Sweetpotato weevils (Cylas spp.) are considered among the most destructive pests impacting sweetpotato production, particularly in sub-Saharan Africa. Traditional methods for assessing weevil damage, predominantly relying on manual scoring, are labour-intensive, subjective, and often yield inconsistent results. These challenges significantly hinder breeding programs aimed at developing resilient sweetpotato varieties. This study introduces a computer vision-based approach for the automated evaluation of weevil damage in both field and laboratory contexts. In the field settings, we collected data to train classification models to predict root-damage severity levels, achieving a test accuracy of 71.43%. Additionally, we established a laboratory dataset and designed an object detection pipeline employing YOLO12, a leading real-time detection model. This methodology incorporated a two-stage laboratory pipeline that combined root segmentation with a tiling strategy to improve the detectability of small objects. The resulting model demonstrated a mean average precision of 77.7% in identifying minute weevil feeding holes. Our findings indicate that computer vision technologies can provide efficient, objective, and scalable assessment tools that align seamlessly with contemporary breeding workflows. These advancements represent a significant improvement in enhancing phenotyping efficiency within sweetpotato breeding programs and play a crucial role in mitigating the detrimental effects of weevils on food security.

翻译：甘薯象甲（Cylas spp.）被认为是影响甘薯生产最具破坏性的害虫之一，尤其在撒哈拉以南非洲地区。传统的象甲虫害评估方法主要依赖人工评分，不仅劳动密集、主观性强，且常产生不一致的结果。这些挑战严重阻碍了旨在培育抗性甘薯品种的育种计划。本研究提出了一种基于计算机视觉的方法，用于在田间和实验室环境下自动评估象甲虫害。在田间场景中，我们收集数据训练分类模型以预测根部损害严重程度，测试准确率达到71.43%。此外，我们建立了实验室数据集，并设计了一个采用领先实时检测模型YOLO12的目标检测流程。该方法结合了包含根部分割与切片策略的两阶段实验室流程，以提升小目标的可检测性。所得模型在识别微小象甲取食孔洞方面实现了77.7%的平均精度均值。我们的研究结果表明，计算机视觉技术能够提供高效、客观且可扩展的评估工具，与当代育种工作流程无缝衔接。这些进展显著提升了甘薯育种计划中的表型分析效率，并对减轻象甲虫害对粮食安全的不利影响起到关键作用。