Pumpkin Maturity Grading Dataset

The current agricultural industry faces challenges in quality control of crops, especially in judging the maturity of pumpkins. Traditional methods often rely on manual identification, which is inefficient and prone to errors. Although existing image recognition technology has made some progress, there is a lack of high-quality datasets specifically targeted at pumpkin maturity. This dataset aims to improve the accuracy of maturity assessment for researchers and developers by providing a large number of accurately labeled pumpkin images. Data collection is conducted using high-resolution cameras under natural light to ensure image clarity, and covers pumpkin samples from different growth environments. We conducted multiple rounds of labeling and consistency checks and invited domain experts for review to ensure data quality. Data is stored in JPEG format and organized by category. The dataset structure includes fields such as image paths, maturity levels, and collection dates, facilitating subsequent analysis and applications. The core advantage of this dataset lies in its labeling consistency and completeness, with annotation accuracy reaching over 95% and improving model robustness through innovative data augmentation techniques. Models utilizing this dataset have seen a 15% increase in accuracy for maturity identification tasks, effectively addressing quality control problems in agricultural production.