Papaya Tree Recognition Dataset

The current agricultural sector faces issues of low efficiency in crop recognition and management, especially against the backdrop of the growing development of smart agriculture. Traditional manual recognition methods are unable to meet the rapidly changing needs. Existing solutions often rely on image data from a single environment, lacking diversity and universality, which leads to poor model generalization. The Papaya Tree Recognition Dataset aims to provide rich data support by including images of papaya trees in different environments, aiding the automatic recognition and classification of AI models. Data collection used high-resolution cameras, with images taken under various lighting and climate conditions to ensure data diversity and representativeness. To ensure data quality, a multi-round annotation and expert review process was adopted for quality control, ensuring annotation consistency and accuracy. Data is stored in JPEG format, organized by environment and category for easy use and analysis.