Papaya Growth Stage Recognition Dataset

The current agriculture industry faces challenges such as inaccurate crop growth monitoring and low management efficiency. Traditional methods rely on manual observation, which can easily lead to errors and omissions. Existing solutions often lack specificity and immediacy, failing to meet the needs of modern precision agriculture. This dataset aims to assist AI models in recognizing crop status by providing comprehensive images of papaya growth stages, thus enhancing the intelligence level of agricultural management. The dataset is built on high-quality image acquisition using professional photographic equipment in natural growing environments. We implemented strict quality control, including multiple rounds of annotation, consistency checks, and expert reviews to ensure data accuracy and reliability. The data is stored in JPEG format, with a clear structure for further analysis and application.