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...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/38202/ 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 |
| _version_ | 1848825448066187264 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:29:05Z |
| format | Conference or Workshop Item |
| id | ump-38202 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:29:05Z |
| publishDate | 2023 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 (Published) https://doi.org/10.1109/ICCCNT56998.2023.10307074 |
| spellingShingle | QA76 Computer software Md Akbar, Jalal Uddin Syafiq Fauzi, Kamarulzaman Tusher, Ekramul Haque Plant stem disease detection using machine learning approaches |
| title | 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_short | Plant stem disease detection using machine learning approaches |
| title_sort | plant stem disease detection using machine learning approaches |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/38202/ http://umpir.ump.edu.my/id/eprint/38202/ 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 |