arXiv:2610.28570v1 Announce Type: new Abstract: Greenhouse supplemental lighting is essential for meeting crop Daily Light Integral (DLI) requirement. However, conventional always-on and rule-based strategies are inherently energy-inefficient since they ignore future sunlight availability. This paper reports the results of deploying various supplemental lighting control strategies, namely, always-on, always-off, heuristic, optimal prediction-based, and two peak-shift variants, simultaneously in a single greenhouse at the Hampton Roads AREC of Virginia Tech, to ensure that every treatment experiences identical conditions. %The shared infrastructure is built on an MQTT-based IoT platform with Photosynthetic Photon Flux Density (PPFD) sensing and PWM-driven LED control; The prediction-based and peak-shift strategies optimize LED schedules using a Markov chain-based solar forecast. Experimental results over two replicates %(20~February--15~April~2026, 72~plants per treatment) show that the prediction-based strategy meets the desired DLI target at 65\% cost reduction relative to the always-on baseline while achieving the highest dry-mass yield among the proposed adaptive strategies. The heuristic strategy incurs only a 6\% cost premium over prediction-based control, offering a competitive forecast-free alternative. The peak-shift variants achieve the lowest active-strategy costs by shifting LED demand into off-peak tariff windows. A yield-per-dollar efficiency metric is finally introduced to jointly capture energy cost and crop output showing that the prediction-based strategy ranks highest at 18.976~g/\$, more than double the always-on value of 9.391~g/\$. These findings can provide greenhouse operators with an evidence-based framework for selecting a lighting control approach that balances energy cost, crop quality, and system complexity.