SMART AGRICULTURE: AN INTELLIGENT DECISION SUPPORT SYSTEM WITH ADVANCED MACHINE LEARNING AND EDGE AI

Mr. Dandu Jayabharath Reddy, Bolbekova Muhlisa, Anvarova Farangiz, Kamolova Sevara

Authors

Mr. Dandu Jayabharath Reddy, Bolbekova Muhlisa, Anvarova Farangiz, Kamolova Sevara Sambhram University, Jizzax, Uzbekistan

Keywords:

Machine Learning, Decision Support System, Smart Agriculture, Precision Farming, Edge AI, Federated Learning, Crop Yield Prediction.

Abstract

Smart agriculture integrates modern digital technologies to enhance farming productivity and sustainability. This paper presents an updated Intelligent Machine Learning-based Decision Support System (IML-DSS) that incorporates Edge AI and Federated Learning for real-time agricultural decision-making. The system predicts crop yield, detects plant diseases, and optimizes resource utilization using multi-source data such as IoT sensors, satellite imagery, and climate databases. Advanced models including ensemble learning, transformer-based architectures, and explainable AI are utilized to process heterogeneous agricultural data. Edge-cloud integration enables low-latency processing and scalability. Experimental validation using recent datasets shows improved prediction accuracy, faster decision-making, reduced environmental impact, and enhanced farmer trust through interpretability. The proposed system contributes significantly to sustainable and data-driven agriculture.