Strawberry Quality Grading Dataset

The current agricultural industry faces inefficiency and high labor costs in grading the quality of fruits and vegetables, especially with strawberries, which are highly perishable. Traditional grading methods often fail to meet market demands. Existing solutions present limitations such as inconsistent labeling and unclear classification standards, resulting in poor grading performance. This dataset aims to establish a scientific grading standard by annotating strawberries based on size, color, and defects, supporting AI applications in post-harvest sorting and e-commerce grading. Data is collected using high-resolution cameras in a greenhouse environment, ensuring that all strawberry features are clearly visible. For quality control, a combination of multiple rounds of annotation and expert review is employed to ensure accuracy and consistency of the annotation results. Data is stored in JPG format, organized into category folders for ease of future use.