Development of Microbiology Plantation-Based Multimodal Segmentation for Smart Garden Using Machine Learning
Normally, gardens lower the ambient temperature, which would improve air quality, absorb pollutants, and produce oxygen. Trees reduce soil erosion, increase fertility, and help retain soil moisture. Decomposed leaves that fall in the garden become nutrients for tree growth and help microbes to thriv...
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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: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/1066535 |
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| author | S. Prasath Ramkumar Govindaraj Ram Subbiah Sulaiman Ali Alharbi Hesham S. Almoallim S. Priya Begna Dejene Mulugeta |
| author_facet | S. Prasath Ramkumar Govindaraj Ram Subbiah Sulaiman Ali Alharbi Hesham S. Almoallim S. Priya Begna Dejene Mulugeta |
| author_sort | S. Prasath |
| collection | DOAJ |
| description | Normally, gardens lower the ambient temperature, which would improve air quality, absorb pollutants, and produce oxygen. Trees reduce soil erosion, increase fertility, and help retain soil moisture. Decomposed leaves that fall in the garden become nutrients for tree growth and help microbes to thrive. When it comes to growing trees in a garden, one should try and choose native trees that are naturally found in a particular area. These trees are well adapted to the environment and require less maintenance. Many insects and birds rely on native trees for food and shelter. Therefore, they are best for the environment. However, not all native trees are evergreen trees. Many evergreen trees can be planted in a small garden. In this paper, a microplantation-based model was developed to enhance the biological impacts for a smart garden. Based on the garden requirements, the smart system was constructed. On this basis, the seeds are planted in the soil. |
| format | Article |
| id | doaj-art-49d9a0cfb76643979c1739facea7f117 |
| institution | Kabale University |
| issn | 1687-8442 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-49d9a0cfb76643979c1739facea7f1172025-02-03T06:12:56ZengWileyAdvances in Materials Science and Engineering1687-84422022-01-01202210.1155/2022/1066535Development of Microbiology Plantation-Based Multimodal Segmentation for Smart Garden Using Machine LearningS. Prasath0Ramkumar Govindaraj1Ram Subbiah2Sulaiman Ali Alharbi3Hesham S. Almoallim4S. Priya5Begna Dejene Mulugeta6Department of MechatronicsDepartment of Electronics and Communication EngineeringDepartment of Mechanical EngineeringDepartment of Botany and MicrobiologyDepartment of Oral and Maxillofacial SurgeryDepartmet of Microbiology ImmunologyDepartment of ITNormally, gardens lower the ambient temperature, which would improve air quality, absorb pollutants, and produce oxygen. Trees reduce soil erosion, increase fertility, and help retain soil moisture. Decomposed leaves that fall in the garden become nutrients for tree growth and help microbes to thrive. When it comes to growing trees in a garden, one should try and choose native trees that are naturally found in a particular area. These trees are well adapted to the environment and require less maintenance. Many insects and birds rely on native trees for food and shelter. Therefore, they are best for the environment. However, not all native trees are evergreen trees. Many evergreen trees can be planted in a small garden. In this paper, a microplantation-based model was developed to enhance the biological impacts for a smart garden. Based on the garden requirements, the smart system was constructed. On this basis, the seeds are planted in the soil.http://dx.doi.org/10.1155/2022/1066535 |
| spellingShingle | S. Prasath Ramkumar Govindaraj Ram Subbiah Sulaiman Ali Alharbi Hesham S. Almoallim S. Priya Begna Dejene Mulugeta Development of Microbiology Plantation-Based Multimodal Segmentation for Smart Garden Using Machine Learning Advances in Materials Science and Engineering |
| title | Development of Microbiology Plantation-Based Multimodal Segmentation for Smart Garden Using Machine Learning |
| title_full | Development of Microbiology Plantation-Based Multimodal Segmentation for Smart Garden Using Machine Learning |
| title_fullStr | Development of Microbiology Plantation-Based Multimodal Segmentation for Smart Garden Using Machine Learning |
| title_full_unstemmed | Development of Microbiology Plantation-Based Multimodal Segmentation for Smart Garden Using Machine Learning |
| title_short | Development of Microbiology Plantation-Based Multimodal Segmentation for Smart Garden Using Machine Learning |
| title_sort | development of microbiology plantation based multimodal segmentation for smart garden using machine learning |
| url | http://dx.doi.org/10.1155/2022/1066535 |
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