PSR-LeafNet: A Deep Learning Framework for Identifying Medicinal Plant Leaves Using Support Vector Machines
In computer vision, recognizing plant pictures has emerged as a multidisciplinary area of interest. In the last several years, much research has been conducted to determine the type of plant in each image automatically. The challenges in identifying the medicinal plants are due to the changes in the...
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MDPI AG
2024-12-01
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| author | Praveen Kumar Sekharamantry Marada Srinivasa Rao Yarramalle Srinivas Archana Uriti |
| author_facet | Praveen Kumar Sekharamantry Marada Srinivasa Rao Yarramalle Srinivas Archana Uriti |
| author_sort | Praveen Kumar Sekharamantry |
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| description | In computer vision, recognizing plant pictures has emerged as a multidisciplinary area of interest. In the last several years, much research has been conducted to determine the type of plant in each image automatically. The challenges in identifying the medicinal plants are due to the changes in the effects of image light, stance, and orientation. Further, it is difficult to identify the medicinal plants due to factors like variations in leaf shape with age and changing leaf color in response to varying weather conditions. The proposed work uses machine learning techniques and deep neural networks to choose appropriate leaf features to determine if the leaf is a medicinal or non-medicinal plant. This study presents a neural network design based on PSR-LeafNet (PSR-LN). PSR-LeafNet is a single network that combines the P-Net, S-Net, and R-Net, all intended for leaf feature extraction using the minimum redundancy maximum relevance (MRMR) approach. The PSR-LN helps obtain the shape features, color features, venation of the leaf, and textural features. A support vector machine (SVM) is applied to the output achieved from the PSR network, which helps classify the name of the plant. The model design is named PSR-LN-SVM. The advantage of the designed model is that it suits more considerable dataset processing and provides better results than traditional neural network models. The methodology utilized in the work achieves an accuracy of 97.12% for the MalayaKew dataset, 98.10% for the IMP dataset, and 95.88% for the Flavia dataset. The proposed models surpass all the existing models, having an improvement in accuracy. These outcomes demonstrate that the suggested method is successful in accurately recognizing the leaves of medicinal plants, paving the way for more advanced uses in plant taxonomy and medicine. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Big Data and Cognitive Computing |
| spelling | doaj-art-fd1a492976084315b32642e1983c30f12024-12-27T14:10:46ZengMDPI AGBig Data and Cognitive Computing2504-22892024-12-0181217610.3390/bdcc8120176PSR-LeafNet: A Deep Learning Framework for Identifying Medicinal Plant Leaves Using Support Vector MachinesPraveen Kumar Sekharamantry0Marada Srinivasa Rao1Yarramalle Srinivas2Archana Uriti3Department of Information Engineering and Computer Science, University of Trento, 38123 Trento, ItalyDepartment of Computer Science and Engineering, GITAM School of Technology, GITAM (Deemed to Be University), Visakhapatnam 530045, IndiaDepartment of Computer Science and Engineering, GITAM School of Technology, GITAM (Deemed to Be University), Visakhapatnam 530045, IndiaDepartment of Information Technology, GMR Institute of Technology, Rajam 532127, IndiaIn computer vision, recognizing plant pictures has emerged as a multidisciplinary area of interest. In the last several years, much research has been conducted to determine the type of plant in each image automatically. The challenges in identifying the medicinal plants are due to the changes in the effects of image light, stance, and orientation. Further, it is difficult to identify the medicinal plants due to factors like variations in leaf shape with age and changing leaf color in response to varying weather conditions. The proposed work uses machine learning techniques and deep neural networks to choose appropriate leaf features to determine if the leaf is a medicinal or non-medicinal plant. This study presents a neural network design based on PSR-LeafNet (PSR-LN). PSR-LeafNet is a single network that combines the P-Net, S-Net, and R-Net, all intended for leaf feature extraction using the minimum redundancy maximum relevance (MRMR) approach. The PSR-LN helps obtain the shape features, color features, venation of the leaf, and textural features. A support vector machine (SVM) is applied to the output achieved from the PSR network, which helps classify the name of the plant. The model design is named PSR-LN-SVM. The advantage of the designed model is that it suits more considerable dataset processing and provides better results than traditional neural network models. The methodology utilized in the work achieves an accuracy of 97.12% for the MalayaKew dataset, 98.10% for the IMP dataset, and 95.88% for the Flavia dataset. The proposed models surpass all the existing models, having an improvement in accuracy. These outcomes demonstrate that the suggested method is successful in accurately recognizing the leaves of medicinal plants, paving the way for more advanced uses in plant taxonomy and medicine.https://www.mdpi.com/2504-2289/8/12/176medicinal leaf plantsconvolutional neural networkgray-level co-occurrence matrixPSR-LeafNetsupport vector machine |
| spellingShingle | Praveen Kumar Sekharamantry Marada Srinivasa Rao Yarramalle Srinivas Archana Uriti PSR-LeafNet: A Deep Learning Framework for Identifying Medicinal Plant Leaves Using Support Vector Machines Big Data and Cognitive Computing medicinal leaf plants convolutional neural network gray-level co-occurrence matrix PSR-LeafNet support vector machine |
| title | PSR-LeafNet: A Deep Learning Framework for Identifying Medicinal Plant Leaves Using Support Vector Machines |
| title_full | PSR-LeafNet: A Deep Learning Framework for Identifying Medicinal Plant Leaves Using Support Vector Machines |
| title_fullStr | PSR-LeafNet: A Deep Learning Framework for Identifying Medicinal Plant Leaves Using Support Vector Machines |
| title_full_unstemmed | PSR-LeafNet: A Deep Learning Framework for Identifying Medicinal Plant Leaves Using Support Vector Machines |
| title_short | PSR-LeafNet: A Deep Learning Framework for Identifying Medicinal Plant Leaves Using Support Vector Machines |
| title_sort | psr leafnet a deep learning framework for identifying medicinal plant leaves using support vector machines |
| topic | medicinal leaf plants convolutional neural network gray-level co-occurrence matrix PSR-LeafNet support vector machine |
| url | https://www.mdpi.com/2504-2289/8/12/176 |
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