AI-Based Crop Demand Forecasting and Cultivation Optimization for Market Price Stabilization in Smart Agriculture

India's agricultural economy continues to face persistent challenges in stabilizing crop prices, balancing production with demand, and securing farmer profitability. Despite progress in precision farming and digital marketplaces, cultivation decisions remain largely reactive, leading to oversupply or shortage cycles. This paper proposes DynaCropNet, an AI-based Crop Demand Forecasting and Cultivation Optimization Framework that integrates multi-source datasets from eNAM and Data.gov.in to forecast demand, predict market prices, and recommend region-specific crop allocations. The model employs Long Short-Term Memory (LSTM), ARIMA, and Prophet components enhanced by a Temporal–Spatial Fusion Transformer and a Differentiable Optimization Layer for end-to-end learning. A reinforcement module dynamically adjusts cultivation strategies based on simulated market feedback. Experimental results show that DynaCropNet achieves RMSE $=27.85$, MAPE $=6.92 \%$, and reduces price variance by 57.9%, while improving farmer income by 16.5% across major states. The system effectively synchronizes cultivation planning with forecasted demand, ensuring price stability and equitable economic outcomes. This research demonstrates how integrating AI-driven forecasting with optimization and reinforcement learning can transform agriculture from reactive management to predictive and prescriptive intelligence for national food security and market resilience.