Harnessing deep learning to quantify microstructural complexity in confocal images of plant protein gels

Abstract Understanding plant protein gel microstructure is key to designing functional food systems. This study introduces a deep learning framework using a U‐Net model with a ResNet34 encoder to segment and quantify confocal laser scanning microscopy (CLSM) images of plant protein gels. The model was trained on CLAHE‐enhanced grayscale images from 23 samples, with ground truth (GT) masks generated through a semi‐automated Fiji‐based workflow. The model achieved high segmentation performance (IoU: 0.82; Dice: 0.90). Structural features including porosity, protein aggregation area, fractal dimension, and lacunarity, were extracted from AI and GT masks, showing strong correlations ( R 2 = 0.741–0.881). Although minor metric biases were observed, structural patterns were consistently preserved. The framework enables reproducible high‐throughput quantification of gel microstructures and provides interpretable metrics that link microscopic organization to macroscopic functionality. These quantitative relationships enhance the understanding of how protein network architecture governs mechanical strength, water retention, and perceived smoothness in plant‐based gels. The integration of CLSM and deep learning thus establishes a scalable platform for data‐driven formulation and optimization of texture and stability in next‐generation food materials.