Cabbage Phenotype Feature Dataset

The current agriculture industry faces challenges in crop growth monitoring and assessment, particularly in monitoring the health status of crops like cabbage. Traditional methods often rely on manual detection, which is inefficient and prone to errors. Existing solutions are mostly image-based monitoring technologies, but they lack in data annotation accuracy and coverage. This dataset aims to provide high-quality cabbage phenotype features to achieve more accurate crop health evaluations through target detection technology. The dataset structure includes image data along with its corresponding labels and bounding box information. Data collection uses high-resolution cameras under various lighting conditions inside and outside greenhouses to ensure data diversity and representativeness. For quality control, a multi-round annotation process and expert review mechanism are adopted to enhance annotation consistency and accuracy. The data storage format is JPG, with each image corresponding to an annotation file to facilitate subsequent training and evaluation.