Machine Learning for Detection and Severity Estimation of Sweetpotato Weevil Damage in Field and Lab Conditions

Doreen M. Chelangat; Sudi Murindanyi; Bruce Mugizi; Paul Musana; Benard Yada; Milton A. Otema; Florence Osaru; Andrew Katumba; Joyce Nakatumba-Nabende

arXiv · 2602.06786

Machine Learning for Detection and Severity Estimation of Sweetpotato Weevil Damage in Field and Lab Conditions

Doreen M. Chelangat·Sudi Murindanyi·Bruce Mugizi·Paul Musana·Benard Yada·Milton A. Otema·Florence Osaru·Andrew Katumba·Joyce Nakatumba-Nabende

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Abstract

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.

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Doreen M. Chelangat, Sudi Murindanyi, Bruce Mugizi, Paul Musana, Benard Yada, Milton A. Otema, Florence Osaru, Andrew Katumba, Joyce Nakatumba-Nabende. 2026-02-06. Machine Learning for Detection and Severity Estimation of Sweetpotato Weevil Damage in Field and Lab Conditions. https://arxiv.org/abs/2602.06786

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