Agrobot agricultural robot using iot and machine learning
Objective
The research aims to identify oil spills using a DAM UNet model in MATLAB. It involves pre-processing with radiometric correction, terrain correction, and Lee speckle filtering, followed by semantic segmentation. Denoising with a Wiener2 filter and PSNR calculation complete the workflow.
Abstract
The focus of recent agricultural research involves leveraging plant leaf images for disease detection and classification, aiming to diminish farmers' reliance on safeguarding their crops. This study proposes employing convolutional neural networks to identify and categorize various diseases affecting paddy leaves, such as leaf blight, brown spot, leaf smut, and blast diseases. Utilizing images directly sourced from the Kaggle portal, specifically of rice plant leaves, the process involves initial image acquisition followed by pre-processing steps. This includes converting RGB images to HSV format for background removal, extracting binary images based on hue and saturation, and segmenting these images via the k-means technique. The final phase involves disease classification using Convolutional Neural Networks. The results of this classification are then expected to be sent via email.
Keywords: Plant diseases, deep learning, image processing, K -means clustering based segmentation, Convolutional neural network, mail.
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
Request Call Back
Would you like to receive a free callback now?
Choose the best time for callback:
Leave your message and we will contact you as soon as possible
6-2-85/B, Old Maternity Hospital Road, Thyagaraja Nagar, Tirupati, Andhra Pradesh – 517501
+91 9030333433
+91 9393939065
0877-2261612
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.
