UAV system to detect fungus disease in vineyards using MS Indices and YOLO

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Sensors Open Cite Open Share Article Open Access 6 January 2026 Direct UAV-Based Detection of Botrytis cinerea in Vineyards Using Chlorophyll-Absorption Indices and YOLO Deep Learning Guillem Montalban-Faet1 , Enrique Pérez-Mateo1 , Rafael Fayos-Jordan1 , Pablo Benlloch-Caballero1 , Aleksandr Lada2 , Jaume Segura-Garcia1,* and Miguel Garcia-Pineda1,* 1 Computer Science Department, ETSE—Universitat de València, 46100 Valencia, Spain 2 Knowledge Genesis Group, Smart Enterprise, Samara State Technical University, 443100 Samara, Russia * Authors to whom correspondence should be addressed. Sensors 2026, 26(2), 374; https://doi.org/10.3390/s26020374 This article belongs to the Special Issue AI-IoT for New Challenges in Smart Cities Version Notes Order Reprints 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.

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