🌾🌾Crop recommendation and production prediction

❓Problem Statement:

The aim is to develop a predictive model that can forecast crop yields based on various factors such as district, state, season, and crop type. By leveraging supervised machine learning techniques, the model will provide farmers with valuable insights into expected crop yields in advance. This information enables farmers to make informed decisions regarding crop selection and planning, ultimately optimizing agricultural productivity and resource allocation.

Introduction:

In agriculture, predicting crop yields is crucial for effective decision-making and resource management. By forecasting yields in advance, farmers can better plan their planting schedules, optimize resource allocation, and mitigate risks associated with crop failure or low yields. Traditional methods of yield prediction often rely on historical data and subjective assessments, which may lack accuracy and reliability.

Leveraging machine learning techniques offers a more systematic and data-driven approach to crop yield prediction. By analyzing historical yield data along with various contextual factors such as district, state, season, and crop type, predictive models can identify patterns and correlations that contribute to accurate yield forecasts. This project aims to develop such a model to empower farmers with actionable insights for improved crop management and decision-making.

🗃️Data Set Information:

• Data Source: [http://localhost:8888/edit/ML%20MiniProject/crop_production.csv],[http://localhost:8888/edit/ML%20MiniProject/Crop_recommendation.csv]

• Description of dataset 1, crop production: The dataset contains historical records of crop yields along with corresponding factors such as district, state, season, and crop type. Each record represents a specific instance of crop cultivation and its corresponding yield.

• Sample Data:

  • District: XYZ State: ABC Season: Kharif Crop Type: Rice Yield (in tons/acre): 5.8
  • District: XYZ State: DEF Season: Rabi Crop Type: Wheat Yield (in tons/acre): 4.3

  Description of dataset 2, crop recommendation: The dataset contains records of multiple attributes that determine what type of crop can be grown for the given weather, soil and nutrient availability conditions

• Sample Data:

  • N: 94,
  • P: 50,
  • K: 37,
  • temperature: 25.66585205,
  • humidity: 80.66385045,
  • ph: 6.94801983,
  • rainfall: 209.5869708,
  • label: rice