Uncertainty Driven Sampling to Handle Intra-class Imbalance Part Segmentation in Wheat

We introduce a novel method to address intra-class imbalance in 3D point cloud segmentation of wheat, focusing on distinguishing between ear and non-ear parts. Variability in plant structure, influenced by factors such as curvature and shape, often leads to data imbalance which complicates segmentation tasks. Our approach utilizes Monte Carlo Dropout to identify and prioritize uncertain samples at the end of each training epoch, employing uncertainty-driven sampling to select samples with the lowest confidence. These samples undergo augmentation through scaling and leaf crossover techniques, enhancing their representation in the training set. Our comparative evaluations demonstrate that this strategy significantly improves the mean Intersection over Union (mIoU) and segmentation accuracy, thereby increasing model robustness for complex 3D plant structures.