A variable-rate spraying system for vineyards based on RGB-D imaging and tensor acceleration

Abstract

• RGB-D camera and Jetson platform enable precise, adaptive vineyard spraying. • Canopy volume estimation reduces plant protection product use by 57.4%. • Real-time system adjusts spray rates for efficient, sustainable vineyard management. • Instance segmentation and 3D meshing provide accurate canopy and trellis detection. • Jetson’s parallel computing accelerates processing for fast, reliable results. Sustainable vineyard management requires precise and efficient application of plant protection products to minimise environmental impact while ensuring plant health. This study presents a variable-rate spraying system that integrates an RGB-D camera with a GPU-equipped edge computing platform to enable accurate, real-time adjustment of spray flow rates in vineyards. A tensor-based representation of RGB-D data is employed to accelerate the entire processing pipeline. Based on this structure, a fast approximate meshing method is applied to rapidly generate 3D meshes from point clouds. To incorporate semantic information from RGB images, an instance segmentation model is used to detect grapevine canopies and trellis posts. The resulting canopy masks are used to isolate the canopy meshes, while the trellis posts serve as reference planes for canopy volume estimation via mesh projection. Based on the computed volume, pulse-width modulation signals are generated to dynamically control spray flow rates. Field experiments were conducted to evaluate the system’s effectiveness and real-time performance. The results demonstrated that the estimated canopy volume is a reliable indicator for regulating application rates. Compared to uniform-rate spraying, the proposed system reduced plant protection product consumption by 57.4% while ensuring adequate droplet coverage. Additionally, the system demonstrated satisfactory real-time performance even on entry-level hardware. Overall, the proposed variable-rate spraying system offers an accurate, real-time, and cost-effective solution for precision viticulture, highlighting its potential for commercial deployment in sustainable vineyard management.

Plant phenotyping relevance

RGB-D画像、インスタンスセグメンテーション、3Dメッシュによりブドウ樹冠を抽出し、樹冠体積という植物形質を推定する手法とリアルタイム基盤が研究の中心であるため。

Code and data availability

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