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Rice Leaf Disease Detection Using Machine Learning Techniques
Rice Leaf Disease Detection Using Machine Learning Techniques
Rice Leaf Disease Detection Using Machine Learning Techniques

Rice Leaf Disease Detection Using Machine Learning Techniques

₹5,000.00
Product Code: Matlab- Machine Learning
Availability: In Stock
Viewed 4551 times

Product Description

                                        Domain: Machine Learning                                                                Tool: MATLAB R2018a


Abstract

Rice is the major cultivation focused in our country. Over the years proper maintenance of paddy leaves help the farmers to retain the growth. The existing system is presented with a rice leaf illness location framework utilizing AI draws near. Three of the most widely recognized rice plant infections specifically leaf muck, bacterial leaf curse and earthy colored spot illnesses are distinguished in this work. Away from of influenced rice leaves with white foundation were utilized as the info. The existing system uses a machine learning approach to detect three different rice leaf diseases: leaf smut, bacterial leaf blight and brown spot disease. The proposed system is focused on improving the detecting accuracy compared with the existing system in which deep recurrent neural network is used to classify the disease. The input test images are processed to color threshold function that extracts the diseases part to be segmented. The segmented part are fetched to GLCM feature extraction process to formulate the statistical parameters of the test image. The deep RNN network classifies the disease and also test the normal leaf. After the detection, suggestions on percentage of Fertilizers used and valuable feedbacks to the farmers are updated in the notification.

 


Proposed system

In the proposed system, the input test images are processed to color threshold function that extracts the diseases part to be segmented. The segmented part is fetched to GLCM feature extraction process to formulate the statistical parameters of the test image. The Deep RNN network classifies the disease and also test the normal leaf. After the detection, suggestions on percentage of Fertilizers used and valuable feedbacks to the farmers are updated in the notification.n or malignant.

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Package Includes

Software Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support


The Delivery time for software projects is 2 -3 working days. Some of the software projects will require Hardware interface. Please go through the hardware Requirements in the abstract carefully. The Hardware will take 7-8 Working Days

 

Hardware Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
  12. Full Project Documentation
  13. Online support


The Delivery time for Hardware projects is 7-8 working days.

   

Mini Projects: Software Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support

 

The Delivery time for software Miniprojects is 2 -3 working days.

 

Mini Projects - Hardware includes

  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.