Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation

Abstract

Vineyard yield estimation provides the winegrower with insightful information regarding the expected yield, facilitating managerial decisions to achieve maximum quantity and quality and assisting the winery with logistics. The use of proximal remote sensing technology and techniques for yield estimation has produced limited success within viticulture. In this study, 2-D RGB and 3-D RGB-D (Kinect sensor) imagery were investigated for yield estimation in a vertical shoot positioned (VSP) vineyard. Three experiments were implemented, including two measurement levels and two canopy treatments. The RGB imagery (bunch- and plant-level) underwent image segmentation before the fruit area was estimated using a calibrated pixel area. RGB-D imagery captured at bunch-level (mesh) and plant-level (point cloud) was reconstructed for fruit volume estimation. The RGB and RGB-D measurements utilised cross-validation to determine fruit mass, which was subsequently used for yield estimation. Experiment one's (laboratory conditions) bunch-level results achieved a high yield estimation agreement with RGB-D imagery (r 2 = 0.950), which outperformed RGB imagery (r 2 = 0.889). Both RGB and RGB-D performed similarly in experiment two (bunch-level), while RGB outperformed RGB-D in experiment three (plant-level). The RGB-D sensor (Kinect) is suited to ideal laboratory conditions, while the robust RGB methodology is suitable for both laboratory and in-situ yield estimation.

Plant phenotyping relevance

ブドウの収量という植物形質を、RGB/RGB-D画像、画像分割、3D再構成、校正、交差検証で推定する手法を比較・評価しており、フェノタイピング手法が中心です。

Code and data availability

The paper describes custom RGB segmentation (MATLAB) and R/alphashape3d point-cloud volume scripts, but no public deposit, availability statement, or authors' URL for the imagery, phenotype data, or code is provided. All URLs cited are generic third-party software tools (Kinect SDK, RTAB-Map, MATLAB, CloudCompare, alph

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