Intelligent crop growth data acquisition and processing system based on K210 and YOLOv5

Smart agriculture is the integration of technological innovation and agriculture. Currently, an important issue to be addressed is how to use technological means to understand the growth status of crops in real time. We propose a target detection method based on the YOLOv5 model and integrated with k210, combined with crop features (such as the number of leaves, plant size, color changes, etc.), to achieve real-time classification and health status assessment of crop growth.