Leaf Diseases Prediction Pest Detection and Pesticides Recommendation using Deep Learning Techniques
Objective
The primary objective of this project is to develop a comprehensive and intelligent system that leverages deep learning techniques to address critical challenges in agriculture, specifically related to leaf diseases, pest detection, and the recommendation of suitable pesticides. The project aims to revolutionize agricultural practices by providing timely and accurate support to farmers and agricultural stakeholders
Abstract
In this paper, we propose a framework for classifying the top view image of amphetamines based on their logo using SURF and Bag-of-features model. During our experiment, we found that the unsmooth surface of amphetamines and low contrast are the main factors of low accuracy for classification. Therefore, we propose a process to enhance the main feature and reduce noise on the surface using adaptive filter, Contrast-Limited Adaptive Histogram Equalization (CLAHE), active contour and image morphology. The result from our proposed preprocess algorithm shows that the clarity of the logo on amphetamines is improved and the noise is reduced. We also then apply SURF to extract features and classify using Bag-of-features model. This experimental result shows that our proposed preprocess for each step can improve the accuracy up and the accuracy of our method up to 97 percent.
Keywords: Amphetamines image, SURF, Bag-of-features, CLAHE.
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 skills
Paper Publishing
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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.
