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MLP-Mixer Approach for Corn Leaf Diseases Classification

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Intelligent Information and Database Systems (ACIIDS 2022)

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

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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.

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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
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  2. Radius Tanone
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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

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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

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21966-5

  • Online ISBN: 978-3-031-21967-2

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