SMART CROP RECOMENDATION SYSTEM USING MACHINE LEARNING

Agriculture is a fundamental sector that sustains human life, and its modernization through digital technologies has become essential for ensuring global food security.Smart crop recommendation systems, driven by machine learning (ML), deep learning, and Internet of Things (IoT) innovations, are transforming the decisionmaking process for farmers.These systems leverage soil parameters, weather data, and historical crop yields to suggest the most suitable crop for a given region and season.This paper presents a comprehensive review and a proposed framework for an intelligent ML-based crop recommendation system.The review covers 19 recent studies (2022-2025) addressing traditional ML, deep learning, explainable AI (XAI), TinyML, and uncertaintyaware models.The comparative analysis highlights high accuracy achieved by ensemble and neural models but also identifies challenges such as limited dataset diversity, lack of interpretability, and poor real-world validation.A novel integrated architecture is proposed, combining data preprocessing, feature selection, hybrid ML algorithms, and XAI tools to provide transparent, adaptive recommendations.The model can be deployed on IoT-enabled or edge devices, allowing for real-time, energy-efficient, and user-friendly implementation.The proposed framework aims to bridge the gap between research prototypes and practical agricultural decision support.Overall, this work contributes to the ongoing pursuit of sustainable, intelligent, and farmer-centric precision agriculture.