Precision Farming Machine Learning Model

This project was developed under the CTE Tech Incubation Program and focuses on building a machine learning model for sustainable precision farming.
The algorithm predicts the optimum hourly water requirement for crops by estimating evapotranspiration rates using weather and environmental data obtained from Google Earth Engine (GEE) and Weather APIs.

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Overview

The model applies regression-based machine learning techniques to analyze climatic variables such as temperature, humidity, solar radiation, and wind speed.
By learning patterns in historical and real-time data, it supports efficient irrigation management and data-driven agricultural planning.

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Tech Stack

• Language: Python
• Libraries: scikit-learn, pandas, NumPy, matplotlib
• Data Sources: Google Earth Engine, Weather APIs

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