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

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01_title page.pdfAttached File174.33 kBAdobe PDFView/Open
02_preliminary page.pdf319.07 kBAdobe PDFView/Open
03_content.pdf236.27 kBAdobe PDFView/Open
04_abstract.pdf212.69 kBAdobe PDFView/Open
05_chapter 1.pdf701.96 kBAdobe PDFView/Open
06_chapter 2.pdf439.14 kBAdobe PDFView/Open
07_chapter 3.pdf659.88 kBAdobe PDFView/Open
08_chapter 4.pdf883.05 kBAdobe PDFView/Open
09_chapter 5.pdf887 kBAdobe PDFView/Open
10_chapter 6.pdf699.05 kBAdobe PDFView/Open
11_chapter 7.pdf231.05 kBAdobe PDFView/Open
12_annexures.pdf276.07 kBAdobe PDFView/Open
80_recommendation.pdf270.36 kBAdobe PDFView/Open
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