Comparative Analysis of Base-Width-Based Annotation Box Ratios for Vine Trunk and Support Post Detection Performance in Agricultural Autonomous Navigation Environments

Abstract

AI-driven agricultural automation increasingly demands efficient data generation methods for training deep learning models in autonomous robotic systems. Traditional bounding box annotation methods for agricultural objects present significant challenges including subjective boundary determination, inconsistent labeling across annotators, and physical strain from extensive mouse movements required for elongated objects. This study proposes a novel base-width standardized annotation method that utilizes the base width of a vine trunk and a support post as a reference parameter for automated bounding box generation. The method requires annotators to specify only the left and right endpoints of object bases, from which the system automatically generates standardized bounding boxes with predefined aspect ratios. Performance assessment utilized Precision, Recall, F1-score, and Average Precision metrics across vine trunks and support posts. The study reveals that vertically elongated rectangular bounding boxes outperform square configurations for agricultural object detection. The proposed method is expected to reduce time consumption from subjective boundary determination and minimize physical strain during bounding box annotation for AI-based autonomous navigation models in agricultural environments. This will ultimately enhance dataset consistency and improve the efficiency of artificial intelligence learning.