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Highlights

Real-time lettuce biomass was estimated using RGB images in a vertical farming system.

ABSTRACT. Lettuce is one of the most commonly cultivated crops in indoor vertical farming systems utilizing artificial lighting. Accurate forecasting of its biomass yield is essential for aligning production with market demand and optimizing resource use. This study presents a two-stage machine learning framework combining a Convolutional Neural Network (CNN) for real-time biomass estimation, followed by both an Extreme Gradient Boosting (XGBoost) and a Long Short-Term Memory (LSTM) model for future biomass forecasting. Using hourly top-down RGB images collected over a 30-day cultivation cycle, the CNN achieved high prediction accuracy with an R² of 99.56% and a root mean square error (RMSE) of 3.65 g. The XGBoost and LSTM models, utilizing a sliding-window approach, achieved R² value of 95.10% and 94.31%, respectively, and a forecasting RMSE of 6.63 g and 6.71 g, respectively. These results demonstrate the potential of machine learning models for non-destructive, real-time yield forecasting in indoor farming, providing a foundation for data-driven resource management and production planning in indoor vertical farming systems.