Cabbage Yield Prediction Dataset

Current agricultural production faces challenges such as inaccurate yield predictions and resource wastage, especially in crop growth monitoring. Most existing solutions rely on human experience and lack scientific quantitative data support, leading to decision errors. This dataset aims to solve the issue of cabbage yield prediction through high-quality images and precise annotations to meet the needs of smart agriculture. Data collection is conducted using high-resolution cameras in real farmland environments, covering different growth stages of cabbage. We have implemented multiple rounds of annotation and expert review as part of our quality control measures to ensure the accuracy and consistency of the data. Data storage is in JPG format, with each image corresponding to a metadata file, facilitating subsequent analysis and model training.