Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/555684
Full metadata record
DC Field | Value | Language |
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dc.coverage.spatial | ||
dc.date.accessioned | 2024-03-29T12:01:34Z | - |
dc.date.available | 2024-03-29T12:01:34Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/555684 | - |
dc.description.abstract | Agriculture is an essential component in the expansion of the human population. The contribution that agriculture makes to the globe is significant, and agriculture in and of itself relies entirely on the need for land to be irrigated. A significant study has also been devoted to understand how these activities alter soil quality and whether or not they are mitigated. Motivation behind the health and productivity analysis in smart agriculture is to improve the efficiency, sustainability, and profitability of agricultural operations. newlineIn the first stage, to consider a wide range of physical, chemical, and biological characteristics of soil as indications for its overall health. Methods like Bagging, AdaBoosting, and XGBoost are used to create prediction models that are taken into consideration for an extensive variety of variables and their relationship to one another. These ensemble methods have the potential to improve prediction and accuracy through combining the best features of various individual models. This study makes use of a dataset gathered from the active agricultural area in the Karuppur village. The results indicate that bagging, AdaBoosting, and XGBoost are all effective techniques for predicting the aggregate soil health condition newline | |
dc.format.extent | ||
dc.language | English | |
dc.relation | ||
dc.rights | university | |
dc.title | Smart Agriculture Health and Productivity Analysis in Soil Parameters | |
dc.title.alternative | ||
dc.creator.researcher | Meenakshi, M | |
dc.subject.keyword | Computer Science | |
dc.subject.keyword | Computer Science Software Engineering | |
dc.subject.keyword | Engineering and Technology | |
dc.description.note | ||
dc.contributor.guide | Naresh, R | |
dc.publisher.place | Kattankulathur | |
dc.publisher.university | SRM Institute of Science and Technology | |
dc.publisher.institution | Department of Computer Science Engineering | |
dc.date.registered | ||
dc.date.completed | 2024 | |
dc.date.awarded | 2024 | |
dc.format.dimensions | ||
dc.format.accompanyingmaterial | DVD | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Department of Computer Science Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 199.12 kB | Adobe PDF | View/Open |
02_preliminary page.pdf | 705.22 kB | Adobe PDF | View/Open | |
03_content.pdf | 536.24 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 336.94 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 1.39 MB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 632.31 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 1.2 MB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 951.3 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 1.19 MB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 908.95 kB | Adobe PDF | View/Open | |
11_chapter 7.pdf | 1.16 MB | Adobe PDF | View/Open | |
12_chapter 8.pdf | 459.2 kB | Adobe PDF | View/Open | |
13_annexures.pdf | 564.79 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 525 kB | Adobe PDF | View/Open |
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