Asparagus Quality Grading Dataset

The current agricultural industry faces challenges in asparagus grading and quality control, such as low efficiency and poor accuracy in manual grading. Existing solutions often rely on manual experience and lack standardization, leading to inconsistent grading results. This dataset aims to improve the application of AI in agricultural detection by establishing an asparagus grading system based on freshness, thickness, and color. The structure of the dataset includes fields such as images, sample ID, freshness, thickness, and color. Data collection uses professional cameras in a greenhouse environment to ensure the captured images are clear and distinguishable. Our quality control measures include multiple rounds of annotation and expert review to enhance the consistency and accuracy of annotations. The data will be stored in JPG format, organized by sample ID to ensure easy retrieval and use.