Multi-Class Classification of Plant Leaf Diseases Using Feature Fusion of Deep Convolutional Neural Network and Local Binary Pattern
Objective
This study develops a dual-stage classification system combining CNN and LBP feature fusion to accurately identify plant species and diseases.
Abstract
This study presents a multi-class classification approach for identifying plant leaf diseases using a combination of deep convolutional neural networks (CNN) and Local Binary Pattern (LBP) feature fusion. The method first classifies the plant species, such as Apple, Tomato, or Grape, and then classifies the specific disease affecting the plant, including Black Rot, Scab, Cedar Rust, and others. The process begins with an input image, which undergoes noise removal and restoration. LBP features are then extracted from the image, and the data is organized into a datastore, with labels split by proportion. Augmented image data is generated in batches to enhance training. A CNN is employed with various layers and training options to classify the plant species. After determining the plant species, the CNN undergoes additional training to classify the specific disease type. This dual-stage classification system improves accuracy by leveraging both deep learning and texture-based features, offering a robust solution for early and precise detection of plant diseases.
Keywords: Plant Disease Dataset, Pre-Processing, Convolutional Neural Networks, Deep learning, Feature Extraction, Classification, Accuracy.
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
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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.
