A Systematic Review on the Advancements in Remote Sensing and Proximity Tools for Grapevine Disease Detection.

Abstract

Grapevines ( Vitis vinifera L.) are one of the most economically relevant crops worldwide, yet they are highly vulnerable to various diseases, causing substantial economic losses for winegrowers. This systematic review evaluates the application of remote sensing and proximal tools for vineyard disease detection, addressing current capabilities, gaps, and future directions in sensor-based field monitoring of grapevine diseases. The review covers 104 studies published between 2008 and October 2024, identified through searches in Scopus and Web of Science, conducted on 25 January 2024, and updated on 10 October 2024. The included studies focused exclusively on the sensor-based detection of grapevine diseases, while excluded studies were not related to grapevine diseases, did not use remote or proximal sensing, or were not conducted in field conditions. The most studied diseases include downy mildew, powdery mildew, Flavescence dorée , esca complex, rots, and viral diseases. The main sensors identified for disease detection are RGB, multispectral, hyperspectral sensors, and field spectroscopy. A trend identified in recent published research is the integration of artificial intelligence techniques, such as machine learning and deep learning, to improve disease detection accuracy. The results demonstrate progress in sensor-based disease monitoring, with most studies concentrating on specific diseases, sensor platforms, or methodological improvements. Future research should focus on standardizing methodologies, integrating multi-sensor data, and validating approaches across diverse vineyard contexts to improve commercial applicability and sustainability, addressing both economic and environmental challenges.

Plant phenotyping relevance

ブドウの病害という植物状態を対象に、リモートセンシング・近接センシングによる検出手法を体系的に評価するレビューであり、フェノタイピング手法が中心です。

Code and data availability

This is a systematic review of grapevine disease detection literature. The supplied blocks contain no public phenotype/trait datasets, plant/sensor imagery, author analysis code, or trained models specific to this paper. The Data Availability Statement only offers further inquiries via the corresponding author, and the

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