Detection of Corn Leaves Nutrient Deficiency Using Support Vector Machine (SVM)
Objective
This project aims to develop a robust and accurate system for identifying nutrient deficiencies in corn plants through the utilization of machine learning techniques, specifically Support Vector Machine (SVM).
Abstract
Like other plants in general, corns are also requiring nutrients for their life. Nitrogen, phosphorus, and potassium are at least three main nutrients that all plants always need except corn. There are so many methods that can use to examine these three nutrients for corn through its leaves such as Leaf Color Chart (LCC), Chlorophyll Meters Soil Plant Analysis Development (SPAD), and Soil Test Kit.
One method that is mostly used by farmers to examine nutrients content through corn leaves is used Leaf Color Chart (LCC) because it cost less than the other two. To overcome this problem, digital image processing could be a good solution that can be adopted by farmers to examine their plant's nutrients needs in an easier and cheaper way. In this study, the RGB extraction method of Hue, Saturation, Value (HSV) is proposed for a digital image processing system for corn leaves images.
To classified its images result, Support Vector Machine (SVM) is used as a classification method for this study. By using this proposed method, an accuracy value of 80% is achieved to detect nutrients content in corn leaves
Keywords: Agriculture, Corn, Nutrients, Digital Image Processing, SVM, Machine Learning
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is an Image/pixel?
· About image formats
· Introduction to Image Processing
· How digital image is formed
· Importing the image via image acquisition tools
· Analyzing and manipulation of image.
· Phases of image processing:
o Acquisition
o Image enhancement
o Image restoration
o Color image processing
o Image compression
o Morphological processing
o Segmentation etc.,
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skillsDemo Video
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
