Crop Recommendation System using Machine Learning

By J Suhasini

The Crop Recommendation System is a machine learning-based application that provides recommendations for suitable crops based on various environmental and soil conditions.

DESCRIPTION OF THE PROJECT:

Crop Recommendation System aims to assist farmers and agricultural professionals in making informed decisions about crop selection, optimizing yields, and maximizing profitability.The system takes into account several factors such as soil type, climate, rainfall, temperature, humidity, and pH levels to determine the most suitable crops for a given region. By analyzing historical data and using predictive models, the system provides personalized recommendations tailored to the specific conditions of a farm or agricultural area.

KEY FEATURES:

Input Data Collection: The system allows users to input relevant data such as soil parameters, climate information, and geographic location.

Data Preprocessing: The input data is preprocessed to handle missing values, normalize or scale features, and transform categorical variables.

Machine Learning Models: Various machine learning algorithms are employed, including decision trees, random forests, support vector machines (SVM), and gradient boosting techniques, to build predictive models.

Model Training and Evaluation: The models are trained on historical data and evaluated using appropriate performance metrics to ensure accuracy and reliability.

Crop Recommendation: Based on the trained models, the system recommends the most suitable crops for the given input parameters.