Traditional agricultural recommendation systems rely on static machine learning (ML) classifiers that treat crop selection as a single-season classification problem, failing to account for the sequential, interdependent nature of farming decisions. Such systems ignore how a current planting choice alters future soil health, water availability, and cumulative economic returns. This paper presents a dynamic, multi-season crop recommendation engine grounded in Temporal Difference (TD) Reinforcement Learning, specifically Q-Learning. The system models the agricultural cycle as a Markov Decision Process (MDP), wherein an autonomous agent learns a sustainable planting policy by interacting with an environment simulator built from district-level datasets of Maharashtra. The state space integrates meteorological data from the Indian Meteorological Department (IMD), crop yield statistics from the Directorate of Economics and Statistics (DES), and soil nutrient profiles from the Soil Health Card Portal. The reward function combines financial yield with ecological penalty terms that discourage unsustainable resource depletion. Experimental validation across 10,000 training episodes demonstrates that the Q-Learning agent converges to a context-aware, rotation-based policy by approximately episode 4,000, consistently outperforming a static Random Forest baseline in long-term cumulative returns. The agent learns to integrate nitrogen-fixing legumes (Soybean, Chickpea) after nutrient-intensive crops, preserving soil fertility while maximizing five-year profit. These findings demonstrate that sequential decision-making frameworks are fundamentally better suited to the temporal realities of precision agriculture than isolated classification models.

Keywords: Reinforcement Learning; Q-Learning; Markov Decision Process; Crop Recommendation; Precision Agriculture; Maharashtra Agriculture