Smart Agriculture Advisor: A Machine Learning Approach to Precision Farming

Precision agriculture employs data-driven methodologies to enhance agricultural efficiency and sustainability. Nevertheless, small and medium-sized farms often face challenges in accessing cost-effective, real-time advice resources. This study introduces the Smart Agriculture Advisor, a machine learning (ML)-based decision support system that provides crop recommendations, fertilizer guidance, and disease diagnostics using pre-processed datasets and localized data storage. This system, in contrast to conventional precision agriculture systems that depend on expensive IoT sensors and cloud services, generates insights from historical agricultural data and farmer-input records, thereby enhancing accessibility for resource-constrained farmers.The suggested methodology integrates predictive analytics, community-driven updates, and a mobile application featuring multilingual assistance (e.g., English, Hindi, Spanish) to empower farmers in many regions, including the USA, India, and other developing agricultural areas. A comparative review of current precision agriculture technologies reveals that machine learning-driven insights substantially improve decision-making while ensuring scalability and cost-efficiency. Findings indicate that AI-driven decision support systems can close the technological divide for smallholder farmers, promoting more sustainable and effective agricultural methods.