Strawberry Defect Detection Dataset

The current agriculture industry faces the challenge of fruit quality control, especially in large-scale planting environments. Traditional manual inspection is inefficient and prone to misjudgment. Existing solutions often rely on human labor, making real-time monitoring and efficient management impossible. Therefore, the establishment of a strawberry defect detection dataset aims to improve the accuracy and efficiency of defect recognition through AI technology. This dataset includes a variety of defect features in strawberries, providing ample samples to support training of deep learning models. Data collection is conducted using professional cameras in greenhouse and field environments to ensure diversity and representativeness of images. Quality control measures include multiple rounds of annotation and expert review to ensure data annotation accuracy and consistency. The data storage format is JPEG, organized by label and time for easy access and processing.