<p>Potato price forecasting remains challenging due to volatility, irregular seasonality, and sensitivity to exogenous shocks. This paper introduces Frequency Aware Channel Modulation (FACM), a lightweight, plug-and-play attention module that augments neural forecasters by injecting explicit priors on trend and frequency. FACM constructs dual path features through first-order differencing and discrete Fourier magnitudes, followed by channel wise reweighting that adaptively emphasizes informative components without modifying the backbone architecture. Evaluation on multi-year, multi region agricultural data shows consistent accuracy gains across recurrent backbones including RNN, LSTM, GRU, and Bi LSTM, and across short to mid-range prediction horizons. Comparative studies with recent Transformer baselines indicate competitive or superior performance under identical training protocols. Robustness analysis under Gaussian perturbations indicates slower performance degradation, attributable to spectral anchoring from the Fourier branch and smooth Lipschitz continuous responses induced by channel reweighting. The module further mitigates spectral bias in sequence models by restoring sensitivity to higher frequency variations that encode abrupt market movements. FACM provides an interpretable and deployment friendly enhancement for agricultural time series forecasting, and its general, model-agnostic design suggests strong transferability to other volatile domains, including energy price prediction, weather and climate forecasting, and traffic flow modeling.</p>