Agriculture is increasingly affected by climate vari- ability, soil degradation, fluctuating market prices,
rising labor costs, and uncertainty in water availability. Traditional crop selection methods are often
based on past practice or intuition and therefore may not adapt well to current environmental and
economic conditions. This paper presents AgroIntel, an AI- powered agricultural decision support system
that recommends suitable and profitable crops by integrating three years of historical data related to
soil, weather, cultivation cost, yield, and market conditions. The proposed framework performs crop
suitability analysis, cultivation cost estimation, yield prediction, profit forecasting, crop failure risk
assessment, and intercropping recommendation within a single pipeline. In addition, the system reports
a Natural Farming Suitability Score to support low-input and environmentally responsible cultivation.
A farmer-friendly output layer presents ranked crop options, crop calendars, visual summaries, and
knowledge tables for practical field use. The results indicate that the proposed system can improve crop
selection quality, reduce cultivation risk, and support sustainable farm planning.
