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Semantic segmentation of rusts and spots of wheat
I.V. Arinichev 1, S.V. Polyanskikh 2, I.V. Arinicheva 3

1 Kuban State University, 350040, Krasnodar, Russia, Stavropolskaya 149;
2 Plarium, 350059, Krasnodar, Russia, Uralskaya 75/1;
3 Kuban State Agrarian University named after I.T. Trubilin, 350044, Krasnodar, Russia, Kalinina 13

 PDF, 4947 kB

DOI: 10.18287/2412-6179-CO-1130

Pages: 118-125.

Full text of article: Russian language.

Abstract:
The paper explores the possibility of semantic segmentation of the yellow rust and wheat blotch classification using the U-Net convolutional neural network architecture. Based on an own dataset of 268 images, collected in natural conditions and in infectious nurseries of the Federal Research Center for Biological Plant Protection (VNII BZR), it is shown that the U-Net architecture with ResNet decoders is able to qualitatively detect, classify and localize rust and spotting even in cases where diseases are present on the plant at the same time. For individual classes of diseases, the main metrics (accuracy, micro-/macro precision, recall, and F1) range from 0.92 to 0.96. This indicates the possibility of recognizing even a few diseases on a leaf with an accuracy that is not inferior to that of a plant pathology expert. The IoU and Dice segmentation metrics are 0.71 and 0.88, respectively, which indicates a fairly high quality of pixel-by-pixel segmentation and is confirmed by visual analysis. The architecture of the neural network used in this case is quite lightweight, which makes it possible to use it on mobile devices without connecting to the network.

Keywords:
semantic segmentation, convolutional neural network, U-Net, wheat diseases, classification of diseases.

Citation:
Arinichev IV, Polyanskikh SV, Arinicheva IV. Semantic segmentation of rusts and spots of wheat. Computer Optics 2023; 47(1): 118-125. DOI: 10.18287/2412-6179-CO-1130.

Acknowledgements:
This work was supported by the Kuban science Foundation (Project No. IFR-20.1/121).

References:
  1. Matveeva IP, Volkova GV. Yellow rust of wheat. Expansion, harm, control measures (review). Vestnik of Ulyanovsk State Agricultural Academy 2019; 46(2): 102-116. DOI: 10.18286/1816-4501-2019-2-102-116.
  2. Boulent J, Foucher S, Theau J, St-Charles PL. Convolutional neural networks for the automatic identification of plant diseases. Front Plant Sci 2019; 10: 941. DOI: 10.3389/fpls.2019.00941.
  3. Ngugi LC, Abelwahab M, Abo-Zahhad M. Recent advances in image processing techniques for automated leaf pest and disease recognition – A review. Inf Process Agric 2021; 8(1): 27-51. DOI: 10.1016/j.inpa.2020.04.004.
  4. Saleem MH, Potgieter J, Arif KM. Plant disease detection and classification by deep learning. Plants 2019; 8(11): 468. DOI: 10.3390/plants8110468.
  5. Atabay H. Deep residual learning for tomato plant leaf disease identification. J Theor Appl Inf Technol 2017; 95(24): 6800-6808.
  6. Brahimi M, Boukhalfa K, Moussaoui A. Deep learning for tomato diseases: classification and symptoms visualization. Appl Artif Intell 2017; 31: 299-315. DOI: 10.1080/08839514.2017.1315516.
  7. Ferentinos KP. Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 2018; 145: 311-318. DOI: 10.1016/j.compag.2018.01.009.
  8. Mohanty SP, Hughes DP, Salathe M. Using deep learning for image-based plant disease detection. Front Plant Sci 2016; 7: 1419. DOI: 10.3389/fpls.2016.01419.
  9. Wang G, Sun Y., Wang J. Automatic image-based plant disease severity estimation using deep learning. Comput Intell Neurosci 2017; 2017: 2917536. DOI: 10.1155/2017/2917536.
  10. Arinichev IV, Polyanskikh SV, Volkova GV, Arinicheva IV. Rice fungal diseases recognition using modern computer vision techniques. Int J Fuzzy Log Intell Syst 2021; 21(1): 1-11. DOI: 10.5391/IJFIS.2021.21.1.1.
  11. Polyanskikh SV, Arinicheva IV, Arinichev IV, Volkova GV. Autoencoders for semantic segmentation of rice fungal diseases. Agron Res 2021, 19(2): 574-585. DOI: 10.15159/AR.21.019.
  12. Lin K, Gong L, Huang Y, Liu C, Pan J. Deep learning-based segmentation and quantification of cucumber powdery mildew using convolutional neural network. Front Plant Sci 2019; 10: 155. DOI: 10.3389/fpls.2019.00155.
  13. Zhang S, Wuc X, You Z, Zhang L. Leaf image based cucumber disease recognition using sparse representation classification. Comput Electron Agric 2017; 2017(134): 135-141. DOI: 10.1016/j.compag.2017.01.014.
  14. DeChant C, Wiesner-Hanks T, Stewart E, Gore M. Automated Identification of Northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology 2017; 107: 1426-1432. DOI: 10.1094/PHYTO-11-16-0417-R.
  15. Picon A, Alvarez-Gila A, Seitz M, Ortiz-Barredo A, Echazarra J, Johannes A. Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Comput Electron Agric 2018; 2018(161): 280-290. DOI: 10.1016/j.compag.2018.04.002.
  16. Chen S, Zhang K, Zhao Y, Sun Y, Ban W, Chen Y, Zhuang H, Zhang X, Liu J, Yang T. An approach for rice bacterial leaf streak disease segmentation and disease severity estimation. Agriculture 2021, 11: 420. DOI: 10.3390/agriculture11050420.
  17. Fuentes AF, Yoon S, Lee J, Park DS. High-performance deep neural network-based tomato plant diseases and pests diagnosis system with refinement filter bank. Front Plant Sci 2018; 29(9): 1162. DOI: 10.3389/fpls.2018.01162.
  18. Saleem R, Shah JH, Sharif M, Ansari GJ. Mango leaf disease identification using fully resolution convolutional network. Comput Mater Contin 2021; 69(3): 3581-3601. DOI: 10.32604/cmc.2021.017700.
  19. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. arXiv Preprint. 2015. Source: <https://arxiv.org/abs/1505.04597v1>.
  20. Berman M, Triki AR, Blaschko MB. The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. arXiv Preprint. 2017. Source: <https://arxiv.org/abs/1705.08790>.
  21. Bishop CM. Pattern recognition and machine learning. Cambridge: Springer; 2006. ISBN: 978-0-387-31073-2.

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