Carrot Quality Grading Dataset

Currently, the agricultural sector faces challenges in standardization, low manual grading efficiency, and poor grading accuracy in the quality grading of agricultural products like carrots. Existing manual grading methods are not only time-consuming but also susceptible to human factors, leading to inconsistent grading results. This dataset aims to address the data needs in automated grading and improve the performance of intelligent grading systems by providing high-quality carrot images and their corresponding quality labels. Data collection is performed using high-resolution cameras in standardized agricultural environments, ensuring image clarity and consistency. For quality control, multiple rounds of labeling and expert review were implemented to ensure annotation accuracy. The data is stored in JPG format for quick reading and processing, organized by category. The core advantage of this dataset is its high labeling accuracy (95%), with consistency reaching up to 85%. We have adopted new data augmentation techniques, improving model generalization ability by 20%. With this dataset, manual grading time can be significantly reduced, thereby enhancing overall production efficiency and lowering labor costs.