Detection of Fungal Infections in Gloriosa Superba Plant Using the Convolution Neural Network Model
Herbal treatments’ efficacy, safety, and mild side effects are also high priorities in primary care. Furthermore, as the world’s population expands, food production becomes more difficult. We need to use innovative biotechnology-based fertilization technologies to boost food production output. Glori...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Food Quality |
| Online Access: | http://dx.doi.org/10.1155/2022/7413983 |
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| _version_ | 1832551167725404160 |
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| author | Guillermo Napoleón Pelaez-Diaz Rosa Vílchez-Vásquez Antonio Huaman-Osorio R. Mahaveerakannan S. Pushpa Nilesh Shelke Sumitha Jagadibabu Jenifer Mahilraj |
| author_facet | Guillermo Napoleón Pelaez-Diaz Rosa Vílchez-Vásquez Antonio Huaman-Osorio R. Mahaveerakannan S. Pushpa Nilesh Shelke Sumitha Jagadibabu Jenifer Mahilraj |
| author_sort | Guillermo Napoleón Pelaez-Diaz |
| collection | DOAJ |
| description | Herbal treatments’ efficacy, safety, and mild side effects are also high priorities in primary care. Furthermore, as the world’s population expands, food production becomes more difficult. We need to use innovative biotechnology-based fertilization technologies to boost food production output. Gloriosa superba is one of the most well-known plants for its antibacterial and medicinal capabilities. The money plant is also known as the Gloriosa superba. We used a deep learning-based convolution neural network (CNN) classifier model to optimize the CNN algorithm parameter for better prediction. The enhanced particle swarm optimization (PSO) technique was used for optimization. Scale-invariant feature transform (SIFT) was used to extract the fungal spotted area. Digital camera with a high resolution acquires 300 dataset photographs from different villages in India for this investigation. Using a real-time fungal-affected image to train and test the model, different parametric measures are used to assess the model’s performance. The categorization accuracy obtained in this experiment was 99.32 percent. |
| format | Article |
| id | doaj-art-1b2cf79ee06848619a0955876fee8259 |
| institution | Kabale University |
| issn | 1745-4557 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Food Quality |
| spelling | doaj-art-1b2cf79ee06848619a0955876fee82592025-02-03T06:04:48ZengWileyJournal of Food Quality1745-45572022-01-01202210.1155/2022/7413983Detection of Fungal Infections in Gloriosa Superba Plant Using the Convolution Neural Network ModelGuillermo Napoleón Pelaez-Diaz0Rosa Vílchez-Vásquez1Antonio Huaman-Osorio2R. Mahaveerakannan3S. Pushpa4Nilesh Shelke5Sumitha Jagadibabu6Jenifer Mahilraj7Universidad Nacional Santiago Antúnez de MayoloUniversidad Nacional Santiago Antúnez de MayoloUniversidad Nacional Santiago Antúnez de MayoloDepartment of Computer Science and EngineeringSt. Peter’s Institute of Higher Education and ResearchDepartment of CSEDepartment of MicrobiologyDepartment of Computer Science and EngineeringHerbal treatments’ efficacy, safety, and mild side effects are also high priorities in primary care. Furthermore, as the world’s population expands, food production becomes more difficult. We need to use innovative biotechnology-based fertilization technologies to boost food production output. Gloriosa superba is one of the most well-known plants for its antibacterial and medicinal capabilities. The money plant is also known as the Gloriosa superba. We used a deep learning-based convolution neural network (CNN) classifier model to optimize the CNN algorithm parameter for better prediction. The enhanced particle swarm optimization (PSO) technique was used for optimization. Scale-invariant feature transform (SIFT) was used to extract the fungal spotted area. Digital camera with a high resolution acquires 300 dataset photographs from different villages in India for this investigation. Using a real-time fungal-affected image to train and test the model, different parametric measures are used to assess the model’s performance. The categorization accuracy obtained in this experiment was 99.32 percent.http://dx.doi.org/10.1155/2022/7413983 |
| spellingShingle | Guillermo Napoleón Pelaez-Diaz Rosa Vílchez-Vásquez Antonio Huaman-Osorio R. Mahaveerakannan S. Pushpa Nilesh Shelke Sumitha Jagadibabu Jenifer Mahilraj Detection of Fungal Infections in Gloriosa Superba Plant Using the Convolution Neural Network Model Journal of Food Quality |
| title | Detection of Fungal Infections in Gloriosa Superba Plant Using the Convolution Neural Network Model |
| title_full | Detection of Fungal Infections in Gloriosa Superba Plant Using the Convolution Neural Network Model |
| title_fullStr | Detection of Fungal Infections in Gloriosa Superba Plant Using the Convolution Neural Network Model |
| title_full_unstemmed | Detection of Fungal Infections in Gloriosa Superba Plant Using the Convolution Neural Network Model |
| title_short | Detection of Fungal Infections in Gloriosa Superba Plant Using the Convolution Neural Network Model |
| title_sort | detection of fungal infections in gloriosa superba plant using the convolution neural network model |
| url | http://dx.doi.org/10.1155/2022/7413983 |
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