Direct UAV-Based Detection of Botrytis cinerea in Vineyards Using Chlorophyll-Absorption Indices and YOLO Deep Learning.

Abstract

The transition toward Agriculture 5.0 requires intelligent and autonomous monitoring systems capable of providing early, accurate, and scalable crop health assessment. This study presents the design and field evaluation of an artificial intelligence (AI)-based unmanned aerial vehicle (UAV) system for the detection of Botrytis cinerea in vineyards using multispectral imagery and deep learning. The proposed system integrates calibrated multispectral data with vegetation indices and a YOLOv8 object detection model to enable automated, geolocated disease detection. Experimental results obtained under real vineyard conditions show that training the model using the Chlorophyll Absorption Ratio Index (CARI) significantly improves detection performance compared to RGB imagery, achieving a precision of 92.6%, a recall of 89.6%, an F1-score of 91.1%, and a mean Average Precision (mAP@50) of 93.9%. In contrast, the RGB-based configuration yielded an F1-score of 68.1% and an mAP@50 of 68.5%. The system achieved an average inference time below 50 ms per image, supporting near real-time UAV operation. These results demonstrate that physiologically informed spectral feature selection substantially enhances early Botrytis cinerea detection and confirm the suitability of the proposed UAV-AI framework for precision viticulture within the Agriculture 5.0 paradigm.

Plant phenotyping relevance

UAVマルチスペクトル画像とYOLOモデルにより、ブドウ樹の病害状態を直接検出する手法を設計・実地評価しており、植物フェノタイピング手法が中心である。

Code and data availability

The paper's UAV multispectral vineyard dataset and YOLOv8 analysis code are not publicly deposited; the Data Availability Statement says raw data are available only on request. All URLs cited (MicaSense SDK, Rasterio, Index Database, Roboflow, Flask) are generic third-party tools/references, not paper-specific assets.

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