Cabbage Disease Spot Detection Dataset

The current agricultural industry faces challenges of low efficiency and insufficient accuracy in crop disease detection. Traditional manual detection methods are not only time-consuming and labor-intensive but also prone to misjudgment and omission. Existing automated detection systems often rely on simple image processing techniques, which cannot meet the demand for high precision. This dataset aims to assist researchers and developers in training more precise deep learning models by providing a large number of high-quality cabbage disease spot images, thereby improving the accuracy and efficiency of disease detection. The dataset construction process includes collecting images from multiple agricultural experimental bases using high-resolution cameras under natural lighting conditions to ensure image clarity and authenticity. The data annotation process involved multiple rounds of annotation and expert review to ensure consistency and accuracy. The data is stored in JPEG format and organized in an orderly manner according to image ID for easy subsequent use and retrieval.