Identifying Species of Mushrooms Through Mushrooms Images by Convolutional Neural Networks with Transfer Learning Method

Öz

Identification of fungi by convolutional neural networks; It is very important for the protection of vital mushrooms and for making sustainable management plans of mushroom resources. It is also a problem solution that can be useful in detecting poisonous mushrooms and for mushroom growers and collectors to identify mushrooms. Mushroom hats have many distinctive features such as rash, stain, scale, sash, groove, unique pattern and color. For this reason, it is thought that hat images will be more successful in defining according to Lamella, Ring, body and Volva images. In addition, the available datasets are insufficient or disorganized to train convolutional neural networks. In order to prove these two theses and contribute to the studies, three new datasets containing the images of 472 classes belonging to 18 families were created. The irregular dataset contains 148318 images. the dataset of the edited Hat, Lamella, Ring, Body and Volva images has 97450 images. Mushroom hat images created by editing consist of 65419 images. In the study, 6 convolutional neural networks were trained using the transfer learning method. The accuracy of the most successful network trained with the regulated mushroom hat dataset is 97.62 %. Although the number of images of this dataset is 44% less than the first dataset and 32% the second dataset. the success rate is 26.53% better than the first dataset and 14.5% the second dataset.

Anahtar Kelimeler

• Identifying species of mushrooms
• Pre-Trained Networks
• Transfer Learning

Evrişimli Sinir Ağları ile Mantar Görüntülerinden Mantar Türlerinin Transfer Öğrenme Yöntemiyle Tanımlanması

Öz

Evrişimli sinir ağlarıyla mantarların tanımlanması; hayatî açıdan önemli mantarların koruma altına alınmasında ve mantar kaynaklarının sürdürülebilir yönetim planlarının yapılmasında oldukça önemlidir Ayrıca, zehirli mantarların tespit edilmesinde, mantar yetiştiricileri ile toplayıcıların mantarları tanımlamasında yararlı olabilecek bir problem çözümüdür. Mantar şapkaları döküntü, leke, pul, kuşak, yiv, özgün desen ve renk gibi çok sayıda ayırt edici özelliği sahiptirler. Bu nedenle şapka görüntülerinin Lamel, Yüzük, Sap ve Volva görüntülerine göre tanımlamaya daha çok katkısı olacağı düşünülmüştür. Bunun yanı sıra mevcut veri setleri evrişimli sinir ağları’nı eğitmek için yetersiz veya düzensizdir. Bu tezleri ispat etmek ve çalışmalara katkı sağlamak amacıyla, 18 aile ait 472 sınıfın görüntülerini içeren üç adet yeni veri seti oluşturulmuştur. Düzensiz veri seti 148318, düzenlenmiş Şapka, Lamel, Yüzük, Sap ve Volva görüntülerini içeren veri seti 97450 görüntü içermektedir. Düzenlenerek oluşturulmuş mantar şapka görüntüleri ise 65419 görüntüden oluşmaktadır. Çalışmada 6 evrişimli sinir ağı, transfer öğrenme yöntemi kullanılarak eğitilmiştir. Düzenlenmiş mantar şapka veri setiyle eğitilmiş en başarılı ağın doğruluk oranı %97.62’dir. Bu veri setinin görüntü sayısı, birinci veri setine göre %44, ikinci veri setine göre %32 daha az olmasına rağmen başarı oranı birinci veri setine göre %26.53, ikinci veri setine göre %14.5 daha iyidir.

Anahtar Kelimeler

• Mantar Türlerini Tanımlama
• Önceden Eğitilmiş Ağlar
• Transfer Öğrenme

Kaynakça

1. [1] Zhang, M., Cui, S. W., Cheung, P. C. K., Wang, Q. 2007. Antitumor polysaccharides from mushrooms: a review on their isolation process, structural characteristics and antitumor activity. Trends in Food Science & Technology, 18(1), 4-19.
2. [2] Cheung, P. C. K. 2013. Mini-review on edible mushrooms as source of dietary fiber: Preparation and health benefits. Food Science and Human Wellness, 2, 162–166.
3. [3] Feeney, M. J., Miller, A. M., Roupas, P. 2014. Mushrooms-Biologically Distinct and Nutritionally Unique: Exploring a Third Food Kingdom. Nutrition Today, 49(6), 301-307.
4. [4] Valentao, P., Andrade, P. B., Rangel, J., Ribeiro, B., Sılva, M. B., Baptista, P., Seabra, R. M. 2005. Effect of The Conservation Procedure on the Contents of Phenolic Compounds and Organic Acids in Chanterelle (Cantharellus cibarius) Mushroom. Journal of Agricultural and Food Chemistry, 53, 4925-4931.
5. [5] Mithril, C., Dragsted, L. O., Meyer, C., Tetens, I., Biltoft-Jensen, A., Astrup, A. 2013. Dietary composition and nutrient content of the New Nordic Diet. Public Health Nutrition, 16(5), 777–785.
6. [6] Kalac, P. 2013. A review of chemical composition and nutritional value of wildgrowing and cultivated mushrooms. Journal of the Science of Food and Agriculture, 93, 209–218.
7. [7] Athanasakis, G., Aligiannis, N., Zagou, Z. G., Skaltsounis, A. L., Fokialakis, N. 2013. Antioxidant properties of the wild edible mushroom Lactarius salmonicolor. Journal of Medicinal Food, 16(8), 760-764.
8. [8] Heleno, S. A., Barros, L., Sousa, M. J., Martins, A., Ferreira, I. C. F. R. 2009. Study and Characterization of Selected Nutrients in Wild Mushrooms from Portugal by Gas Chromatography and High Performance Liquid Chromatography. Microchemical Journal, 93, 195-199.

9. [9] Wang, Y., Hall, R. 2004. Edible ectomycorrhizal mushrooms: challenges and achievements. Canadian Journal Botany, 82(8), 1063-1073.
10. [10] Chang, S. T. 1999. World Production of Cultivated Edible and Medicinal Mushrooms in 1997 With Emphasis on Lentinus edodes (Berk.) Sing, in China. International Journal of Medicinal Mushrooms, 1, 291-300.
11. [11] Boa, E. 2004. Wild Edible Fungi a Global Overview of Their Use and Importance to People. Fao Press, Rome, 150s.
12. [12] Kalac, P. 2009. Chemical Composition and Nutritional Value of European Species of Wild Growing Mushrooms: A Review. Food Chemistry, 113, 9–16.
13. [13] Chang, S. T., Miles, P. G. 2004. Mushrooms: Cultivation, Nutritional Value, Medicinal Effect, and Environmental Impact. 2nd edition. CRC Press, Boca Raton, 431s.
14. [14] Food and Agriculture Organization of the United Nations. World Food and Agriculture-Statistical Pocketbook 2018. http://www.fao.org/3/CA1796EN/ca1796en.pdf (Erişim Tarihi: 11.09.2019).
15. [15] Zion Market Research. Global Mushroom Industry Size & Analysis Report, 2015-2021 by Market.https://www.zionmarketresearch.com/report/mushroom-market (Erişim Tarihi: 10.12.2019).
16. [16] Mushroom observer. https://mushroomobserver.org/ (Erişim Tarihi:12.12.2019).
17. [17] GitHub. 2018 FGVCx Fungi Classification Challenge.https://github.com/visipedia/fgvcx_fungi_comp (Erişim Tarihi:10.07.2019).
18. [18] Danmarks svampeatlas. https://svampe.databasen.org/ (Erişim Tarihi:10.07.2019).
19. [19] Kaggle. Mushrooms classification-Common genus's images. https://www.kaggle.com/maysee/mushrooms-classification-common-genuss-images (Erişim Tarihi: 21.07.2019).
20. [20] Goeau, H., Bonnet, P., Joly, A., Bakic, V., Barthélémy, D., Boujemaa, N., Molino, J. F. 2013. The imageclef 2013 plant identification task, 23-26 September, Valencia, Spain.
21. [21] Goeau, H., Joly, A., Bonnet, P., Selmi, S., Molino, J. F., Barth´el´emy, D., Boujemaa, N. 2014. Lifeclef plant identification task 2014. Working Notes for CLEF 2014 Conference, 15-18 September, Sheffield, United Kingdom, 598–615.
22. [22] Lowe, D. G. 1990. Object recognition from local scale-invariant features. The proceedings of the seventh IEEE international conference on Computer vision, 2, 1150–1157.
23. [23] Bay, H., Tuytelaars, T., Van, G. L. 2006. Surf: Speeded up robust features. 9th European Conference on Computer Vision, 7-13 May, Graz, Austria, 404–417.
24. [24] Ojala, T., Pietikäinen, M., Harwood, D. 1996. A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1), 51–59.
25. [25] Goëau, H., Bonnet, P., Joly, A. 2015. LifeCLEF plant identification task 2015. LifeCLEF 2015: Multimedia Life Species Identification Challenges, 8-11 September, Toulouse, France.
26. [26] Šulc, M., Mishkin, D., Matas, J. 2016. Very deep residual networks with maxout for plant identification in the wild. Conference Working notes of CLEF 2016, 5-8 September, Evora, Portugal.
27. [27] Sunderhauf, N., McCool, C., Upcroft, B., Tristan, P. 2014. Fine-grained plant classification using convolutional neural networks for feature extraction. Working notes of CLEF 2014 Conference, 15-18 September, Sheffield, United Kingdom, 756–762.
28. [28] Champ, J., Lorieul, T., Servajean, M., Joly, A. 2015. A comparative study of fine-grained classification methods in the context of the LifeCLEF plant identification challenge 2015. in CEUR Workshop Proceedings, 8-11 September, Toulouse, France, 1391.
29. [29] Goëau, H., Bonnet, P., Joly, A. 2017. Plant identification based on noisy web data: the amazing performance of deep learning. Working notes of CLEF 2017 Conference, 11-14 September, Dublin, Ireland.
30. [30] Masoudian, A., Kenneth, A. M. 2013. Application of support vector machine to detect microbial spoilage of mushrooms. 2013 International Conference on Computer and Robot Vision, 28-31 May, Regina, Canada, 281–287.
31. [31] Subramaniam, A., Oh, B. J. 2016 Mushroom Recognition Using PCA Algorithm. International Journal of Software Engineering and Its Applications, 10(1), 43-50.
32. [32] Kim, K. J., Jung, S. H., So, W. H., Sim, C. B. 2017. A Study on Mushroom Pest and Diseases Analysis System Implementation based on Convolutional Neural Networks for Smart Farm. International Journal of Control and Automation, 10(11), 61-72.
33. [33] Olpin, A. J. 2018. Convolutional Networks for Segmentation and Detection of Agricultural Mushrooms. MSc Thesis, University of Guelph, Ontario, Canada.
34. [34] Rahmat, R. F., Aruan, T., Purnamawati, S., Faza, S., Lini T. Z. 2018. Fungus image identification using K-Nearest Neighbor. IOP Conference Series Materials Science and Engineering, 19-20 July, Medan, Indonesia, 420(1).
35. [35] Wibowo, A., Rahayu, Y., Riyanto, A., Hidayatulloh, T. 2018. Classification Algorithm for Edible Mushroom Identification. 2018 International Conference on Information and Communications Technology, 6-7 March, Yogyakarta, Indonesia.
36. [36] Wulandari, M., Kusumaningtyas, E. M., Politeknik A. R. B. 2018. Identification of Poisonous Fungi Basidiomycota Macro Based on Mobile Device Using Neural Network. 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing, 29-30 October, Bali, Indonesia.
37. [37] Anil, A., Gupta, H., Arora, M. 2019. Computer vision based method for identification of freshness in mushrooms. 2019 International Conference on Issues and Challenges in Intelligent Computing Technique, 27-28 September, Ghazıabad, India.
38. [38] Maurya, P., Singh, N. P. 2020. Mushroom Classification Using Feature-Based Machine Learning Approach. Proceedings of 3rd International Conference on Computer Vision and Image Processing, 197-206.
39. [39] Gürkan, H., Hanilçi, A. 2020. Evrişimli sinir ağı ve QRS imgeleri kullanarak EKG tabanlı biyometrik tanıma yöntemi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 26(2), 318-327.
40. [40] Goodfellow, I., Bengio, Y., Courville, A. 2015. Deep Learning. The MIT Press, Cambridge, Massachusetts, United States, 848s.
41. [41] Indolia, S., Goswami, A. K., Mishra, S. P., Asopa, P. 2018. Conceptual Understanding of Convolutional Neural Network-A Deep Learning Approach. Procedia Computer Science, 132, 679-688.
42. [42] Nebauer, C. 1998. Evaluation of convolutional neural networks for visual recognition. IEEE Transactions on Neural Networks, 9(4), 685-696.
43. [43] Yamashita, R., Nishio, M., Do, R. K. G., Togashi, K. 2018. Convolutional neural networks: an overview and application in radiology. Insights Imaging, 9, 611–629.
44. [44] Lawrence, S., Giles, C. L., Tsoi, A. C., Back, A. D. 1997. Face recognition: A convolutional neural-network approach. IEEE Transactions on Neural Networks, 8(1), 98-113.
45. [45] MathWorks. Learn About Convolutional Neural Networks.https://www.mathworks.com/help/deeplearning/ug/introduction-to-convolutional-neural-networks.html (Erişim Tarihi: 26.11.2019).
46. [46] Baykal E, Doğan H, Ercin ME, Ersoz S, Ekinci M. Transfer learning with pre-trained deep convolutional neural networks for serous cell classification. Multimedia Tools and Applications, 1-19, 2019.
47. [47] Zhou, Y., Nejati, H., Do, T. T., Cheung, N. M., Cheah, L. 2016. Image-based vehicle analysis using deep neural network: A systematic study. IEEE international conference on digital signal processing, 276-280.
48. [48] He, K., Zhang, X., Ren, S., Sun, J. 2016. Deep Residual Learning for Image Recognition. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, United States, 770-778.
49. [49] Theckedath, D., Sedamkar, R. R. 2020. Detecting Affect States Using VGG16, ResNet50 and SE‑ResNet50 Networks. Springer Nature Computer Science, 79, 1-7.
50. [50] Ye, H., Han, H., Zhu, L., Duan, Q. 2019. Vegetable pest image recognition method based on improved VGG convolution neural network. Journal of Physics: Conference Series, 1237(3).
51. [51] Leo F. Isikdogan, https://www.isikdogan.com/ (Erişim Tarihi:12.12.2020).
52. [52] Nova Research Lab, https://medium.com/novaresearchlab/%C3%B6%C4%9Frenme-aktar%C4%B1m%C4%B1-transfer-learning-c0b8126965c4 (Erişim Tarihi:12.12.2019)
53. [53] Pan, S. J., Yang, Q. 2010. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359.
54. [54] Chollet, F. 2017. Deep Learning with Python. Manning Publications, New York, United States, 400s.
55. [55] Machine Learning Mastery. A Gentle Introduction to Transfer Learning for Deep Learning.https://machinelearningmastery.com/ transfer-learning-for-deep-learning/ (Erişim Tarihi: 10.12.2019).
56. [56] Martinez, J.B., Gill, G. 2019. Comparison of Pre-trained vs Domain-specific Convolutional Neural Networks for Classification of Interstitial Lung Disease, 2019 International Conference on Computational Science and Computational Intelligence (CSCI), 991-994, 5-7 December, Las Vegas, NV, USA.
57. [57] First Nature. Fungi Identification-Picture Galleries. https://www.first-nature.com/fungi/index1binom.php (Erişim Tarihi: 06.01.2020).
58. [58] Mushroom World. Mushrooms-Alphabetical list. http://www.mushroom.world/mushrooms/namelist (Erişim Tarihi:10.11.2019).
59. [59] Eyssartier, G., Roux, P. 2011. Le Guide Des Champignons. Belin, Paris, France, 1095s.
60. [60] Borgarino, D., Hurtado, C., Lagier, R. 2016. Le Guide Des Champignons. Edisud, France, 452.