Machine Learning for Detection and Severity Estimation of Sweetpotato Weevil Damage in Field and Lab Conditions
This provides an efficient and objective tool for sweetpotato breeding programs, addressing food security issues in sub-Saharan Africa, though it is incremental as it applies existing computer vision methods to a new agricultural domain.
This study tackled the problem of labor-intensive and subjective assessment of sweetpotato weevil damage by developing a computer vision-based approach, achieving a test accuracy of 71.43% for severity classification in field conditions and a mean average precision of 77.7% for detecting feeding holes in lab conditions.
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.