, IEEE Transactions on Electron Devices, 66 (3), 1281–1288, 2019.
• 53. Santos L., Santos F.N., Oliveira P.M., Shinde P., Deep Learning Applications in Agriculture: A Short Review, In: Iberian Robotics conference, 139–151, 2019.
• 54. Kuutti S., Fallah S., Bowden R., Barber P., Deep Learning for Autonomous Vehicle Control: Algorithms, State-of-the-Art, and Future Prospects, Synthesis Lectures on Advances in Automotive Technology, 3 (4), 1–80, 2019.
• 55. Kayaalp K., Süzen A.A., Derin Öğrenme ve Türkiye’deki Uygulamaları, Yayın Yeri: IKSAD International Publishing House, Basım sayısı, 1 2018.
• 56. Palani S., Liong S.-Y., Tkalich P., An ANN application for water quality forecasting, Marine pollution bulletin, 56 (9), 1586–1597, 2008.
• 57. Alsarraf J., Moayedi H., Rashid A.S.A., Muazu M.A., Shahsavar A., Application of PSO–ANN modelling for predicting the exergetic performance of a building integrated photovoltaic/thermal system, Engineering with Computers, 1–14, 2019.
• 58. Wan J., Li S., Modeling and application of industrial process fault detection based on pruning vine copula, Chemometrics and Intelligent Laboratory Systems, 184 1–13, 2019.
• 59. Buchanan B.G., Wilkins D.C., Readings in knowledge acquisition and learning: Automating the construction and improvement of expert systems Morgan Kaufmann Publishers Inc., 1993.
• 60. Michalski R.S., Toward a unified theory of learning: Multistrategy task-adaptive learning, 1993.
• 61. Fukunaga K., Introduction to statistical pattern recognition, Elsevier, 2013.
• 62. Koptur M.,Yapay Sinir Ağları ve Derin Öğrenme – 3, https://makineogrenimi.wordpress.com/2017/07/18/ yapay-sinir-aglari-ve-derin-ogrenme-3/, Erişim Tarihi Mayıs 30, 2020.
• 63. Emeksiz C., Doğan Z., Gökrem L., Yavuz A.H., Tokat bölgesi rüzgar karakteristiğinin istatistiksel yöntemler ile incelenmesi, Politeknik Dergisi, 19 (4), 481–489, 2016.
• 64. Metlek S., Özkan T., Analysis of Perceived Service Quality and Customer Satisfaction in the Aviation Sector with Artificial Neural Networks, In: Techno-Science, 2nd Internatioanl Conference on Technology and Science, 583–864, Burdur, Türkiye, 2019.
• 65. Alvear-Sandoval R.F., Figueiras-Vidal A.R., On building ensembles of stacked denoising auto-encoding classifiers and their further improvement, Information Fusion, 39 41–52, 2018.
• 66. Alzantot M., Chakraborty S., Srivastava M., Sensegen: A deep learning architecture for synthetic sensor data generation, In: 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 188–193, 2017.
• 67. Guan Y., Plötz T., Ensembles of deep lstm learners for activity recognition using wearables, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1 (2), 1–28, 2017.
• 68. Ordóñez F.J., Roggen D., Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition, Sensors, 16 (1), 115, 2016.
• 69. Ronao C.A., Cho S.-B., Human activity recognition with smartphone sensors using deep learning neural networks, Expert systems with applications, 59, 235–244, 2016.
• 70. Alsheikh M.A., Selim A., Niyato D., Doyle L., Lin S., Tan H.-P., Deep activity recognition models with triaxial accelerometers, In: Workshops at the Thirtieth AAAI Conference on Artificial Intelligence, 2016.
• 71. Erfani S.M., Rajasegarar S., Karunasekera S., Leckie C., High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning, Pattern Recognition, 58 121–134, 2016.