Explainable LightGBM of Plant-Growth Responses Across Diverse LED Experiments

Interpreting plant-growth responses across diverse light-emitting diode (LED) experiments requires attention to differences in species, lighting conditions, measurements, and experimental design. This study combines a descriptive comparison of six public LED-growth resources with an explainable Light Gradient Boosting Machine (LightGBM 4.6.0) case study. The resources cover sweet pepper, lettuce, soybean, Arabidopsis, European yew, and ornamental plants; regression modeling and SHapley Additive exPlanations (SHAP 0.50.0) analysis were conducted only on sweet pepper. Of 144 original observations, 71 complete records were retained for dry-biomass prediction. On the original held-out test set, LightGBM Model A outperformed ridge regression (R2 = 0.755 versus 0.460; RMSE = 0.136 versus 0.216 g; MAE = 0.104 versus 0.185 g). However, leave-one-temporal-replicate-out cross-validation favored ridge regression (mean R2 = 0.600 ± 0.191) over LightGBM (0.078 ± 0.248). Excluding instrument gain and additional harvest-related predictors reduced LightGBM R2 to 0.111. SHAP ranked chlorophyll-fluorimeter gain and LED treatment highest in Model A, whereas LED treatment and initial plant height led Model B. The resource comparison identifies challenges in comparing lighting treatments and growth measurements, while the case study demonstrates sensitivity to predictor availability and validation design. Together, these findings clarify the requirements for applying explainable LightGBM to diverse LED experiments without establishing cross-species predictive generalizability or causal lighting effects.