A Non-Destructive Methodological Approach for Modeling Continuous Drought Stress Dynamics in Opuntia ficus-indica Using Hyperspectral and UAV RGB Imagery

Destructive methods for monitoring stress responses remain a bottleneck in precision agriculture. This study presents a non-destructive methodological framework evaluating drought responses in 30 Opuntia ficus-indica plants over four months under five irrigation levels. Cladode traits (color, weight, and thickness) were measured alongside RGB imagery from a UAV and hyperspectral imaging (400–1000 nm). Partial least squares regression (PLSR) models showed high capability to model proline (R2 = 0.91), chlorophyll a (R2 = 0.97), and total chlorophyll (R2 = 0.97) within the experimental dataset. Crucially, these models reflected continuous spectral–physiological variation across the irrigation gradient rather than discrete treatment separation, with key spectral regions identified at 530–600 nm and 550–750 nm. UAV-derived RGB imagery enabled the estimation of plant area and biomass (R2 = 0.88). Under extreme drought, cladode thickness decreased by approximately 41%, accompanied by reduced biomass and increased soluble solids (°Brix). While no statistically significant differences were observed among irrigation treatments for biochemical variables, limiting treatment discrimination based on discrete classification, the hyperspectral data successfully captured the underlying continuous physiological variation. Consequently, this work demonstrates the methodological viability of integrating UAV structural phenotyping and hyperspectral analysis as a continuous monitoring tool rather than a rigid classification system. These findings provide a methodological baseline that highlights the need for continuous sensing in CAM plants, though further validation with independent datasets remains essential for wider operational application.