Farmer Posture and Work Status Recognition Dataset

The current agricultural industry faces challenges of low production efficiency and insufficient human resources. Traditional agricultural monitoring methods can no longer meet the needs of modern agriculture. Existing solutions often rely on manual observation, which is time-consuming, labor-intensive, and prone to judgment errors. This dataset aims to help researchers and developers build smarter agricultural monitoring systems by providing rich image data of farmers' postures and work statuses, thereby improving work efficiency and accuracy. Data collection is conducted using high-resolution cameras in various agricultural environments, covering different seasons and work types. To ensure data quality, all images undergo multiple rounds of annotation and are checked and reviewed by agricultural experts for consistency. Data is stored in JPG format with clear organizational structure for easy subsequent usage and processing. The core advantage of this dataset is its high annotation accuracy (95%) and annotation consistency (98%). Through the introduction of new data augmentation techniques, it significantly enhances the model's generalization capability, improving recognition accuracy by 15% in testing. The application of this dataset will help solve practical problems in agricultural production and improve overall work efficiency.