Grape Destemming Recognition Dataset

The current agriculture industry faces challenges of labor shortages and efficient management, especially in orchard operations where traditional manual destemming methods are inefficient and prone to errors. Existing solutions largely rely on manpower, lacking automation, leading to high production costs and low efficiency. The Grape Destemming Recognition Dataset aims to provide high-quality image data to help develop automated destemming systems based on computer vision, to improve efficiency and reduce costs. The dataset structure includes a variety of grape images, covering different growth stages and environmental conditions. Data collection uses high-resolution cameras in different orchard environments to ensure diversity coverage. Quality control includes multiple rounds of annotation and expert review to improve consistency and accuracy of annotations. All data is stored in JPG format and categorized by type and environment.