Seedling and Soil Segmentation Dataset

The current agricultural industry faces challenges such as low crop monitoring efficiency and inaccurate soil quality analysis. Existing solutions often rely on manual observation, which is inefficient and prone to errors. This dataset aims to address the issue of insufficient image segmentation accuracy by providing high-quality images of seedling and soil segmentation, supporting precision agriculture and intelligent crop management. The dataset contains 15,000 high-resolution images collected using high-performance cameras in various environments and lighting conditions, ensuring data diversity and representativeness. Quality control includes multiple rounds of annotation, annotation consistency checks, and expert reviews to ensure high data quality. The data is stored in JPG format, and mask data is organized in JSON format, facilitating subsequent deep learning training and analysis.