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), 2024. Published by Science Publishing Group |
Deep Learning, CNN, VGG-16, Image Preprocessing, Feature Extraction
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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
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
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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Download@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}
}
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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