Machine learning approach for classification of mangifera indica leaves using digital image analysis
There is a wide range of horticulture farming in Asia. Mangifera Indica belongs to the species of flowering plant, also publicly recognized as mango. It has a significant local demand as well as a broad export marketplace throughout the world, and is considered as ‘King of Fruits.’ There are many ma...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | International Journal of Food Properties |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10942912.2022.2117822 |
| _version_ | 1828112948734722048 |
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| author | Tanveer Aslam Salman Qadri Syed Furqan Qadri Syed Ali Nawaz Abdul Razzaq Syeda Shumaila Zarren Mubashir Ahmad Muzammil Ur Rehman Amir Hussain Israr Hussain Javeria Jabeen Adnan Altaf |
| author_facet | Tanveer Aslam Salman Qadri Syed Furqan Qadri Syed Ali Nawaz Abdul Razzaq Syeda Shumaila Zarren Mubashir Ahmad Muzammil Ur Rehman Amir Hussain Israr Hussain Javeria Jabeen Adnan Altaf |
| author_sort | Tanveer Aslam |
| collection | DOAJ |
| description | There is a wide range of horticulture farming in Asia. Mangifera Indica belongs to the species of flowering plant, also publicly recognized as mango. It has a significant local demand as well as a broad export marketplace throughout the world, and is considered as ‘King of Fruits.’ There are many mango varieties and each has its own business market. Efficient identification of the mango varieties is still difficult because of untrained growers and obsolete farming culture, especially in remote areas of the Asia. The primary purpose of this research study was to discriminate mango varieties with the potential of machine learning techniques by analyzing their leaves. For the purpose, we selected leaves of eight mango varieties, namely: Anwar-Ratul (AR), Chaunsa (CHAUN), Langra (LANG), Sindhri (SIND), Saroli (SARO), Fajri (FAJ), Desi (DESI), Alo-Marghan (ALM). A digital cell phone camera captured these datasets in open atmosphere without any well-equipped lab and infrastructure. Binary, histogram, RST, spectral, and texture features were employed for machine learning (ML)-based mango leaf image discrimination. A k-fold (k = 10) cross-validation method was used for ML classification. The k nearest neighbors (KNN) classifier achieved maximum overall classification accuracy (OCA) from 88.33% to 97%. |
| first_indexed | 2024-04-11T11:58:38Z |
| format | Article |
| id | doaj.art-836834d09fdc45148d6cb29864e86a81 |
| institution | Directory Open Access Journal |
| issn | 1094-2912 1532-2386 |
| language | English |
| last_indexed | 2024-04-11T11:58:38Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Food Properties |
| spelling | doaj.art-836834d09fdc45148d6cb29864e86a812022-12-22T04:25:02ZengTaylor & Francis GroupInternational Journal of Food Properties1094-29121532-23862022-12-012511987199910.1080/10942912.2022.2117822Machine learning approach for classification of mangifera indica leaves using digital image analysisTanveer Aslam0Salman Qadri1Syed Furqan Qadri2Syed Ali Nawaz3Abdul Razzaq4Syeda Shumaila Zarren5Mubashir Ahmad6Muzammil Ur Rehman7Amir Hussain8Israr Hussain9Javeria Jabeen10Adnan Altaf11Computer Science Department, Muhammad Nawaz Shareef University of Agriculture Multan, Multan, PakistanComputer Science Department, Muhammad Nawaz Shareef University of Agriculture Multan, Multan, PakistanComputer Science and Software Engineering, Shenzhen University, Shenzhen, GD, ChinaInformation Technology, the Islamia University of Bahawalpur Pakistan, Bahawalpur, PakistanComputer Science Department, Muhammad Nawaz Shareef University of Agriculture Multan, Multan, PakistanComputer Science and Software engineering, Beijing University of Technology, Beijing, HB, ChinaComputer Science & IT, The University of Lahore - City Campus, Lahore, PakistanInformation Technology, the Islamia University of Bahawalpur Pakistan, Bahawalpur, PakistanComputer Science Department, Muhammad Nawaz Shareef University of Agriculture Multan, Multan, PakistanComputer Science Department, Muhammad Nawaz Shareef University of Agriculture Multan, Multan, PakistanComputer Science Department, Muhammad Nawaz Shareef University of Agriculture Multan, Multan, PakistanComputer Science Department, Muhammad Nawaz Shareef University of Agriculture Multan, Multan, PakistanThere is a wide range of horticulture farming in Asia. Mangifera Indica belongs to the species of flowering plant, also publicly recognized as mango. It has a significant local demand as well as a broad export marketplace throughout the world, and is considered as ‘King of Fruits.’ There are many mango varieties and each has its own business market. Efficient identification of the mango varieties is still difficult because of untrained growers and obsolete farming culture, especially in remote areas of the Asia. The primary purpose of this research study was to discriminate mango varieties with the potential of machine learning techniques by analyzing their leaves. For the purpose, we selected leaves of eight mango varieties, namely: Anwar-Ratul (AR), Chaunsa (CHAUN), Langra (LANG), Sindhri (SIND), Saroli (SARO), Fajri (FAJ), Desi (DESI), Alo-Marghan (ALM). A digital cell phone camera captured these datasets in open atmosphere without any well-equipped lab and infrastructure. Binary, histogram, RST, spectral, and texture features were employed for machine learning (ML)-based mango leaf image discrimination. A k-fold (k = 10) cross-validation method was used for ML classification. The k nearest neighbors (KNN) classifier achieved maximum overall classification accuracy (OCA) from 88.33% to 97%.https://www.tandfonline.com/doi/10.1080/10942912.2022.2117822Machine learningMango leavesTexture featuresclassification |
| spellingShingle | Tanveer Aslam Salman Qadri Syed Furqan Qadri Syed Ali Nawaz Abdul Razzaq Syeda Shumaila Zarren Mubashir Ahmad Muzammil Ur Rehman Amir Hussain Israr Hussain Javeria Jabeen Adnan Altaf Machine learning approach for classification of mangifera indica leaves using digital image analysis International Journal of Food Properties Machine learning Mango leaves Texture features classification |
| title | Machine learning approach for classification of mangifera indica leaves using digital image analysis |
| title_full | Machine learning approach for classification of mangifera indica leaves using digital image analysis |
| title_fullStr | Machine learning approach for classification of mangifera indica leaves using digital image analysis |
| title_full_unstemmed | Machine learning approach for classification of mangifera indica leaves using digital image analysis |
| title_short | Machine learning approach for classification of mangifera indica leaves using digital image analysis |
| title_sort | machine learning approach for classification of mangifera indica leaves using digital image analysis |
| topic | Machine learning Mango leaves Texture features classification |
| url | https://www.tandfonline.com/doi/10.1080/10942912.2022.2117822 |
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