Grape Planting Row Detection Dataset

The current agricultural sector faces challenges in efficient planting and management, particularly in grape cultivation, where traditional manual detection methods are often inefficient and prone to errors. Existing solutions largely rely on manual inspection, lacking intelligent technological support, resulting in resource waste and labor shortages. This dataset aims to assist AI systems in row detection and drone navigation by providing high-quality annotated images, meeting the urgent demand for automation and intelligence in modern agriculture. Data collection uses high-resolution cameras under various lighting and weather conditions to ensure image diversity and representativeness. For data quality control, multiple annotation rounds and consistency checks are employed to ensure annotation accuracy exceeds 95%. Data storage uses JPEG format, organized in a folder structure, facilitating subsequent processing and analysis.