Seedling Quantity Statistics Dataset

The current agriculture industry faces challenges in crop quantity monitoring and health assessment. Traditional methods rely on manual counting, which is inefficient and error-prone. Existing automatic detection technologies perform poorly in complex environments and lack efficient datasets for seedlings. This dataset aims to address accuracy and efficiency issues in target detection by providing high-quality seedling images and annotations. Data collection uses high-resolution cameras to capture seedlings in different environments, ensuring diversity and representativeness. We conduct multiple rounds of annotation and consistency checks to ensure data quality, and finally store it in JPEG format with clear structure for subsequent processing and analysis. The core advantage of this dataset is the annotation accuracy of over 95%, high data consistency, and coverage of various growth stages, enhancing detection algorithm performance and increasing processing speed by 30%.