Skip to main content
SpringerLink
Log in

Deep Learning for the Classification of Cassava Leaf Diseases in Unbalanced Field Data Set

  • Conference paper
  • First Online:
Advanced Network Technologies and Intelligent Computing (ANTIC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1798))

Abstract

Cassava is one of the main sources of carbohydrates in the world. However, the diagnosis of diseases in cassava crops is laborious, time-consuming and requires specialised personnel. In addition, very little research is available on images of cassava leaves taken with mobile phones and under field conditions. Therefore, the study designs deep learning models for the detection of diseases in cassava leaves from photos taken with mobile phones in the field. This study used a dataset of 21’397 images of cassava bacterial blight, cassava brown streak disease, cassava green mottle and cassava mosaic disease from a Kaggle competition. Twelve CNN models have been evaluated by applying transfer learning and data augmentation. Each of the models was trained with uniform samples and class-weighted samples. The results showed that the use of weighted samples reduced F1 score and accuracy in all cases. Furthermore, the DenseNet169 model was outstanding with an accuracy and F1 score of 74.77% and 0.59 respectively. Finally, the causes that hinder correct classification have been identified. The results reveal that it is still necessary to work on creating a balanced and refined database.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Olsen, K.M., Schaal, B.A.: Microsatellite variation in cassava (Manihot esculenta, Euphorbiaceae) and its wild relatives: further evidence for a southern Amazonian origin of domestication. Am. J. Botany 88(1), 131–42 (2001). https://doi.org/10.2307/2657133

  2. Gibbons, A.: New view of early amazonia: Recent findings suggest complex culture was indigenous to the Amazon basin-upsetting some received opinions about environment and culture. Science 248(4962), 1488–90 (1990). https://doi.org/10.1126/science.248.4962.1488

  3. Patiño, V.M.: Plantas cultivadas y animales domésticos en América Equinoccial, Imprenta Departamental (1963) (in Spanish)

    Google Scholar 

  4. Wanapat, M., Kang, S.: Cassava chip (Manihot esculenta Crantz) as an energy source for ruminant feeding. Animal Nutrition. 1(4), 266–270 (2015). https://doi.org/10.1016/j.aninu.2015.12.001

    Article  Google Scholar 

  5. Howeler, R., Lutaladio, N., Thomas, G.: Save and Grow: Cassava. A Guide to Sustainable Production Intensification. FAO (2013)

    Google Scholar 

  6. Nassar, N.M., Ortiz, R.: Cassava improvement: Challenges and impacts. J. Agricult. Sci. 145(2), 163–171 (2007). https://doi.org/10.1017/S0021859606006575

  7. Ekeleme, F., et al.: Increasing cassava root yield on farmers’ fields in Nigeria through appropriate weed management. Crop Protection 150, 105810 (2021). https://doi.org/10.1016/j.cropro.2021.105810

  8. Patil, B.L., Legg, J.P., Kanju, E., Fauquet, C.M.: Cassava brown streak disease: a threat to food security in Africa. J. Gen. Virol. 96(5), 956–68 (2015). https://doi.org/10.1099/jgv.0.000014

    Article  Google Scholar 

  9. Haggag, W.M., Saber, M., Abouziena, H.F., Hoballah, E.M., Zaghloul, A.M.: Climate change potential impacts on plant diseases and their management. Der Pharm. Lettre 8(5), 17–24 (2016)

    Google Scholar 

  10. Ranjan, M., Weginwar, M.R., Joshi, N., Ingole, A.B.: Detection and classification of leaf disease using artificial neural network. Int. J. Tech. Res. Appl. 3(3), 331–3 (2015)

    Google Scholar 

  11. Kusumo, B.S., Heryana, A., Mahendra, O., Pardede, H.F.: Machine learning-based for automatic detection of corn-plant diseases using image processing. In: 2018 International Conference on Computer, Control, Informatics and Its Applications (IC3INA), pp. 93–97 (2018). https://doi.org/10.1109/IC3INA.2018.8629507

  12. Barbedo, A., Garcia, J.: Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus 2(1), 1–12 (2013). https://doi.org/10.1186/2193-1801-2-660

  13. Sankaran, S., Mishra, A., Ehsani, R., Davis, C.: A review of advanced techniques for detecting plant diseases. Comput. Electron. Agricult. 72(1), 1–3 (2010). https://doi.org/10.1016/j.compag.2010.02.007

    Article  Google Scholar 

  14. Lu, J., Tan, L., Jiang, H.: Review on convolutional neural network (CNN) applied to plant leaf disease classification. Agriculture 11(8), 707 (2021). https://doi.org/10.3390/agriculture11080707

    Article  Google Scholar 

  15. Sharma, V.K.: Designing of face recognition system. In: 2019 International Conference on Intelligent Computing and Control Systems (ICCS), 15 May 2019, pp. 459–461. IEEE (2019)

    Google Scholar 

  16. Ngugi, L.C., Abelwahab, M., Abo-Zahhad, M.: Recent advances in image processing techniques for automated leaf pest and disease recognition-A review. Inf. Process. Agricult. 8(1), 27–51 (2021). https://doi.org/10.1016/j.inpa.2020.04.004

    Article  Google Scholar 

  17. Husin, Z.B., Shakaff, A.Y., Aziz, A.H., Farook, R.B.: Feasibility study on plant chili disease detection using image processing techniques. In: 2012 Third International Conference on Intelligent Systems Modelling and Simulation, pp. 291–296 (2012). https://doi.org/10.1109/ISMS.2012.33

  18. Kaur, S., Pandey, S., Goel, S.: Plants disease identification and classification through leaf images: A survey. Archiv. Comput. Methods Eng. 26(2), 507–530 (2018). https://doi.org/10.1007/s11831-018-9255-6

    Article  Google Scholar 

  19. Liu, J., Wang, X.: Plant diseases and pests detection based on deep learning: a review. Plant Methods 17(1), 1–8 (2021). https://doi.org/10.1186/s13007-021-00722-9

    Article  MathSciNet  Google Scholar 

  20. He, Y., Zhou, Z., Tian, L., Liu, Y., Luo, X.: Brown rice planthopper (Nilaparvata lugens Stal) detection based on deep learning. Precis. Agricult. 21(6), 1385–1402 (2020). https://doi.org/10.1007/s11119-020-09726-2

    Article  Google Scholar 

  21. Saleem, M.H., Potgieter, J., Arif, K.M.: Plant disease detection and classification by deep learning. Plants 8(11), 468 (2019). https://doi.org/10.3390/plants8110468

    Article  Google Scholar 

  22. Abade, A., Ferreira, P.A., de Barros, V.F.: Plant diseases recognition on images using convolutional neural networks: A systematic review. Comput. Electron. Agricult. 185, 106–125 (2021). https://doi.org/10.1016/j.compag.2021.106125

    Article  Google Scholar 

  23. Zhang, K., Wu, Q., Liu, A., Meng, X.: Can deep learning identify tomato leaf disease? Adv. Multim. (2018). https://doi.org/10.1155/2018/6710865

    Article  Google Scholar 

  24. Türkoğlu, M., Hanbay, D.: Plant disease and pest detection using deep learning-based features. Turkish J. Electric. Eng. Comput. Sci. 27(3): 1636–1651 (2019). https://doi.org/10.3906/elk-1809-181

  25. Ferentinos, K.P.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agricult. 145, 311–318 (2018). https://doi.org/10.1016/j.compag.2018.01.009

  26. Hassan, S.M., Maji, A.K.: Plant disease identification using a novel convolutional neural network. IEEE Access 7(10), 5390–401 (2022). https://doi.org/10.1109/ACCESS.2022.3141371

    Article  Google Scholar 

  27. Ye, Y., et al.: An improved efficientNetV2 model based on visual attention mechanism: Application to identification of cassava disease. Comput. Intell. Neurosci. 8(5) (2022). https://doi.org/10.1155/2022/1569911

  28. Ravi, V., Acharya, V., Pham, T.D.: Attention deep learning-based large-scale learning classifier for Cassava leaf disease classification. Exp. Syst. 39(2), e12862 (2022). https://doi.org/10.1111/exsy.12862

    Article  Google Scholar 

  29. Arivazhagan, S., Shebiah, R.N., Ananthi, S., Varthini, S.V.: Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agricult. Eng. Int.: CIGR J. 15(1), 211–7 (2013)

    Google Scholar 

  30. Thangaraj, R., Anandamurugan, S., Pandiyan, P., Kaliappan, V.K.: Artificial intelligence in tomato leaf disease detection: A comprehensive review and discussion. J. Plant Diseases Protect. 1–20 (2021). https://doi.org/10.1007/s41348-021-00500-8

  31. Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016). https://doi.org/10.3389/fpls.2016.01419

    Article  Google Scholar 

  32. Barbedo, J.G.: Factors influencing the use of deep learning for plant disease recognition. Biosyst. Eng. 172, 84–91 (2018). https://doi.org/10.1016/j.biosystemseng.2018.05.013

    Article  Google Scholar 

  33. Boulent, J., Foucher, S., Théau, J., St-Charles, P.L.: Convolutional neural networks for the automatic identification of plant diseases. Front. Plant Sci. 10, 941 (2019). https://doi.org/10.3389/fpls.2019.00941

    Article  Google Scholar 

  34. Barbedo, J.G.: A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst. Eng. 144, 52–60 (2016). https://doi.org/10.1016/j.biosystemseng.2016.01.017

    Article  Google Scholar 

  35. Shrivastava, S., Hooda, D.S.: Automatic brown spot and frog eye detection from the image captured in the field. Am. J. Intell. Syst. 4(4), 131–4 (2014). https://doi.org/10.5923/j.ajis.20140404.01

    Article  Google Scholar 

  36. Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., Hughes, D.P.: Deep learning for image-based cassava disease detection. Front. Plant Sci. 8, 1852 (2017). https://doi.org/10.3389/fpls.2017.01852

    Article  Google Scholar 

  37. Ramcharan, A., et al.: A mobile-based deep learning model for cassava disease diagnosis. Front. Plant Sci. 272 (2019). https://doi.org/10.3389/fpls.2019.00272

  38. Kaggle. Cassava leaf disease classification. identify the type of disease present on a cassava leaf image (2021). https://www.kaggle.com/353competitions/cassava-leaf-disease-classification

  39. Mwebaze, E., Gebru, T., Frome, A., Nsumba, S., Tusubira, J.: iCassava 2019 fine-grained visual categorization challenge. arXiv preprint arXiv:1908.02900 (2019).

  40. Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019). https://doi.org/10.1186/s40537-019-0197-0

  41. Weiss, K., Khoshgoftaar, T.M., Wang, D.D.: A survey of transfer learning. J. Big Data 3(1), 1–40 (2016). https://doi.org/10.1186/s40537-016-0043-6

    Article  Google Scholar 

  42. Smith, L.N.: Cyclical learning rates for training neural networks. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 464–472 (2017). https://doi.org/10.1109/WACV.2017.58

  43. Prechelt, L.: Automatic early stopping using cross validation: Quantifying the criteria. Neural Netw. 11(4), 761–7 (1998). https://doi.org/10.1016/S0893-6080(98)00010-0

    Article  Google Scholar 

  44. Atila, Ü., Uçar, M., Akyol, K., Uçar, E.: Plant leaf disease classification using EfficientNet deep learning model. Ecol. Inf. 61, 101182 (2021). https://doi.org/10.1016/j.ecoinf.2020.101182

    Article  Google Scholar 

  45. Tiwari, V., Joshi, R.C., Dutta, M.K.: Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images. Ecol. Inf. 63, 101289 (2021). https://doi.org/10.1016/j.ecoinf.2021.101289

    Article  Google Scholar 

Download references

Acknowledgment

Thanks to the Universidad Tecnológica del Perú for its support throughout the project. The authors declare no conflict of interest related to this work. Ernesto Paiva gave the idea, did the experiments, interpreted the results and wrote the paper.

Author information

Authors and Affiliations

  1. Universidad Tecnológica del Perú, Lima, Peru

    Ernesto Paiva-Peredo

Authors
  1. Ernesto Paiva-Peredo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ernesto Paiva-Peredo .

Editor information

Editors and Affiliations

  1. Ryerson University, Toronto, ON, Canada

    Isaac Woungang

  2. Netaji Subhas University of Technology, New Delhi, India

    Sanjay Kumar Dhurandher

  3. ABV-Indian Institute of Information Technology and Management, Gwalior, India

    Kiran Kumar Pattanaik

  4. Banaras Hindu University, Varanasi, India

    Anshul Verma

  5. Indian Institute of Technology, Patna, India

    Pradeepika Verma

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Paiva-Peredo, E. (2023). Deep Learning for the Classification of Cassava Leaf Diseases in Unbalanced Field Data Set. In: Woungang, I., Dhurandher, S.K., Pattanaik, K.K., Verma, A., Verma, P. (eds) Advanced Network Technologies and Intelligent Computing. ANTIC 2022. Communications in Computer and Information Science, vol 1798. Springer, Cham. https://doi.org/10.1007/978-3-031-28183-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-28183-9_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28182-2

  • Online ISBN: 978-3-031-28183-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation