Empirical Calibration of RGB Vegetation Indices for Non-Destructive Shoot Segmentation and Area Estimation in Blackberry Micropropagation

Accurate, non-destructive quantification of shoot development in plant micropropagation is essential for optimising culture protocols and improving throughput. This study evaluated the performance of four RGB vegetation indices, Excess Green (ExG), Green Leaf Index (GLI), Modified Green Red Vegetation Index (MGRVI), and Visible Atmospherically Resistant Index (VARI), for the automated segmentation of Rubus spp. shoots in close-range orthophotos acquired with a smartphone camera. Twelve vessels, each containing fifteen blackberry shoots, grown at four sucrose concentrations (0, 7.5, 15 and 30 g L−1) were analysed at both pixel and ROI (Region Of Interest) levels. Classification thresholds were determined by maximising the Youden index on the ROC curve, both as a single global threshold and as concentration-specific thresholds. GLI and ExG substantially outperformed MGRVI and VARI in all discriminability metrics (AUC-ROC = 0.991 and 0.988, respectively), with stable classification performance across all sucrose concentrations. Projected shoot cluster area estimates derived from the two best-performing indices were benchmarked against a geometrically explicit reference obtained from a colour-filtered 3D point cloud reconstructed by Structure-from-Motion (SfM) photogrammetry from the same image set. Strong agreement was observed between RGB-based and point-cloud-derived area estimates (R2=0.990 for GLI), with Intersection over Union (IoU) ≥0.84 for all vessels. Concentration-specific thresholds provided a modest improvement in in-sample regression fit over the single global threshold without substantially affecting geometric agreement. A separate external dataset of six vessels, with independently annotated masks, was subsequently used to assess threshold transferability (mean IoU 0.908 for GLI and 0.861 for ExG). The results indicate that, under the tested conditions, GLI and ExG applied to smartphone-acquired orthophotos provide a relatively low-cost, non-destructive approach for projected shoot cluster area extraction in Rubus spp. in vitro cultures. The study is primarily methodological: the relationship between the image-derived measurements and independent biological descriptors of shoot development or propagation performance was not assessed.