CNN-DQN Fusion for Precision Agriculture: A Reinforcement Learning Approach to Yield Prediction and Resource Optimization

This paper introduces a novel CNN-DQN fusion framework to address the spatiotemporal coupling challenges inherent in precision agriculture. In contrast to conventional methods that treat prediction and decision-making as separate tasks, our approach integrates 3D-CNN-based feature extraction with dueling deep Q-network (DQN) optimization within an end-to-end learning system. The key technical innovations of this framework include: (1) A 5-layer pyramidal 3D-CNN encoder with adaptive dilation rates $(\mathbf{2, 4, 8})$ for Sentinel-2 multispectral fusion ($10 \mathrm{~m} / 20 \mathrm{~m}$ bands), coupled with TCN-based IoT temporal encoding (kernel size $=1.5 \times$ seasonal cycle); (2) A bio-inspired reward mechanism that incorporates crop growth models to optimize farming decisions; (3) A dynamic action space configuration for the allocation of fertilizer and water resources. Experimental results, obtained from multi-year agricultural datasets, show a 23.7% improvement in yield prediction accuracy ($\mathbf{R M S E} \boldsymbol{=} \mathbf{0. 8 7}$) and an 18.4% increase in resource utilization efficiency compared to traditional benchmark methods. This framework offers a robust decision-support paradigm for adaptive farm management, particularly under the influence of climate variability.