AgriScout: AI-powered robot for precise detection of PVY-infected potato plants

Abstract

The early detection of plant diseases is critical for ensuring optimal crop health and maximizing yield. This study presents an AI-driven autonomous robotic system, “AgriScout”, designed for the early identification and mapping of Potato Virus Y (PVY) infections in potato crops. The developed system integrates an electric field robot equipped with RGB cameras and a GPS-RTK module for precise image capture and geolocation of infected plants. The collected high-resolution images are transmitted to a cloud-based server, where a YOLO (You Only Look Once) deep learning model processes them to detect PVY-infected plants. The system generates an infestation map with accurate geospatial coordinates of affected areas, facilitating targeted intervention. Field trials were conducted in Prince Edward Island, Canada, to develop a labeled dataset comprising healthy and PVY-infected plants across different growth stages and environmental conditions. The YOLO model was trained and validated using this dataset, achieving a mean Average Precision (mAP@0.5) of 85%, an F1-score of 0.80, a Precision of 0.85, and a Recall of 0.76 during testing. The model demonstrated robust detection capabilities under varying foliage densities, effectively distinguishing infected plants with high accuracy. The results underscore the potential of “AgriScout” as a scalable, real-time disease detection solution for precision agriculture. By automating disease monitoring and reducing reliance on manual scouting, the system enhances farm productivity, minimizes yield losses, and supports sustainable disease management practices. The integration of robotics and AI in pathogen detection represents a significant advancement in agricultural automation, paving the way for intelligent, data-driven decision-making in modern farming systems.

Plant phenotyping relevance

植物のPVY感染状態を画像から検出・地理化するロボット撮像とYOLO解析が研究の中心であり、データセット作成とモデル検証も行っているため、病害表現型の計測手法として適格。

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