基于YOLO11的玉米播种粒距检测方法研究OA

Seed spacing is a key parameter in planting operations.To address the problems of time lag and low accuracy associated with traditional manual seed spacing measurement,a maize field seed spacing detection method based on YO-LO11 object detection technology is proposed.First,a high-speed color camera is used to acquire a large number of images of maize seeds placed on the soil surface during sowing,and the maize seeds in these images are manually anno-tated using the labeling tool Label Studio.Then,a pre-trained YOLO11 model is trained to obtain a visual detection model for maize seeds in field conditions.Finally,the trained model is deployed on the embedded edge computing plat-form Jetson AGX Orin NX,enabling real-time detection of maize seed spacing in the field.Test results show that when operating at a speed of 5 km/h,the average error of measured seed spacing is less than 3 cm.This method meets the re-quirements for real-time detection of seed spacing during field sowing,enables online monitoring of corn sowing quali-ty,and provides technical support for intelligent and precise sowing operations.