Intelligent Crop Recommendation System using Machine Learning

Intelligent Crop Recommendation System using Machine Learning

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

Aim:


         Predict the crop yield and deliver the end user with proper recommendations about required fertilizer ratio based on soil parameters. We also recommend the crop price using machine learning.


Abstract


          India is one of the leading countries worldwide in terms of farm output. Even after being a leading producer of agricultural products, India still lacks farm productivity. Farmers have very less income because of the lack of farm productivity. There needs to be an increase in productivity, in order to get more income for the farmers. To increase productivity, farmers should know which crop would suit the specific piece of land. If the right type of crop is cultivated in that piece of land, then automatically, the yield of the crop will increase.


        Hence, crop recommendation systems can be very beneficial for farmers. Many factors do effect the growth of crops. Temperature, humidity, pH, rainfall, amount of potassium, nitrogen, phosphorous in soil all of these are the factors on which the yield depends. Many farmers have no idea about what crop to be grown in which area that will lead to maximum yield as well as profit. Hence in this paper we are going to explain how machine learning algorithm can be used to predict the crop and price prediction.


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 Web application created by flask. The login details are stored in SQLite database. Once the user login into the system they gets all the access for predicting crop yield and using the input such as location, nitrogen, phosphorous, potassium and pH values depends on their forming land environment.. We can also find the primary nutrients of soil by given the input as crop name. It passes the various inputs to the controller which uses the Random Forest for classification. We recommend to the former how much fertilizer required in ratio based on soil parameters and the crop price using machine learning techniques.








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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.

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