Tomato Growth Stage Monitoring Dataset

The current agricultural industry faces challenges in crop growth monitoring, especially in how to timely identify crop growth stages to optimize management. Existing monitoring solutions often rely on manual observation, which is inefficient and prone to errors, failing to meet the needs of modern agriculture for precise management. This dataset aims to help researchers and developers train object detection models through high-quality image data, thereby achieving automatic recognition and monitoring of tomato growth stages. Data collection is conducted with high-resolution cameras under natural light conditions to ensure image clarity and authenticity. Each image is annotated through multiple rounds, with annotators required to label according to guidelines and experts conducting consistency checks to ensure annotation accuracy and reliability. Data is stored in JPG format and organized into folders by growth stage for easy retrieval and use.