Crop Yield Prediction Using Random Forest Algorithm

Crop Yield Prediction Using Random Forest Algorithm

₹5,500.00
Product Code: Python - Machine Learning
Availability: In Stock
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Product Description

Aim:

               Predict the crop type and price of the crop using machine learning methodology with accurate results.


Abstract

             India is the land of agriculture and it is the major source of economy. 70% of Indian population directly relies on agriculture. The common problem existing among the young Indian farmers is to choose the right crop based on the location, humidity, temperature, rainfall. Due to this, they face a serious setback in productivity. Our work proposes to help farmers determine the predict crop type and price by doing analysis on its various parameters and to suggest crops based on the results obtained. The system uses the Classification algorithm of Random Forest to improve the efficiency of Crop Recommendation System. The system maps the location, temperature, humidity, pH value and rainfall to predict the list of suitable crops for the soil and it also provides cost of the crop. Hence it leaves upon the user to decide on the crop to be sown. Thus, the system helps to provide knowledge to the dilettante farmers.

Proposed System:

            The system prepared predict major crops yield in a particular district in Tamil Nadu. The client on their first login has to register themselves on the application on android phone .Once the user logins into the system he gets all the access for predicting crop yield and using the input such as location, temperature, pH value, rainfall and humidity depends on their forming land environment. After submitting the inputs, it’s redirect into Firebase. The firebase is an intermediate between user input and trained data set. The input goes to the trained data, where it processes random forest algorithm to predict crop and price. After the prediction, the predicted value passes to the fire base. That firebase gives the predict value to the user on android application.


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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.