How o-ns23/smart-irrigation-requirement-prediction-using-machine-learning works
This repository is a notebook-based machine-learning project for classifying irrigation need as Low, Medium, or High. Its documented workflow is dataset preprocessing, model training, and evaluation; the notebook also contains visualizations, and the report is a separate included artifact. The README documents these stages but no source excerpts were available, so specific model-to-model calls, saved-model behavior, and a deployed farmer-facing prediction interface are not represented.
Main entry points: Irrigation.ipynb Model training · irrigation_prediction.csv Agriculture dataset
Components 9
ML workflow
- Data preprocessing
Irrigation.ipynb - Model training
Irrigation.ipynb - Irrigation classification
Irrigation.ipynb
Evaluation insights
- Model evaluation
Irrigation.ipynb - Performance visualizations
Irrigation.ipynb
Project artifacts
- Agriculture dataset
irrigation_prediction.csv - Project report
Smart_Irrigation_Requirement_Prediction_using_ML_Project_Report.pdf
Other components
- Farmer or planner
- Low / Medium / High need
Connections 7
- Agriculture dataset → Data preprocessing: provides data
- Data preprocessing → Model training: prepares features
- Model training → Model evaluation: evaluated by
- Model training → Irrigation classification: classifies need
- Irrigation classification → Low / Medium / High need: produces class
- Model evaluation → Performance visualizations: summarized as
- Farmer or planner → Low / Medium / High need: uses guidance