Skip to main content
SpringerLink
Log in

MLP-Mixer Approach for Corn Leaf Diseases Classification

  • Conference paper
  • First Online:
Intelligent Information and Database Systems (ACIIDS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13758))

Included in the following conference series:

Abstract

Corn is one of the staple foods in Indonesia. However, corn leaf disease poses a threat to corn farmers in increasing production. Farmers find it difficult to identify the type of corn leaf that is affected by the disease. Seeing the development of corn that continues to increase, prevention of common corn leaf disease needs to be prevented to increase production. By using an open dataset, the modern MLP-Mixer model is used to train the smaller size of datasets for further use in predicting the classification of diseases that attack corn leaves. This experiment uses an MLP-Mixer with a basic Multi-Layer Perceptron which is repeatedly applied in feature channels. This makes the MLP-Mixer model more resource efficient in carrying out the process to classify corn leaf disease. In this research, a well-designed method ranging from data preparation related to corn leaf disease images to pre-training and model evaluation is proposed. The performance of our model shows 98.09% of test accuracy. This result is certainly a new trend in image classification, so that it can be a solution in handling computer vision problems in general. Furthermore, the high precision achieved in this experiment can be applied to small devices such as smartphones, drones, or embedded systems. Based on the images obtained, these results can undoubtedly be a solution for corn farmers in recognizing the types of leaf diseases in order to achieve smart farming in Indonesia.

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. Rentschler, J., Salhab, M., Jafino, B.A.: Flood exposure and poverty in 188 countries. Nat. Commun. 13(1), 3527 (2022). https://doi.org/10.1038/S41467-022-30727-4

    Article  Google Scholar 

  2. Timmer, C.P.: The Corn economy of Indonesia, p. 302 (1987)

    Google Scholar 

  3. Kementerian Pertanian - Kementan Pastikan Produksi Jagung Nasional Surplus. https://www.pertanian.go.id/home/?show=news&act=view&id=3395. Accessed 13 Jan 2022

  4. Hamaisa, A., Estiasih, T., Putri, W.D.R., Fibrianto, K.: Physicochemical characteristics of jagung bose, an ethnic staple food from East Nusa Tenggara, Indonesia. J. Ethn. Foods 9(1), 24 (2022). https://doi.org/10.1186/S42779-022-00140-9

    Article  Google Scholar 

  5. Diseases of Corn | CALS. https://cals.cornell.edu/field-crops/corn/diseases-corn. Accessed 13 Jan 2022

  6. Waheed, A., Goyal, M., Gupta, D., Khanna, A., Hassanien, A.E., Pandey, H.M.: An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Comput. Electron. Agric. 175 (2020). https://doi.org/10.1016/j.compag.2020.105456

  7. Noola, D.A., Basavaraju, D.R.: Corn leaf image classification based on machine learning techniques for accurate leaf disease detection. Int. J. Electr. Comput. Eng. 12(3), 2509–2516 (2022). https://doi.org/10.11591/IJECE.V12I3.PP2509-2516

    Article  Google Scholar 

  8. Hein, L., et al.: The health impacts of Indonesian peatland fires. Environ. Heal. 21(1), 62 (2022). https://doi.org/10.1186/S12940-022-00872-W

    Article  Google Scholar 

  9. Salim, J.N., Trisnawarman, D., Imam, M.C.: Twitter users opinion classification of smart farming in Indonesia. IOP Conf. Ser. Mater. Sci. Eng. 852(1), 012165 (2020). https://doi.org/10.1088/1757-899X/852/1/012165

  10. Gunawan, F.E., et al.: Design and energy assessment of a new hybrid solar drying dome - Enabling Low-Cost, Independent and Smart Solar Dryer for Indonesia Agriculture 4.0. IOP Conf. Ser. Earth Environ. Sci. 998(1), 012052 (2022). https://doi.org/10.1088/1755-1315/998/1/012052

  11. Habeahan, N.L.S., Leba, S.M.R., Wahyuniar, W., Tarigan, D.B., Asaloei, S.I., Werang, B.R.: Online teaching in an Indonesian higher education institution: Student’s perspective. Int. J. Eval. Res. Educ. 11(2), 580–587 (2022). https://doi.org/10.11591/IJERE.V11I2.21824

    Article  Google Scholar 

  12. Internet - Our World in Data. https://ourworldindata.org/internet. Accessed 13 Jan 2022

  13. Tolstikhin, I., et al.: MLP-Mixer: An all-MLP Architecture for Vision.

    Google Scholar 

  14. Javanmardi, S., Miraei Ashtiani, S.H., Verbeek, F.J., Martynenko, A.: Computer-vision classification of corn seed varieties using deep convolutional neural network. J. Stored Prod. Res. 92, 101800 (2021). https://doi.org/10.1016/J.JSPR.2021.101800

  15. Misra, N.N., Dixit, Y., Al-Mallahi, A., Bhullar, M.S., Upadhyay, R., Martynenko, A.: IoT, big data and artificial intelligence in agriculture and food industry. IEEE Internet Things J. 9, 6305–6324 (2020). https://doi.org/10.1109/JIOT.2020.2998584

  16. Yu, H., et al.: Corn Leaf Diseases Diagnosis Based on K-Means Clustering and Deep Learning. IEEE Access 9, 143824–143835 (2021). https://doi.org/10.1109/ACCESS.2021.3120379

    Article  Google Scholar 

  17. Lakshmi, P., Mekala, K.R., Sai, V., Sree Modala, R., Devalla, V., Kompalli, A.B.: Leaf disease detection and remedy recommendation using CNN algorithm. Int. J. Online Biomed. Eng. 18(07), 85–100 (2022). https://doi.org/10.3991/IJOE.V18I07.30383

  18. Prashar, N., Sangal, A.L.: Plant Disease Detection Using Deep Learning (Convolutional Neural Networks). In: Chen, J.-Z., Tavares, J.M.R.S., Iliyasu, A.M., Du, K.-L. (eds.) ICIPCN 2021. LNNS, vol. 300, pp. 635–649. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-84760-9_54

    Chapter  Google Scholar 

  19. Cham, M.F.X., Tanone, R., Riadi, H.A.T.: Identification of rice leaf disease using convolutional neural network based on android mobile platform. 2021 2nd Int Conf. Innov. Creat. Inf. Technol. ICITech 2021, 140–144 (2021). https://doi.org/10.1109/ICITECH50181.2021.9590188

  20. Mahum, R., et al.: A novel framework for potato leaf disease detection using an efficient deep learning model. Hum. Ecol. Risk Assess. (2022). https://doi.org/10.1080/10807039.2022.2064814

    Article  Google Scholar 

  21. Araujo, A., Norris, W., Sim, J.: Computing Receptive Fields of Convolutional Neural Networks. Distill 4(11), e21 (2019). https://doi.org/10.23915/DISTILL.00021

  22. Luo, W., Li, Y., Urtasun, R., Zemel, R.: Understanding the Effective Receptive Field in Deep Convolutional Neural Networks.

    Google Scholar 

  23. What is Android | Android. https://www.android.com/what-is-android/. Accessed 17 Jan 2022

  24. TensorFlow Lite | ML for Mobile and Edge Devices. https://www.tensorflow.org/lite. Accessed 17 Jan 2022

  25. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  26. What is Artificial Intelligence (AI)? | IBM. https://www.ibm.com/cloud/learn/what-is-artificial-intelligence. Accessed 21 Apr 2022

  27. What is Machine Learning? | IBM. https://www.ibm.com/cloud/learn/machine-learning. Accessed 21 Apr 2022

  28. What is Deep Learning? | IBM. https://www.ibm.com/cloud/learn/deep-learning. Accessed 21 Apr 2022

  29. Bangladeshi Crops Disease Dataset | Kaggle. https://www.kaggle.com/datasets/nafishamoin/bangladeshi-crops-disease-dataset. Accessed 29 Mar 2022

  30. Sasaki, Y., Fellow, R.: The truth of the F-measure (2007)

    Google Scholar 

  31. Van Rijsbergen, C.J.: INFORMATION RETRIEVAL. Butterworth-Heinemann (1979)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Management, Chaoyang University of Technology, Taichung City, Taiwan

    Li-Hua Li & Radius Tanone

  2. Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Salatiga, Indonesia

    Radius Tanone

Authors
  1. Li-Hua Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Radius Tanone
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Radius Tanone .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Vietnam National University, Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Tien Khoa Tran

  3. Al-Farabi Kazakh National University, Almaty, Kazakhstan

    Ualsher Tukayev

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  6. University of Newcastle, Newcastle, NSW, Australia

    Edward Szczerbicki

Rights and permissions

Reprints and permissions

Copyright information

© 2022 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

Li, LH., Tanone, R. (2022). MLP-Mixer Approach for Corn Leaf Diseases Classification. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13758. Springer, Cham. https://doi.org/10.1007/978-3-031-21967-2_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-21967-2_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21966-5

  • Online ISBN: 978-3-031-21967-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation