COMPREHENSIVE CROP ADVISORY SYSTEM FOR SUSTAINABLE AND RESILIENT AGRICULTURE

This paper presents an intelligent, data-driven Crop Advisory System designed to enhance the resilience and
sustainability of modern agriculture. The system addresses the critical challenge of optimal crop selection by
leveraging a Random Forest Classifier trained on a comprehensive dataset of agronomic and environmental
parameters. The model achieved a predictive accuracy of approximately 99.3% and is deployed as an interactive
web application using the Flask framework. A key innovation is the system’s ability to provide a diversified list
of the top three most suitable crops, mitigating the economic risks associated with market saturation from
monoculture trends. The platform integrates a live weather API for real-time data accuracy and generates a multifaceted analytical dashboard with dynamic visualizations to support farmer decision-making. By translating
complex data into accessible and actionable insights, the system directly contributes to the principles of
Sustainable Development Goal 2 (SDG 2), promoting efficient resource management, improving food security,
and strengthening the economic viability of farmers.