Crop field paddy crop disease detection using SVM and CNN algorithm

Project Code :TCMAPY571

Objective

In this application, we create a deep learning architecture to identify and detect of rice plantation based diseases.

Abstract

Agriculture is essential for the economy and plant leaf disease must be minimized. Early recognition of problems is important, but the manual inspection is slow, error-prone, and has high manpower and time requirements. Artificial intelligence can be used to extract leaf color, shape, or texture data, thus aiding the detection of infections. Rice is one of the major cultivated crops in India which is affected by various diseases at various stages of its cultivation. It is very difficult for the farmers to manually identify these diseases accurately with their limited knowledge. Recent developments in Deep Learning show that Automatic Image Recognition systems using Convolutional Neural Network (CNN) with Transfer Learning (TL) models can be very beneficial in such problems. Since rice and tomato leaf disease image dataset is not easily available, we have created our own dataset which is small in size hence we have used Transfer Learning to develop our deep learning model. The proposed CNN architecture is based on MobileNet and is trained and tested on the dataset collected from rice fields and the internet.

KEYWORDS: Convolutional Neural Network, Deep Learning, Tomato leaf diseases, Rice Leaf Diseases, Transfer 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

H/W Configuration:

  • Processor:I3/Intel Processor
  • Hard Disk:160GB
  • Key Board: Standard Windows Keyboard
  • Mouse: Two or Three Button Mouse
  • Monitor: SVGA
  • RAM : 8Gb

 

S/W Configuration:

  • Operating System :Windows 7/8/10                 
  • IDE: Pycharm
  • Libraries Used : Numpy, Tensorflow, Keras, Flask
  • Technology: Python 3.6+

 

Learning Outcomes

·         Scope of Real Time Application Scenarios.

·         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.

·         Practical exposure to

o   Hardware and software tools.

o   Solution providing for real time problems.

o   Working with team/ individual.

o   Work on Creative ideas.

·         Frame work use.

·         About python.

Demo Video

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