Proximal sensing for geometric characterization of vines: A review of the latest advances

Abstract

Several variables, including a rising human population, varying weather patterns in the context of ongoing climate change, and the rapid worldwide spread of epidemics, all contribute to boosting agricultural demand. To assure food availability, quality, and safety while increasing yields and profitability, precision agriculture must progress swiftly. Precision viticulture aims to optimize vineyard management in this setting by reducing resource consumption and environmental impact while simultaneously enhancing the yield, product quality, and oenological potential of vineyards. This comprehensive review article offers an overview of the real-world and laboratory applications of optical and non-optical sensors in precision viticulture for 3D modelling. Hence, there is a pressing need to track the development of crops at a wide range of spatial and temporal scales, in a wide variety of environments, and for a wide range of objectives in a non-destructive manner. Due to the intrinsic spatial heterogeneity of vineyards, the adoption of precision viticulture necessitates crop monitoring using contactless and non-invasive sensors such as ultrasonic, LiDAR (Light Detection and Ranging), depth, or RGB cameras to prevent low accuracy and sparse sampling. This study aims to assist researchers in gaining a broad understanding of the sensing technologies for precision viticulture, the present problems, and the advancement of the state of the art. The study focuses on sensors used for Proximal Sensing to geometrically characterize vines using statically or dynamically ground-based measurements through a wide range of mobile sensing platforms. The employed sensors, data extraction, and analysis procedures are described. Moreover, the present and future potential of Proximal Sensing and Remote Sensing in vineyards is discussed.

Plant phenotyping relevance

ブドウ樹の形状を光学・非光学センサーで計測・3Dモデル化する近接センシング手法を中心に扱うレビューであり、植物フェノタイピング手法レビューに該当する。

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