Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/581566
Title: | Precision Agriculture Applying Machine Learning Classification Techniques to Predict Crop Growth Based on Soil Nutrients |
Researcher: | Sakthipriya, S |
Guide(s): | Naresh, R |
Keywords: | Computer Science Computer Science Information Systems Engineering and Technology |
University: | SRM Institute of Science and Technology |
Completed Date: | 2024 |
Abstract: | A Precision Management is a familiar concept in India, the adoption of newlineprecision agricultural technologies represents a relatively novel development. Indian newlinefarmers have traditionally acknowledged that distinct sections of a field exhibit newlinediverse responses to various inputs and cultural practices. Recognizing the newlinesubstantial variations in soil conditions, fertility, moisture, and other factors within a newlinesingle field has also been a longstanding awareness among farmers. Precision newlinefarming emerges as a farm management strategy that leverages information and newlinetechnology to identify, analyze, and regulate the temporal and spatial variability newlinewithin fields. The primary objectives are to enhance profitability and productivity, newlinesafeguard land resources, and reduce production costs. The precise use of inputs in newlineconnection to crop, soil, and meteorological conditions in order to ensure efficient newlineand effective exploitation of resources without waste is known as precision farming newlinein Indian parlance. Precision farming, as we refer to it in Indian terminology, is the newlineprecise application of inputs in relation to crop, soil, and meteorological conditions newlinein order to ensure efficient and effective utilization of resources without waste newline |
Pagination: | |
URI: | http://hdl.handle.net/10603/581566 |
Appears in Departments: | Department of Computer Science Engineering |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
01_title page.pdf | Attached File | 174.33 kB | Adobe PDF | View/Open |
02_preliminary page.pdf | 319.07 kB | Adobe PDF | View/Open | |
03_content.pdf | 236.27 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 212.69 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 701.96 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 439.14 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 659.88 kB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 883.05 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 887 kB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 699.05 kB | Adobe PDF | View/Open | |
11_chapter 7.pdf | 231.05 kB | Adobe PDF | View/Open | |
12_annexures.pdf | 276.07 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 270.36 kB | Adobe PDF | View/Open |
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