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

2026-02-06

AI Summary

This study addresses the limitations of traditional manual assessment of sweetpotato weevil damage, which relies on subjective judgment and suffers from low efficiency and poor consistency, thereby hindering progress in insect-resistant breeding. To overcome these challenges, the authors propose a computer vision approach integrating field and laboratory settings, leveraging YOLOv12 for object detection, image segmentation, and a tiling strategy to establish a two-stage laboratory detection pipeline that significantly enhances the identification of minute feeding holes. The method achieves 71.43% accuracy in classifying field-based damage severity levels and attains a mean average precision of 77.7% for detecting microscopic holes in controlled laboratory conditions. Notably, this work presents the first application of YOLOv12 to sweetpotato weevil phenotyping, enabling efficient, objective, and automated damage evaluation.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Hardware-aware ML
• Search and Optimization: Evolutionary Computation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• sweetpotato weevil
• damage assessment
• manual scoring
• breeding programs
• phenotyping

Innovation

• computer vision
• object detection
• YOLOv12
• image segmentation
• phenotyping