HSIFreezeNet: a lightweight random forest assisted framework for hyperspectral segmentation of camelina freezing damage

Freezing damage quantification is essential for understanding phenotypic-genotypic relationships, yet automated, scalable framework exists for pixel-wise freeze damage segmentation from hyperspectral imagery remain limited. Current assessments rely on visual inspection, which is subjective and time-consuming. Hyperspectral data, while rich in information, are high-dimensional and large in size, making pixel-wise manual annotation labor-intensive and slow. Deep learning models further increase computational demands when trained on large datasets. To overcome these challenges, we proposed a pseudo-labeling segmentation pipeline in which a random forest model trained on sparse region-of-interest annotation generates full-image masks for training deep learning architectures, substantially reducing labeling effort relative to dense pixel-wise annotation. Manual annotation covered 57 of 442 plants, from which dense masks were generated for the full dataset. We benchmarked thirteen state-of-the-art models and introduced a lightweight architecture, HSIFreezeNet-V1. On a plant-grouped held-out test partition, UPerEfficientNet-B7 achieved the highest mean IoU (0.785 ± 0.005), while HSIFreezeNet-V1 achieved 0.752 ± 0.018 using 759-fold fewer parameters and requiring 71% less inference time on an NVIDIA Jetson AGX Orin (2.78 s versus 9.57 s per hyperspectral cube). To assess accuracy beyond agreement with machine-generated labels, we additionally evaluated all architectures against an independent expert-annotated test set, providing a direct measure of pseudo-label quality. Preliminary qualitative transfer to strawberry and barley was also observed. These findings show that lightweight models trained on pseudo-labeled hyperspectral data can enable rapid quantitative damage assessment on embedded hardware, while independent expert evaluation provides a realistic measure of practical segmentation performance.