ISSN No:2250-3676 ----- Crossref DOI Prefix: 10.64771 ----- Impact Factor: 9.625
(Peer Reviewed, Referred & Indexed Journal)
Real-Time Smart Irrigation Management With Extended Hybrid Regression And Neural NetworksID: 3169 Abstract :Hybrid Ensemble Regression Model With Web-based Deployment Is Added To The Prediction Framework To Increase Smart Irrigation System Accuracy And Dependability. Gradient Boosting, XGBoost, And AdaBoost Predict Soil Moisture, Temperature, And Humidity Utilizing A Voting Regressor In The Suggested Expansion. A Two-stage ANN With The Suggested TANELU Activation Function Uses These Predicted Properties To Assess Irrigation Need And Optimal Watering Time. A Flask-based Web Interface Allows Real-time Data Entry, Analysis, And Irrigation Suggestions, Making The System Scalable, User-friendly, And Adaptable To Varied Agricultural Settings While Decreasing Water Waste. |
Published:31-5-2026 Issue:Vol. 26 No. 5 (2026) Page Nos:1827 - 1837 Section:Articles License:This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. How to Cite |