SemanticSugarBeets: A Multi-Task Framework and Dataset for Inspecting Harvest and Storage Characteristics of Sugar Beets

SemanticSugarBeets is a comprehensive dataset and framework designed for analyzing post-harvest and post-storage sugar beets using monocular RGB images. It supports three key tasks: instance segmentation to identify and delineate individual sugar beets, semantic segmentation to classify specific regions of each beet (e.g., damage, soil adhesion, vegetation, and rot) and oriented object detection to estimate the size and mass of beets using reference objects. The dataset includes 952 annotated images with 2,920 sugar-beet instances, captured both before and after storage. Accompanying the dataset is a demo application and processing code, available on GitHub. For more details, refer to the paper presented at the Agriculture-Vision Workshop at CVPR 2025. Annotations and Learning Tasks The dataset supports three primary learning tasks, each designed to address specific aspects of sugar-beet analysis: Instance SegmentationDetect and delineate entire sugar-beet instances in an image. This task provides coarse-grained annotations for identifying individual beets, which is useful for counting and localization. Semantic SegmentationPerform fine-grained segmentation of each beet instance to classify its regions into specific categories relevant to quality assessment, such as: Beet: healthy, undamaged beet surfaces Cut: areas where the beet has been topped or trimmed Leaf: residual vegetation attached to the beet Soil: soil adhering to the beet's surface Damage: visible damage on the beet Rot: areas affected by rot Oriented Object DetectionDetect and estimate the position and orientation of reference objects (folding-ruler elements and plastic signs) within the image. These objects can be used for scale estimation to calculate the absolute size and mass of sugar beets. Data Structure and Formats The dataset is organized into the following directories: images: contains all RGB images in .jpg format with a resolution of 2120x1192 pixels, which correspond to the annotations in the instances and markers directories instances: annotations and split files used in instance-segmentation experiments: anno: instance contours for a single sugar-beet class in YOLO11 format train/val/test.txt: lists of image IDs for training, validation and testing markers: annotations and split files used in oriented-object-detection experiments: anno: oriented-bounding-box annotations for two classes of markers in YOLO11 format: 0: Ruler (folding-ruler element) 1: Sign (numbered plastic sign) train/val/test.txt: lists of image IDs for training, validation and testing segmentation: annotations, image patches and split files used in semantic-segmentation experiments: anno: single-channel segmentation masks for each individual beet, where pixel values correspond to the following classes: 0: Background 1: Beet 2: Cut 3: Leaf 4: Soil 5: Damage 6: Rot patches: image patches of individual sugar-beet instances cropped from the original images for convenience train/val/test.txt: lists of beet IDs for training, validation, and testing File Naming Convention File names of images and annotations follow this format:ssb-<group_id><side>[-<beet_id>] <group_id>: a 5-digit number (e.g., 00001) identifying the group of recorded sugar beets <side>: either a or b, indicating the same group of beets captured before (a) or after flipping (b) <beet_id>: a 3-digit number (e.g., 001) enumerating individual sugar beets within an image (used only for semantic segmentation) Example ssb-00001a: group ID 00001, side a ssb-00001a-001: group ID 00001, side a, beet instance 001 Citing If you use the SemanticSugarBeets dataset or source code in your research, please cite the following paper to acknowledge the authors' contributions:Croonen, G., Trondl, A., Simon, J., Steininger, D., 2025. SemanticSugarBeets: A Multi-Task Framework and Dataset for Inspecting Harvest and Storage Characteristics of Sugar Beets. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.