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Citrus disease detection and classification using end-to-end anchor-based deep learning model

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Abstract

Plant diseases are the primary issue that reduces agricultural yield and production, causing significant economic losses and instability in the food supply. In plants, citrus is a fruit crop of great economic importance, produced and typically grown in about 140 countries. However, citrus cultivation is widely affected by various factors, including pests and diseases, resulted in significant yield and quality losses. In recent years, computer vision and machine learning have been widely used in plant disease detection and classification, which present opportunities for early disease detection and bring improvements in the field of agriculture. Early and accurate detection of plant diseases is crucial to reducing the disease’s spread and damage to the crop. Therefore, this paper employs a two-stage deep CNN model for plant disease detection and citrus diseases classification using leaf images. The proposed model consists of two main stages; (a) proposing the potential target diseased areas using a region proposal network; (b) classification of the most likely target area to the corresponding disease class using a classifier. The proposed model delivers 94.37% accuracy in detection and an average precision of 95.8%. The findings demonstrate that the proposed model identifies and distinguishes between the three different citrus diseases, namely citrus black spot, citrus bacterial canker and Huanglongbing. The proposed model serves as a useful decision support tool for growers and farmers to recognize and classify citrus diseases.

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Notes

  1. https://www.kaggle.com/dtrilsbeek/citrus-leaves-prepared?select=citrus_leaves_prepared

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Authors and Affiliations

  1. School of Computer Science, Faculty of Engineering and IT (FEIT), University of Technology Sydney, Ultimo, Australia

    Sharifah Farhana Syed-Ab-Rahman & Mukesh Prasad

  2. School of Electrical and Data Engineering, Faculty of Engineering and IT (FEIT), University of Technology Sydney, Ultimo, Australia

    Mohammad Hesam Hesamian

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  1. Sharifah Farhana Syed-Ab-Rahman
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  2. Mohammad Hesam Hesamian
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  3. Mukesh Prasad
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Contributions

Conceptualization, SFSAR and MHH; methodology, SFSAR and MHH; software, SFSAR and MHH; validation, MHH; formal analysis SFSAR and MHH; investigation, SFSAR and MHH; resources, SFSAR and MHH; data curation, SFSAR and MHH; writing—original draft preparation, SFSAR; writing—review and editing SFSAR, MHH and MP; visualization, SFSAR and MHH; supervision, MP; project administration, SFSAR, MHH and MP; All authors have read and agreed to the published version of the manuscript.

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Correspondence to Sharifah Farhana Syed-Ab-Rahman.

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Syed-Ab-Rahman, S.F., Hesamian, M.H. & Prasad, M. Citrus disease detection and classification using end-to-end anchor-based deep learning model. Appl Intell 52, 927–938 (2022). https://doi.org/10.1007/s10489-021-02452-w

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