Modeling of wheat yield using plant structural biomarkers.

This research focused on enhancing wheat yield modeling by incorporating plant morphological parameters, specifically the Leaf Area Index (LAI) normalized by plant height (H) and its square (H²), alongside the Normalized Difference Vegetation Index (NDVI). The goal was to assess whether dimensional parameters such as LAI/H and LAI/H² improve yield prediction accuracy compared to using NDVI alone. Data analysis was performed at 60, 90, and 120 days after sowing (DAS). The LAI/Height² parameter demonstrated a stronger correlation with observed yields, with R² values ranging from 0.72 to 0.81 across all DAS stages. However, at DAS 60, the model’s performance was poor for certain plots (F3, N1-N3), indicating variability due to specific plot conditions. Combining NDVI with LAI/Height and LAI/Height² further enhanced predictive accuracy, with R² values consistently between 0.71 and 0.81 across DAS 60, 90, and 120. Despite these improvements, the INSEY model based solely on NDVI showed limited predictive capability. The study emphasizes that while NDVI captures vegetative vigor, integrating plant morphological traits better accounts for yield variability. Future research should investigate cumulative NDVI and direct measurements of LAI along with height, considering environmental and agronomic factors such as nutrient application, soil fertility, and weather conditions. These findings contribute to refining crop yield models, enabling more accurate forecasting and informed agronomic practices. Overall, the combination of NDVI, LAI, and plant height proves to be a robust method for predicting wheat yield, especially when monitored at critical growth stages.