A dataset of images for indoor soybean survey

<p indent="0mm">Breeding superior varieties is an important guarantee for enhancing the quality and yield of soybeans. However, traditional soybean phenotyping methods are often time-consuming, labor-intensive, and prone to data, which is a far cry from the one-stop high-throughput acquisition of soybean phenotypic information. Therefore, building a dataset of images for indoor soybean survey is very important for developing methods for high-throughput and accurate phenotypic data acquisition and for further paving the way for automated and intelligent soybean survey. This dataset consists of three parts: original image data, annotation file data, and test image data. It features in-vitro pods and plants of typical soybean varieties, in which the former includes non-overlapping in-vitro pods and overlapping ones, and the latter includes plants of single branch, double branches and complex branches. The annotation file data includes instance segmentation labeling of main stems in soybean plants, detection box labeling of main stem nodes, detection box labeling of in-body pods, and the labeling of instance segmentation and detection box of the in-vitro pods. Moreover, the horizontal and rotation boxes are employed regarding the detection box labeling of soybean plant pods, and in-vitro pods, with a total amount of 4.2 GB. The models trained and validated on this dataset have demonstrated excellent detection and segmentation performance across various metrics, effectively handling non-overlapping and overlapping in-vitro pods, as well as soybean plant structures. This dataset can provide foundational image data for in-vitro pod detection, in-body pod detection and plant morphological analysis of object detection and instance segmentation models. Furthermore, it is of great value for advancing the research on intelligent soybean survey for the one-stop high-throughput acquisition of soybean key information.