Tea Picking Labor Behavior Dataset

The current agricultural sector faces challenges such as high labor costs and low efficiency, especially in the tea picking process where traditional manual methods are limited in efficiency. Most existing automated picking solutions lack targeted behavior recognition capabilities, resulting in poor practical application results. This dataset aims to help researchers and developers improve the accuracy and efficiency of automated technology identification by providing high-quality tea picking labor behavior image data. Data collection is conducted with high-resolution cameras in real tea plantation environments to ensure diversity under various lighting and weather conditions. We adopt quality control measures such as multiple rounds of annotation and consistency checks to ensure data accuracy and reliability. The data is stored in JPEG format, accompanied by JSON files providing detailed annotation information, with a clear organization structure to facilitate subsequent processing and analysis.