Pasture Coverage Segmentation Dataset

The current agricultural industry is facing challenges such as climate change, land resource constraints, and declining crop yields. Traditional pasture monitoring methods are inefficient and costly, failing to meet the needs of modern agriculture. Existing solutions often rely on manual observation, lacking systematization and precision. Therefore, this dataset aims to provide high-quality pasture coverage segmentation data to help researchers and developers build more accurate monitoring and management systems. Data collection is conducted using professional drone equipment to capture large-scale images under different seasons and climate conditions. To ensure data quality, multiple rounds of annotation and consistency checks have been implemented; all annotations have been reviewed by experts and stored in a standardized data storage format. Data is stored in JPEG format, organized into a structure that includes image files and corresponding mask files. The core advantage of this dataset lies in its high annotation accuracy and consistency, with an annotation precision of 95% ensuring data reliability. Additionally, a new deep learning annotation method has been introduced, improving annotation efficiency by about 30%. This dataset can effectively enhance the performance of pasture monitoring systems, increasing accuracy by over 15%, meeting the intelligent demands of modern agriculture.