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Identification of Potato Virus Y in Potato Plants Using Deep Learning and GradCAM Verification

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Intelligent Systems and Applications (IntelliSys 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 823))

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Abstract

Controlling potato plant diseases is crucial in agriculture because diseased plants can result in a significant loss in crop production. Farmers currently use manual visual inspections to classify potato plants that are diseased with the potato virus Y. This paper shows the feasibility of using RGB images to automate this process. A small dataset of potato plants pictures was acquired from an outdoor potato field and annotated with polygons to denote the location of the leaves. VGG16 and ResNet50, two convolutional neural networks, were trained to classify individual leaves. Additionally, experiments were conducted by changing the brightness of the images during training. This was done to counteract the different illumination levels present in our dataset. ResNet50’s best performing experimental setup achieved an accuracy of 0.77, while VGG16 achieved an accuracy of 0.70. In general, classifiers with brightness augmentations outperformed other setups. Examining the class activation mappings of shallow layers revealed a focus on either the margins and midrib or the overall texture of the leaf. This corresponds to the characteristics used by domain experts who concentrate on colour and mosaic patterns on the foliage.

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References

  1. Ahmad, A., Saraswat, D., El Gamal, A.: A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools. Smart Agric. Technol. 3, 100083 (2023)

    Article  Google Scholar 

  2. Alam, K., Rolfe, J.: Economics of plant disease outbreaks. Agenda: J. Policy Anal. Reform 133–146 (2006)

    Google Scholar 

  3. Alicioglu, G., Sun, B.: A survey of visual analytics for explainable artificial intelligence methods. Computers & Graphics, 102:502–520, 2 2022

    Google Scholar 

  4. Bauske, M.J., Robinson, A.P., and Gudmestad, N.C. Early Blight in Potato, NDSU Agriculture and Extension, 7 2018

    Google Scholar 

  5. Centraal Bureau voor de Statistiek (CBS). Ondernemers zien werkdruk toenemen als gevolg personeelstekort. 2022

    Google Scholar 

  6. Chattopadhay, A., Sarkar, A., Howlader, P., and Balasubramanian, V.N. Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 839–847, 2018

    Google Scholar 

  7. Couture, J.J., Singh, A., Charkowski, A.O., Groves, R.L., Gray, S.M., Bethke, P.C., Townsend, P.A.: Integrating spectroscopy with potato disease management. Plant Dis. 102(11), 2233–2240 (2018)

    Article  Google Scholar 

  8. L. Evans-Goldner. Potato Virus Y Strains, USDA - APHIS Animal and Plant Health Inspection Service, 2020

    Google Scholar 

  9. Deng, J. and W. Dong and R. Socher and L. J. Li and Li, K. and L. Fei-Fei. Imagenet: a large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE, 2009

    Google Scholar 

  10. Ferentinos, K.P.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018)

    Article  Google Scholar 

  11. Flood, J. The importance of plant health to food security. Food Security, 2(3), 215–231, 7 2010

    Google Scholar 

  12. Griffel, L.M., Delparte, D., Edwards, J.: Using support vector machines classification to differentiate spectral signatures of potato plants infected with potato virus y. Comput. Electron. Agric. 153, 318–324 (2018)

    Article  Google Scholar 

  13. Griffel, L.M., Delparte, D., Whitworth, J., Bodily, P., Hartley, D.: Evaluation of artificial neural network performance for classification of potato plants infected with potato virus y using spectral data on multiple varieties and genotypes. Smart Agric. Technol. 3, 100101 (2023)

    Article  Google Scholar 

  14. He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1026–1034 (2015)

    Google Scholar 

  15. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p. 6 (2016)

    Google Scholar 

  16. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269 (2017)

    Google Scholar 

  17. Hughes, D.P., Salathe, M.: An open access repository of images on plant health to enable the development of mobile disease diagnostics (2015)

    Google Scholar 

  18. Jiang, P.T., Zhang, C.B., Hou, Q., Cheng, M.M., Wei, Y.: Layercam: exploring hierarchical class activation maps for localization. IEEE Trans. Image Process. 30, 5875–5888 (2021)

    Article  Google Scholar 

  19. Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., NanoCode012, Kwon, Y., TaoXie, Michael, K., Fang, J., imyhxy, Lorna, Wong, C., Yifu, Z., Abhiram, Montes, D., Wang, Z., Fati, C., Nadar, J., Laughing, UnglvKitDe, tkianai, yxNONG, Skalski, P., Hogan, A., Strobel, M., Jain, M., Mammana, L., xylieong. ultralytics/yolov5: v6.2 - YOLOv5 Classification Models, Apple M1, Reproducibility, ClearML and Deci.ai integrations (2022)

    Google Scholar 

  20. Johnson, J., Sharma, G., Srinivasan, S., Masakapalli, S.K., Sharma, S., Sharma, J., Dua, V.K.: Enhanced field-based detection of potato blight in complex backgrounds using deep learning. Plant Phenomics (2021)

    Google Scholar 

  21. Jolliffe, I., Cadima, J.: Principal component analysis: a review and recent developments. Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. 374, 20150202, 04 (2016)

    Google Scholar 

  22. Khalifa, N.E.M., Taha, M.H.N., El-Maged, L.M.A., Hassanien, A.E.: Artificial intelligence in potato leaf disease classification: a deep learning approach. Stud. Big Data 63–79, 12 (2020)

    Google Scholar 

  23. Khan, A., Sohail A., Zahoora, U., Qureshi, A.S.: A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. 53(8), 5455–5516, 4 (2020)

    Google Scholar 

  24. Kinger, S., Kulkarni, V.: Explainable ai for deep learning based disease detection. In: 2021 Thirteenth International Conference on Contemporary Computing (IC3-2021), IC3 ’21, New York, NY, USA, 2021, pp. 209–216. Association for Computing Machinery (2021)

    Google Scholar 

  25. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.), 3rd International Conference on Learning Representations, ICLR 2015, an Diego, CA, USA, May 7–9, 2015, Conference Track Proceedings (2015)

    Google Scholar 

  26. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc. (2012)

    Google Scholar 

  27. Land-en Tuinbouworganisatie Nederland (LTO). Peiling werknemerstekort voor teelt-, productie- en oogstwerkzaamheden. Technical report, 2 2022

    Google Scholar 

  28. Lee, J.R., Kim, S., Park, I., Eo, T., Hwang, D.: Relevance-cam: your model already knows where to look. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14939–14948 (2021)

    Google Scholar 

  29. Lee, S.H., Goëau, H., Bonnet, P., Joly, A.: New perspectives on plant disease characterization based on deep learning. Comput. Electron. Agric. 170, 105220 (2020)

    Article  Google Scholar 

  30. Li, X., Zhou, Y., Liu, J., Wang, L., Zhang, J., Fan, X.: The detection method of potato foliage diseases in complex background based on instance segmentation and semantic segmentation. Front. Plant Sci. 13, 7 (2022)

    Google Scholar 

  31. Mahlein, A.K., Kuska, M.T., Behmann, J., Polder, G., Walter, A.: Hyperspectral sensors and imaging technologies in phytopathology: state of the art. Annu. Rev. Phytopathol. 56(1), 535–558, 8 (2018)

    Google Scholar 

  32. Mahum, R., Munir, H., Mughal, Z., Awais, M., Khan, F.S., Saqlain, M., Mahamad, S., Tlili, I.: A novel framework for potato leaf disease detection using an efficient deep learning model. Hum. Ecol. Risk Assess.: Int. J. 1–24, 4 (2022)

    Google Scholar 

  33. Martinelli, F., Scalenghe, R., Davino, S., Panno, S., Scuderi, G., Ruisi, P., Villa, P., Stroppiana, D., Boschetti, M., Goulart, L.R., Davis, C.E., Dandekar, A.M.: Advanced methods of plant disease detection. a review. Agron. Sustain. Dev. 35(1), 1–25, 9 (2014)

    Google Scholar 

  34. Micikevicius, P., Narang, S., Alben, J., Diamos, G., Elsen, E., Garcia, D., Ginsburg, B., Houston, M., Kuchaiev, O., Venkatesh, G., Wu, H.: Mixed precision training. In: International Conference on Learning Representations (2018)

    Google Scholar 

  35. Mishra, S., Singh, A., Singh, V.: Application of MobileNet-v1 for potato plant disease detection using transfer learning. In: 2021 Workshop on Algorithm and Big Data, p. 3 (2021)

    Google Scholar 

  36. Polder, G., Blok, P.M., De Villiers, H.A.C., Van der Wolf, J.M., Kamp, J.: Potato virus y detection in seed potatoes using deep learning on hyperspectral images. Front. Plant Sci. 10, 209 (2019)

    Article  Google Scholar 

  37. Rashid, J., Khan, I., Ali, G., Almotiri, S.H., AlGhamdi, M.A., Masood, K.: Multi-level deep learning model for potato leaf disease recognition. Electronics 10(17), 2064, 8 (2021)

    Google Scholar 

  38. Robinson, A., Secor, G., Pasche, J.: Late blight in potato, NDSU agriculture and extension, p. 5 (2022)

    Google Scholar 

  39. De Rossia, R.L., Guerraab, F.A., Plazasa, M.C., Vuleticab, E.E., Brücherab, E., Guerraa, G.D., Reisc, E.M.: Crop damage, economic losses, and the economic damage threshold for northern corn leaf blight 154, 105901 (2022)

    Google Scholar 

  40. Sandhu, G.K., Kaur, R.: Plant disease detection techniques: a review. In: 2019 International Conference on Automation, Computational and Technology Management (ICACTM), vol. 4 (2019)

    Google Scholar 

  41. Savary, S., Teng, P.S., Willocquet, L., Nutter, F.W., Jr.: Quantification and modeling of crop losses: a review of purposes 44(1), 89–112 (2006)

    Google Scholar 

  42. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision (2017)

    Google Scholar 

  43. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv:1409.1556

  44. Speith, T.: A review of taxonomies of explainable artificial intelligence (XAI) methods. In: 2022 ACM Conference on Fairness, Accountability, and Transparency, p. 6 (2022)

    Google Scholar 

  45. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016)

    Google Scholar 

  46. Thomas, S., Kuska, M.T., Bohnenkamp, D., Brugger, A., Alisaac, E., Wahabzada, M., Behmann, J., Mahlein, A.K.: Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective. J. Plant Dis. Prot. 125(1), 5–20, 9 (2017)

    Google Scholar 

  47. Chebet Too, E., Yujian, L., Njuki, S., Yingchun, L.: A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric. 161, 272–279 (2019). BigData and DSS in Agriculture

    Google Scholar 

  48. Valkonen, J.P.T.: Viruses: economical losses and biotechnological potential. In: Potato Biology and Biotechnology, pp. 619–641. Elsevier (2007)

    Google Scholar 

  49. van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(86), 2579–2605 (2008)

    Google Scholar 

  50. Vanhaute, E., Gráda, Ó, Paping, R.: The european subsistence crisis of 1845–1850. A comparative perspective. In: When the potato failed. Causes and effects of the ‘last’ European subsistance crisis, 1845–1850, pp. 15–42. Brepols (2007)

    Google Scholar 

  51. Wei, K., Chen, B., Zhang, J., Fan, S., Wu, K., Liu, G., Chen, D.: Explainable deep learning study for leaf disease classification. Agronomy 12(5) (2022)

    Google Scholar 

  52. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929 (2016)

    Google Scholar 

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Authors and Affiliations

  1. NHL Stenden University of Applied Sciences, Rengerslaan 8-10, 8917, DD Leeuwarden, The Netherlands

    Roy Voetman, Willem Dijkstra & Klaas Dijkstra

  2. Smart Agri Technology B.V., Groot Maarslag 10, 9961TB, Mensingeweer, The Netherlands

    Jeroen E. Wolters

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  1. Roy Voetman
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  2. Willem Dijkstra
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  4. Klaas Dijkstra
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Correspondence to Willem Dijkstra .

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  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Voetman, R., Dijkstra, W., Wolters, J.E., Dijkstra, K. (2024). Identification of Potato Virus Y in Potato Plants Using Deep Learning and GradCAM Verification. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-031-47724-9_16

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