FarmForecast: An Explainable AI System for Sustainable and Profitable Agriculture

Indian agriculture faces many challenges due to changing local climates, limited resources, and a lack of data-driven tools. Farmers often rely on traditional knowledge, which can lead to poor decisions, more waste, poverty, and instability. The common practice of growing the same crop repeatedly also harms the soil and threatens long-term sustainability. This paper presents an AI-based Decision Support System (DSS) that checks crop suitability and recommends the best crops for specific $r$ egions b y a nalyzing $f$ actors $l$ ike $r$ ainfall, season, location, and cultivated area. Using machine learning and Explainable AI methods like SHAP, the system clearly explains its crop recommendations and suggests alternatives if needed. It also provides estimates for expected yields, fertilizer and pesticide needs, and possible market prices. Additionally, a Genetic Algorithm helps optimize crop combinations to support sustainable, nutrient-balanced multiple cropping. This reduces soil damage and improves ecological health. The easy-to-use platform helps farmers make better crop choices, supporting sustainable agriculture to boost both economic and environmental outcomes. By overcoming the limits of traditional farming and offering practical, data-based advice, this DSS aims to reduce waste, improve farmers' incomes, and promote environmentally friendly farming across India, leading to more stability and productivity in the sector.