Plant Disease Detection and Classification by Deep Learning: A Review

Plant Disease Detection and Classification by Deep Learning: A Review

₹5,500.00
Product Code: Python - Deep Learning
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Product Description

Aim:


           To detect the plant leaf diseases using convolutional neural network for high accuracy detection.


Synopsis:


          Identification of leaf disease is very difficult in agriculture field. If identification is incorrect then there is a huge loss on the production of crop and economical value of market. Traditionally, visual examination by experts has been carried out to diagnose plant diseases and biological examination is the second option, if necessary. Leaf disease detection requires huge amount of work, knowledge in the plant diseases, and require the more processing time. Therefore, we can use image processing for identification of leaf disease. The system has been tested with the different numbers of test data set collected from different regions. This system has tested for different numbers of clusters to get the optimal number of cluster that can produce the best performance of the proposed leaf disease identification and control prediction system. In our approach, we use the technique of Convolutional Neural Network which uses the concept of hidden layers to classify the different diseases that affect the plants. The proposed deep-learning based approach can automatically identify the discriminative features of the diseased leaf images and detect the types of plant leaf diseases with high accuracy.


Proposed System:


          Agriculture is one of the most significant occupations around the world. It plays a major role because food is a basic need for every living being on this planet. In this proposed system, deep learning approach Region Based Convolutional Neural Networks(R-CNN) for identification. They have two phases namely the training phase and testing phase. In the initial phase, they have carried out image acquisition, pre-processed the image and trained the images using R-CNN. In the second phase classification and identification of the Leaf disease. For training purposes, image is taken from the dataset whereas, for testing, real-time images can be used. The diagnosis of the leaf disease is done with the images that are uploaded in the system or present in the database. If the real-time input is taken from the surrounding, then the image needs to be preprocessed followed by the feature classification The Diagnosis of diseases is detected and the name of the disease is obtained.




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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.