Image Based Plant Leaf Disease Detection Using Transfer Learning Plant Name-Hibiscus
Objective
In this application, we are detecting and identifying the crops disease using CNN (Convolutional Neural Network) model.
Abstract
Crop diseases and pests are important factors determining the yield and quality of plants. Crop diseases identification In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study Crop diseases and pests suggestion has become a research issue of great concern to researchers. In this project we evaluate the crops 1) Hibiscuss Plant to detect disease and suggest pesticides. This review provides a definition of crop diseases detection problem, puts forward a comparison with traditional plant diseases methods. According to the difference of network structure, this study outlines the research on crop diseases based on deep learning in recent years. In this project we use CNN (Convolutional neural network) model to detect plants disease.
KEYWORDS: Convolutional Neural Network, Deep Learning, Hygiene Crops.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
SYSTEM SPECIFICATIONS:
SOFTWARE CONFIGURATIONS:
Technology : Machine learning, Python
Libraries using : Pandas, Matplotlib, Numpy, Sklearn
Version : Python 3.6+
Server side scripts : HTML, CSS, JS
Frame works : Flask
IDE : Pycharm, Miniconda
HARDWARE CONFIGURATIONS:
RAM : 8GB, 64 bit os.
Processor : I3/Intel processor
Operating system : Windows 10 pro
Learning Outcomes
LEARNING OUTCOMES:
· Scope of Real Time Application Scenarios.
· Objective of the project .
· How Internet Works.
· What is a search engine and how browser can work.
· What type of technology versions are used .
· Use of HTML , and CSS on UI Designs .
· Data Parsing Front-End to Back-End.
· Working Procedure.
· Introduction to basic technologies used for.
· How project works.
· Input and Output modules .
· Frame work use.
· About python.
· About CNN
· What is Deep learning.
· What are Deep learning algorithms.
· How can we identify and detect the fake reviews by using machine learning algorithms.
· What is meant by preprocessing.
· What are preprocessing techniques.
· How can we collect dataset.
· Practical exposure to
· Hardware and software tools.
· Solution providing for real time problems.
· Working with team/ individual.
· Work on Creative ideas.
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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, and not projects of or by IEEE.
