FLDO-LKNet: An Efficient Method for Segmenting Stone Cells in Rubber Tree Bark with Robustness to Staining Differences and Morphological and Scale Changes.

Stone cells are an important structural component of rubber tree bark and are closely associated with traits such as cracking propensity, bark hardness, stress tolerance and latex production. However, high-precision segmentation methods that are robust to staining variations, morphological diversity, and scale changes are still missing. To address these challenges in stone-cell histological images, such as the inconsistent staining intensity, the diverse shapes and sizes of cells, and the interference of cell debris, we proposed an automated semantic segmentation network called FLDO-LKNet, which formulates stone-cell delineation as a pixel-wise semantic segmentation task. Specifically, we introduce an LDFE module to recalibrate backbone features at the channel level, thereby mitigating the effects of staining differences. A KCFA attention mechanism is designed to better capture complex morphology and scale variation. In addition, we develop an FLDO optimization algorithm with a performance-feedback-based dynamic learning rate adjustment strategy to enhance robustness against training instability caused by debris interference. We further construct a dataset of 1084 stone-cell images collected from CATAS to support model training and evaluation. Experimental results demonstrate that FLDO-LKNet achieves 75.18% mIoU, 98.5% accuracy, and 82.21% sensitivity. Overall, as a dedicated semantic segmentation network, the proposed method enables high-precision pixel-level segmentation of stone cells, which may facilitate subsequent studies of stone-cell development and genetic functions and shows potential for agricultural applications.