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Extreme learning machine for plant diseases classification: a sustainable approach for smart agriculture

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A Correction to this article was published on 26 November 2021

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Abstract

Nowadays, the economy of countries highly depends on the agriculture productivity which has a great effect on the development of human civilization. Sometimes, plant diseases cause a major reduction in agricultural products. This paper proposes a new approach for the automatic detection and classification of plant leaf diseases based on using the ELM deep learning algorithm on a real dataset of plant leaf images. The proposed approach uses the k-means clustering algorithm for image segmentation and applies the GLCM for feature extraction. The BDA optimization algorithm is employed for feature selection, and lastly the ELM algorithm is used for plant leaf diseases classification. The presented approach optimizes the input weights and hidden biases for ELM. The dataset used in this study includes 73 plant leaf images, such that we tested our approach on four diseases that usually affect plants, including: Alternaria alternata, Anthracnose, Bacterial blight, and Cercospora leaf spot. The experimental results show that the proposed approach has achieved encouraging results in terms of these classification measures: accuracy, error rate, recall, F score, and AUC which are 94%, 6%, 92%, 95%, and 96% respectively. Babu

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The data set used in the work will be available upon request

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Funding

This work was supported by AL Zaytoonah University of Jordan

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Authors and Affiliations

  1. Faculty of Science and IT, Al-Zaytoonah University of Jordan, Amman, Jordan

    Darah Aqel & Shadi Al-Zubi

  2. Department of Computer science and application, Faculty of prince Al-Hussien bin Abdullah || for IT, The Hashemite University, P.O. Box 330127, Zarqa, 13133, Jordan

    Ala Mughaid

  3. Computer Science Department, Jordan University of Science and Technology, Irbid, Jordan

    Yaser Jararweh

Authors
  1. Darah Aqel
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  2. Shadi Al-Zubi
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  3. Ala Mughaid
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  4. Yaser Jararweh
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All four Authors worked in an equivalent load at all stages to produce this research

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Correspondence to Darah Aqel or Shadi Al-Zubi.

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The original online version of this article was revised: The affiliation of the author Ala Mughaid has been corrected.

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Aqel, D., Al-Zubi, S., Mughaid, A. et al. Extreme learning machine for plant diseases classification: a sustainable approach for smart agriculture. Cluster Comput 25, 2007–2020 (2022). https://doi.org/10.1007/s10586-021-03397-y

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