Development of Microbiology Plantation-Based Multimodal Segmentation for Smart Garden Using Machine Learning. (5th October 2022)

• Record Type: Journal Article
• Authors: Prasath, S.; Govindaraj, Ramkumar; Subbiah, Ram; Alharbi, Sulaiman Ali; Almoallim, Hesham S.; Priya, S.; Mulugeta, Begna Dejene
• Other Names: Raja K. Academic Editor.
• Is Part Of: Advances in materials science and engineering. Volume 2022(2022)
• Publication Date: 2022-10-05
• Subjects: Materials science -- Periodicals; Materials science; Periodicals; 620.11
• DOI: 10.1155/2022/1066535
• Languages: English
• ISSNs: 1687-8434

Abstract

Abstract : 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.