The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach
This study proposes the machine learning based classification of medical plant leaves. The total six varieties of medicinal plant leaves-based dataset are collected from the Department of Agriculture, The Islamia University of Bahawalpur, Pakistan. These plants are commonly named in English as (herb...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/11/2/263 |
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| author | Samreen Naeem Aqib Ali Christophe Chesneau Muhammad H. Tahir Farrukh Jamal Rehan Ahmad Khan Sherwani Mahmood Ul Hassan |
| author_facet | Samreen Naeem Aqib Ali Christophe Chesneau Muhammad H. Tahir Farrukh Jamal Rehan Ahmad Khan Sherwani Mahmood Ul Hassan |
| author_sort | Samreen Naeem |
| collection | DOAJ |
| description | This study proposes the machine learning based classification of medical plant leaves. The total six varieties of medicinal plant leaves-based dataset are collected from the Department of Agriculture, The Islamia University of Bahawalpur, Pakistan. These plants are commonly named in English as (herbal) Tulsi, Peppermint, Bael, Lemon balm, Catnip, and Stevia and scientifically named in Latin as Ocimum sanctum, Mentha balsamea, Aegle marmelos, Melissa officinalis, Nepeta cataria, and Stevia rebaudiana, respectively. The multispectral and digital image dataset are collected via a computer vision laboratory setup. For the preprocessing step, we crop the region of the leaf and transform it into a gray level format. Secondly, we perform a seed intensity-based edge/line detection utilizing Sobel filter and draw five regions of observations. A total of 65 fused features dataset is extracted, being a combination of texture, run-length matrix, and multi-spectral features. For the feature optimization process, we employ a chi-square feature selection approach and select 14 optimized features. Finally, five machine learning classifiers named as a multi-layer perceptron, logit-boost, bagging, random forest, and simple logistic are deployed on an optimized medicinal plant leaves dataset, and it is observed that the multi-layer perceptron classifier shows a relatively promising accuracy of 99.01% as compared to the competition. The distinct classification accuracy by the multi-layer perceptron classifier on six medicinal plant leaves are 99.10% for Tulsi, 99.80% for Peppermint, 98.40% for Bael, 99.90% for Lemon balm, 98.40% for Catnip, and 99.20% for Stevia. |
| first_indexed | 2024-03-09T03:16:23Z |
| format | Article |
| id | doaj.art-d472c1377b19411a85f09bdbce939f5a |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T03:16:23Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-d472c1377b19411a85f09bdbce939f5a2023-12-03T15:22:13ZengMDPI AGAgronomy2073-43952021-01-0111226310.3390/agronomy11020263The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning ApproachSamreen Naeem0Aqib Ali1Christophe Chesneau2Muhammad H. Tahir3Farrukh Jamal4Rehan Ahmad Khan Sherwani5Mahmood Ul Hassan6Department of Computer Science & IT, Glim Institute of Modern Studies, Bahawalpur 63100, PakistanDepartment of Computer Science & IT, Glim Institute of Modern Studies, Bahawalpur 63100, PakistanDepartment of Mathematics, Université de Caen, LMNO, Campus II, Science 3, 14032 Caen, FranceDepartment of Statistics, The Islamia University of Bahawalpur, Bahawalpur 61300, PakistanDepartment of Statistics, The Islamia University of Bahawalpur, Bahawalpur 61300, PakistanCollege of Statistical and Actuarial Sciences, University of the Punjab, Lahore 54000, PakistanDepartment of Statistics, Stockholm University, SE-106 91 Stockholm, SwedenThis study proposes the machine learning based classification of medical plant leaves. The total six varieties of medicinal plant leaves-based dataset are collected from the Department of Agriculture, The Islamia University of Bahawalpur, Pakistan. These plants are commonly named in English as (herbal) Tulsi, Peppermint, Bael, Lemon balm, Catnip, and Stevia and scientifically named in Latin as Ocimum sanctum, Mentha balsamea, Aegle marmelos, Melissa officinalis, Nepeta cataria, and Stevia rebaudiana, respectively. The multispectral and digital image dataset are collected via a computer vision laboratory setup. For the preprocessing step, we crop the region of the leaf and transform it into a gray level format. Secondly, we perform a seed intensity-based edge/line detection utilizing Sobel filter and draw five regions of observations. A total of 65 fused features dataset is extracted, being a combination of texture, run-length matrix, and multi-spectral features. For the feature optimization process, we employ a chi-square feature selection approach and select 14 optimized features. Finally, five machine learning classifiers named as a multi-layer perceptron, logit-boost, bagging, random forest, and simple logistic are deployed on an optimized medicinal plant leaves dataset, and it is observed that the multi-layer perceptron classifier shows a relatively promising accuracy of 99.01% as compared to the competition. The distinct classification accuracy by the multi-layer perceptron classifier on six medicinal plant leaves are 99.10% for Tulsi, 99.80% for Peppermint, 98.40% for Bael, 99.90% for Lemon balm, 98.40% for Catnip, and 99.20% for Stevia.https://www.mdpi.com/2073-4395/11/2/263medicinal plant leavesmulti spectral featurestexture featuresclassificationmachine learningMulti-Layer Perceptron |
| spellingShingle | Samreen Naeem Aqib Ali Christophe Chesneau Muhammad H. Tahir Farrukh Jamal Rehan Ahmad Khan Sherwani Mahmood Ul Hassan The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach Agronomy medicinal plant leaves multi spectral features texture features classification machine learning Multi-Layer Perceptron |
| title | The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach |
| title_full | The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach |
| title_fullStr | The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach |
| title_full_unstemmed | The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach |
| title_short | The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach |
| title_sort | classification of medicinal plant leaves based on multispectral and texture feature using machine learning approach |
| topic | medicinal plant leaves multi spectral features texture features classification machine learning Multi-Layer Perceptron |
| url | https://www.mdpi.com/2073-4395/11/2/263 |
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