Abstract:Efficient and accurate monitoring of maize ear height (EH) is critical for anti-lodging breeding. The traditional manual measurement approach is labor-intensive and time-consuming, while existing automated approaches often lack robustness under varying field conditions or involve high costs. To address these limitations, an iOS application (APP) was developed based on the you only look once (YOLO) model and augmented reality (AR) technology for real-time, accurate, efficient, and low-cost maize EH measurement. It comprised two modules: a maize ear detection model and a height measurement module. The ear detection model was trained and validated on a dataset comprising 1000 field images collected from maize fields during the filling stage, under various lighting and occlusion conditions. Among different object detection models, the YOLO v5s model demonstrated the most robust performance with a precision of 0.844, a recall of 0.724, and an AP0.5 of 0.814. The trained detection model had been integrated into a maize EH measurement system, which utilized the AR technology for real-time measurement. It demonstrated excellent compatibility and performance on iOS devices, with response time below 0.3 s. Field evaluation results indicated a high correlation between the EH measured by the app and manual measurements (R2=0.750~0.864, RMSE=0.10~0.13m). The app was optimized for solo operation. To finish measuring a plot with over 10 maize plants only took less than 2 minutes, which was over 6 times faster than that of the traditional measurement with the leveling rod. This app significantly improved the efficiency of maize EH measurements while maintaining accuracy, providing real-time and precise data support for field management and breeding programs.
