Accurate harvest date prediction and control are critical for optimizing crop yield, aligning supply with market demands, and maximizing profitability in precision agriculture. However, traditional approaches often neglect the complex interactions among environmental factors. To address this challenge, we propose VegFormer, a Transformer-based framework that integrates historical sensor data, real-time crop imagery, and future environmental forecasts to enhance harvest prediction and management. Experimental results show that our Harvest Maturity Prediction (HMP) model achieves a mean absolute error (MAE) of 2 days, significantly improving prediction accuracy. Building upon this, the Adaptive Harvest Control (AHC) model, which factors in forecasted environmental conditions, further reduces the MAE to 1.6 days, enabling proactive adjustments to harvest timing based on market conditions or operational needs. Controlled experiments confirm that manipulating internal greenhouse temperatures can either accelerate or delay crop maturation, validating the system’s effectiveness in dynamic harvest management. By providing a scalable and adaptive approach to harvest prediction and control, VegFormer empowers data-driven decision-making, fostering greater agricultural efficiency and flexibility in precision farming.