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We provide abstract of classification ml algorithm in this paper

CROP RECOMMENDATION USING MACHINE LEARNING TECHNIQUES-crop recommendation using machine learning

In the world of developing technologies, the success of sharing information will help the agriculturists in realizing and developing their potential. so The information sharing is that the valuable and timely information is being shared between agriculturists, either formally or informally. so The willingness of information sharing refers to the open attitude among agriculturists. so This open attitude determines the degree and scope of information sharing. Using web-technologies like html and CSS .We build the web application, we create dataset by gathering data from multiple resources and place them in place which is used to predict the price of the crop and results are subjected to non-linear test later priorities are set and rankings are given to the list of crops.

Place information in our application and share that information to agriculturists whose data is collected and stored in the MY SQL server. Thus software to automatically send the updated information to the agriculturists in the form of text message. So that agriculturists no need to go to near by towns and cities to know the updated information. so We will be using machine learning algorithms to predict the price of the crop for the next two months. so For prediction purpose we will be using Support vector machine(SVM), Naïve Bayes and K-Nearest Neighbour(KNN) algorithms to predict the cost of the crop production. Further, a ranking process is applied for decision making in order to select the classifiers results.

Existing System:
Currently, farmers rely on traditional methods and local knowledge for crop selection. This approach often lacks precision and may not be optimal for maximizing yield. thus The absence of a systematic method for crop recommendation based on scientific data leads to suboptimal agricultural practices.

Proposed System:
finally The proposed system leverages machine learning algorithms to analyze various factors like soil type, temperature, humidity, and precipitation to suggest the most suitable crops. Hence By integrating this system into a web platform, farmers can easily access personalized recommendations, thereby optimizing their agricultural productivity.

Module-wise Explanation:

  1. User Input Module: Firstly, Farmers enter specific details about their land, including soil type, location, and climate conditions.
  2. Data Processing Module: Machine learning algorithms process the input data, considering historical crop performance and environmental parameters.
  3. Recommendation Module: so The system generates a list of recommended crops based on the processed data and presents it to the user.
  4. User Interface Module: so The web-based interface provides an intuitive platform for users to interact with the system.

System Requirements:

  • Web server (e.g., Apache, Nginx)
  • Database server (e.g., MySQL, PostgreSQL)
  • Python runtime environment
  • Machine learning libraries (e.g., scikit-learn, TensorFlow)
  • Web development frameworks (e.g., Django, Flask)


  • Random Forest: For predicting crop suitability based on historical data.
  • K-Means Clustering: For grouping similar environmental conditions.
  • Decision Trees: For interpreting and explaining the reasoning behind recommendations.

Hardware and Software Requirements:

  • Hardware: Standard server infrastructure with sufficient processing power and storage.
  • Software: Operating system (e.g., Linux), Python interpreter, web server, database server.

The system follows a client-server architecture. so The web server handles user requests, communicates with the machine learning module for data processing, and retrieves recommendations from the database.

Technologies Used:

  • Frontend: HTML, CSS, JavaScript, Bootstrap
  • Backend: Python, Django framework
  • Database: MySQL
  • Machine Learning: scikit-learn, TensorFlow

Web User Interface:
The user interface provides a simple form for farmers to input their land details. Hence The recommendation results are displayed in a clear and understandable format. Farmers can view suggested crops along with relevant details such as expected yield, growth period, and any additional cultivation advice(classification ml algorithm).


  • Soil type
  • Location details (latitude, longitude)
  • Climate parameters (temperature, humidity, precipitation)


  • List of recommended crops
  • Additional information on recommended crops (e.g., growth period, expected yield)
  • Explanation of the reasoning behind the recommendations


Collaboration Diagram

classification ml algorithm
Collaboration Diagram

sequence diagram

classification ml algorithm
sequence diagram

component diagram

classification ml algorithm
component diagram

Flow chart Diagram

classification ml algorithm
Flow chart Diagram
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