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Classification and Detection of Cabbage Leaf Diseases from Images Using Deep Learning Methods

Received: 26 December 2022     Accepted: 30 January 2023     Published: 4 March 2023
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Abstract

The presented work uses Deep learning methods to detect diseases in cabbage leaves. The plant disease detection is constrained by human visual capabilities. Because, most of the early symptoms detected are microscopic. This process is tedious, time consuming and prediction is a challenging task. Hence, there is a need for developing a methodology that automatically recognizes, classifies and detects plant infection symptoms. Five major types of diseases namely Leaf miner, Diamond backmoth, Blackrot, Maggvots and Downy mildew are considered. Initially, the input images are classified into two types as healthy and diseased. Further, the diseased images are categorized into five different varieties. Around 3000 images of cabbage leaves are used containing healthy and infected leaves. Different phases namely preprocessing, feature extraction, training, testing and classification are used the proposed methodology. The accuracies of 93.5% and 90.5% are achieved for healthy and diseased leaf images. Classification accuracies for different types of diseased images are 89.9, 89.5, 91.8, 90.5 and 90.8 for Leaf miner, Diamond backmoth, Blackrot, Maggvots and Downy mildew respectively. The overall classification accuracy of 92% is attained. The developed methodology is found to provide good classification accuracy. The developed model finds its applications in APMCs, online purchase, Agricultural departments etc.

Published in Automation, Control and Intelligent Systems (Volume 11, Issue 1)
DOI 10.11648/j.acis.20231101.11
Page(s) 1-7
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Deep Learning, CNN, VGG-16, Image Preprocessing, Feature Extraction

References
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[2] Badiger, Manjunatha, Varuna Kumara, Sachin CN Shetty, and Sudhir Poojary. "Leaf and skin disease detection using image processing." Global Transitions Proceedings 3, no. 1 (2022): 272-278.
[3] Aiswarya, K., and N. R. Sreekumar. "Plant Disease Detection Using Quantum Image Processing." In 2022 International Conference on Industry 4.0 Technology (I4Tech), pp. 1-6. IEEE, 2022.
[4] Kulkarni, Pranesh, Atharva Karwande, Tejas Kolhe, Soham Kamble, Akshay Joshi, and Medha Wyawahare. "Plant disease detection using image processing and machine learning." arXiv preprint arXiv: 2106. 10698 (2021).
[5] Vishnoi, Vibhor Kumar, Krishan Kumar, and Brajesh Kumar. "Plant disease detection using computational intelligence and image processing." Journal of Plant Diseases and Protection 128, no. 1 (2021): 19-53.
[6] Sharma, Parul, Yash Paul Singh Berwal, and Wiqas Ghai. "Performance analysis of deep learning CNN models for disease detection in plants using image segmentation." Information Processing in Agriculture 7, no. 4 (2020): 566-574.
[7] Annabel, L. Sherly Puspha, T. Annapoorani, and P. Deepalakshmi. "Machine Learning for Plant Leaf Disease Detection and Classification–A Review." In 2019 International Conference on Communication and Signal Processing (ICCSP), pp. 0538-0542. IEEE, 2019.
[8] Türkoğlu, Muammer, and Davut Hanbay. "Plant disease and pest detection using deep learning-based features." Turkish Journal of Electrical Engineering and Computer Sciences 27, no. 3 (2019): 1636-1651.
[9] Monishanker Halder, Ananya Sarkar, Habibullah Bahar, “Plant Disease Detection by Image Processing: A Literature Review”, SDRP Journal of Food Science & Technology. Vol-3, Issue-6, pp. 534-538, February 2019.
[10] M. Deepan, P., and M. Akila. "Detection and Classification of Plant Leaf Diseases by using Deep Learning Algorithm." International Journal of Engineering Research & Technology (IJERT) (2018).
[11] Jayswal, Hardikkumar S., and Jitendra P. Chaudhari. "Plant Leaf Disease Detection and Classification using Conventional Machine Learning and Deep Learning." International Journal on Emerging Technologies 11 (3): 1094-1102 (2020).
[12] Sladojevic, Srdjan, Marko Arsenovic, Andras Anderla, Dubravko Culibrk, and Darko Stefanovic. "Deep neural networks based recognition of plant diseases by leaf image classification." Computational intelligence and neuroscience 2016.
[13] Prasanna Mohanty, Sharada, David Hughes, and Marcel Salathe. "Using Deep Learning for Image-Based Plant Disease Detection." arXiv e-prints arXiv-1604, 2016. https://www.frontiersin.org/articles/10.3389/fpls.2016.01419/full
[14] Alka Dixit, Erande Rani, LokhandeYogita, NighotRutuja, Kote S. V, “Plant Disease Detection”, International Journal on Emerging Technologies. Volume-9, Issue 03, pp. 66-70, February 2016.
[15] Mukesh Kumar Tripathi, Dhananjay D. Maktedar, Machine Learning Based Approaches for Disease Detection and Classification of Agricultural Products, 2016 International Conference on Computing Communication Control and automation (ICCUBEA), Pune, 2016, pp. 1-6, doi: 10.1109/ICCUBEA, 2016.
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  • APA Style

    Myna A. N., Manasvi K., Pavan J. K., Rakshith H. S., Yuktha D. Jain. (2023). Classification and Detection of Cabbage Leaf Diseases from Images Using Deep Learning Methods. Automation, Control and Intelligent Systems, 11(1), 1-7. https://doi.org/10.11648/j.acis.20231101.11

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    ACS Style

    Myna A. N.; Manasvi K.; Pavan J. K.; Rakshith H. S.; Yuktha D. Jain. Classification and Detection of Cabbage Leaf Diseases from Images Using Deep Learning Methods. Autom. Control Intell. Syst. 2023, 11(1), 1-7. doi: 10.11648/j.acis.20231101.11

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    AMA Style

    Myna A. N., Manasvi K., Pavan J. K., Rakshith H. S., Yuktha D. Jain. Classification and Detection of Cabbage Leaf Diseases from Images Using Deep Learning Methods. Autom Control Intell Syst. 2023;11(1):1-7. doi: 10.11648/j.acis.20231101.11

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  • @article{10.11648/j.acis.20231101.11,
      author = {Myna A. N. and Manasvi K. and Pavan J. K. and Rakshith H. S. and Yuktha D. Jain},
      title = {Classification and Detection of Cabbage Leaf Diseases from Images Using Deep Learning Methods},
      journal = {Automation, Control and Intelligent Systems},
      volume = {11},
      number = {1},
      pages = {1-7},
      doi = {10.11648/j.acis.20231101.11},
      url = {https://doi.org/10.11648/j.acis.20231101.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20231101.11},
      abstract = {The presented work uses Deep learning methods to detect diseases in cabbage leaves. The plant disease detection is constrained by human visual capabilities. Because, most of the early symptoms detected are microscopic. This process is tedious, time consuming and prediction is a challenging task. Hence, there is a need for developing a methodology that automatically recognizes, classifies and detects plant infection symptoms. Five major types of diseases namely Leaf miner, Diamond backmoth, Blackrot, Maggvots and Downy mildew are considered. Initially, the input images are classified into two types as healthy and diseased. Further, the diseased images are categorized into five different varieties. Around 3000 images of cabbage leaves are used containing healthy and infected leaves. Different phases namely preprocessing, feature extraction, training, testing and classification are used the proposed methodology. The accuracies of 93.5% and 90.5% are achieved for healthy and diseased leaf images. Classification accuracies for different types of diseased images are 89.9, 89.5, 91.8, 90.5 and 90.8 for Leaf miner, Diamond backmoth, Blackrot, Maggvots and Downy mildew respectively. The overall classification accuracy of 92% is attained. The developed methodology is found to provide good classification accuracy. The developed model finds its applications in APMCs, online purchase, Agricultural departments etc.},
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - Classification and Detection of Cabbage Leaf Diseases from Images Using Deep Learning Methods
    AU  - Myna A. N.
    AU  - Manasvi K.
    AU  - Pavan J. K.
    AU  - Rakshith H. S.
    AU  - Yuktha D. Jain
    Y1  - 2023/03/04
    PY  - 2023
    N1  - https://doi.org/10.11648/j.acis.20231101.11
    DO  - 10.11648/j.acis.20231101.11
    T2  - Automation, Control and Intelligent Systems
    JF  - Automation, Control and Intelligent Systems
    JO  - Automation, Control and Intelligent Systems
    SP  - 1
    EP  - 7
    PB  - Science Publishing Group
    SN  - 2328-5591
    UR  - https://doi.org/10.11648/j.acis.20231101.11
    AB  - The presented work uses Deep learning methods to detect diseases in cabbage leaves. The plant disease detection is constrained by human visual capabilities. Because, most of the early symptoms detected are microscopic. This process is tedious, time consuming and prediction is a challenging task. Hence, there is a need for developing a methodology that automatically recognizes, classifies and detects plant infection symptoms. Five major types of diseases namely Leaf miner, Diamond backmoth, Blackrot, Maggvots and Downy mildew are considered. Initially, the input images are classified into two types as healthy and diseased. Further, the diseased images are categorized into five different varieties. Around 3000 images of cabbage leaves are used containing healthy and infected leaves. Different phases namely preprocessing, feature extraction, training, testing and classification are used the proposed methodology. The accuracies of 93.5% and 90.5% are achieved for healthy and diseased leaf images. Classification accuracies for different types of diseased images are 89.9, 89.5, 91.8, 90.5 and 90.8 for Leaf miner, Diamond backmoth, Blackrot, Maggvots and Downy mildew respectively. The overall classification accuracy of 92% is attained. The developed methodology is found to provide good classification accuracy. The developed model finds its applications in APMCs, online purchase, Agricultural departments etc.
    VL  - 11
    IS  - 1
    ER  - 

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Author Information
  • Department of Computer Science and Engineering, Navkis College of Engineering, Hassan, India

  • Department of Computer Science and Engineering, Navkis College of Engineering, Hassan, India

  • Department of Computer Science and Engineering, Navkis College of Engineering, Hassan, India

  • Department of Computer Science and Engineering, Navkis College of Engineering, Hassan, India

  • Department of Computer Science and Engineering, Navkis College of Engineering, Hassan, India

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