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Plant stem disease detection using machine learning approaches

The rapid identification of plant stem diseases is crucial for implementing timely intervention and minimizing crop loss. While previous research has primarily focused on leaf-based disease detection, this paper proposes an automated stem disease detection and classification model using digital imag...

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Main Authors: Md Akbar, Jalal Uddin, Syafiq Fauzi, Kamarulzaman, Tusher, Ekramul Haque
Format: Conference or Workshop Item
Language:English
English
Published: IEEE 2023
Subjects:
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/38202/1/Plant%20stem%20disease%20detection%20using%20machine%20learning%20approaches.pdf
http://umpir.ump.edu.my/id/eprint/38202/2/Plant%20stem%20disease%20detection%20using%20machine%20learning%20approaches_abst.pdf
http://umpir.ump.edu.my/id/eprint/38202/
https://doi.org/10.1109/ICCCNT56998.2023.10307074
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spelling my.ump.umpir.382022024-01-23T04:57:00Z http://umpir.ump.edu.my/id/eprint/38202/ Plant stem disease detection using machine learning approaches Md Akbar, Jalal Uddin Syafiq Fauzi, Kamarulzaman Tusher, Ekramul Haque QA76 Computer software The rapid identification of plant stem diseases is crucial for implementing timely intervention and minimizing crop loss. While previous research has primarily focused on leaf-based disease detection, this paper proposes an automated stem disease detection and classification model using digital image processing and machine learning techniques. A dataset comprising 3789 images of diseased and healthy stems, categorized into five classes (stem rot, gummy blight, blackleg, didymella, and healthy), was split into training (80%) and testing (20%) sets. Our experiments were conducted on multiple platforms, including Google Colab, Jupyter Notebook, and OpenCV, and compared the performance of four classification techniques: Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), and Impact Learning. Various performance metrics, such as accuracy, precision, recall, and F1 score, were employed to evaluate the classifiers. Our findings reveal that SVM outperformed the other classifiers, achieving an average accuracy of 87%, followed by Random Forest (79%), KNN (75%), and Impact Learning (70%). This research offers valuable insights for farmers and the agricultural industry, paving the way for future studies exploring disease detection in other plant parts using similar techniques. IEEE 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/38202/1/Plant%20stem%20disease%20detection%20using%20machine%20learning%20approaches.pdf pdf en http://umpir.ump.edu.my/id/eprint/38202/2/Plant%20stem%20disease%20detection%20using%20machine%20learning%20approaches_abst.pdf Md Akbar, Jalal Uddin and Syafiq Fauzi, Kamarulzaman and Tusher, Ekramul Haque (2023) Plant stem disease detection using machine learning approaches. In: 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023 , 6 - 8 July 2023 , Delhi, India. pp. 1-8.. ISBN 979-835033509-5 https://doi.org/10.1109/ICCCNT56998.2023.10307074
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA76 Computer software
spellingShingle QA76 Computer software
Md Akbar, Jalal Uddin
Syafiq Fauzi, Kamarulzaman
Tusher, Ekramul Haque
Plant stem disease detection using machine learning approaches
description The rapid identification of plant stem diseases is crucial for implementing timely intervention and minimizing crop loss. While previous research has primarily focused on leaf-based disease detection, this paper proposes an automated stem disease detection and classification model using digital image processing and machine learning techniques. A dataset comprising 3789 images of diseased and healthy stems, categorized into five classes (stem rot, gummy blight, blackleg, didymella, and healthy), was split into training (80%) and testing (20%) sets. Our experiments were conducted on multiple platforms, including Google Colab, Jupyter Notebook, and OpenCV, and compared the performance of four classification techniques: Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), and Impact Learning. Various performance metrics, such as accuracy, precision, recall, and F1 score, were employed to evaluate the classifiers. Our findings reveal that SVM outperformed the other classifiers, achieving an average accuracy of 87%, followed by Random Forest (79%), KNN (75%), and Impact Learning (70%). This research offers valuable insights for farmers and the agricultural industry, paving the way for future studies exploring disease detection in other plant parts using similar techniques.
format Conference or Workshop Item
author Md Akbar, Jalal Uddin
Syafiq Fauzi, Kamarulzaman
Tusher, Ekramul Haque
author_facet Md Akbar, Jalal Uddin
Syafiq Fauzi, Kamarulzaman
Tusher, Ekramul Haque
author_sort Md Akbar, Jalal Uddin
title Plant stem disease detection using machine learning approaches
title_short Plant stem disease detection using machine learning approaches
title_full Plant stem disease detection using machine learning approaches
title_fullStr Plant stem disease detection using machine learning approaches
title_full_unstemmed Plant stem disease detection using machine learning approaches
title_sort plant stem disease detection using machine learning approaches
publisher IEEE
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/38202/1/Plant%20stem%20disease%20detection%20using%20machine%20learning%20approaches.pdf
http://umpir.ump.edu.my/id/eprint/38202/2/Plant%20stem%20disease%20detection%20using%20machine%20learning%20approaches_abst.pdf
http://umpir.ump.edu.my/id/eprint/38202/
https://doi.org/10.1109/ICCCNT56998.2023.10307074
_version_ 1822924059839561728
score 13.234133

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