Agricultural Product Stall Image Classification Dataset

The current agricultural industry faces challenges such as inaccurate classification of agricultural products and asymmetric market information, leading to low efficiency in trade markets. Existing solutions often rely on manual labeling, which is inefficient and lacks labeling accuracy. This dataset aims to assist in developing more precise image classification algorithms by providing high-quality images of agricultural product stalls to improve the accuracy and efficiency of product recognition. The dataset includes stall images from different trade markets, captured using high-resolution cameras to ensure image clarity. Data collection is conducted under various lighting conditions and environments to enhance data diversity. To ensure data quality, quality control measures such as multiple rounds of labeling, consistency checks, and expert reviews are employed. Data is stored in JPG format, with a clear structure, facilitating subsequent processing and analysis.