Evaluation of Vineyard Cropping Systems Using On-Board RGB-Depth Perception

Moreno Párrizas, Hugo and Rueda-Ayala, Victor and Ribeiro Seijas, Angela and Bengochea-Guevara, Jose and Lopez, Juan and Peteinatos, Gerassimos and Valero Ubierna, Constantino and Andújar Sánchez, Dionisio (2020). Evaluation of Vineyard Cropping Systems Using On-Board RGB-Depth Perception. "Sensors", v. 20 (n. 23); p. 6912. ISSN 1424-8220. https://doi.org/10.3390/s20236912.

Description

Title: Evaluation of Vineyard Cropping Systems Using On-Board RGB-Depth Perception
Author/s:
  • Moreno Párrizas, Hugo
  • Rueda-Ayala, Victor
  • Ribeiro Seijas, Angela
  • Bengochea-Guevara, Jose
  • Lopez, Juan
  • Peteinatos, Gerassimos
  • Valero Ubierna, Constantino
  • Andújar Sánchez, Dionisio
Item Type: Article
Título de Revista/Publicación: Sensors
Date: 3 December 2020
ISSN: 1424-8220
Volume: 20
Subjects:
Freetext Keywords: depth cameras; Kinect v2; 3D reconstruction; woody crops; vineyards
Faculty: E.T.S. de Ingeniería Agronómica, Alimentaria y de Biosistemas (UPM)
Department: Ingeniería Agroforestal
UPM's Research Group: Técnicas Avanzadas en Agroalimentación LPF-TAGRALIA
Creative Commons Licenses: None

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (3MB) | Preview

Abstract

A non-destructive measuring technique was applied to test major vine geometric traits on measurements collected by a contactless sensor. Three-dimensional optical sensors have evolved over the past decade, and these advancements may be useful in improving phenomics technologies for other crops, such as woody perennials. Red, green and blue-depth (RGB-D) cameras, namely Microsoft Kinect, have a significant influence on recent computer vision and robotics research. In this experiment an adaptable mobile platform was used for the acquisition of depth images for the non-destructive assessment of branch volume (pruning weight) and related to grape yield in vineyard crops. Vineyard yield prediction provides useful insights about the anticipated yield to the winegrower, guiding strategic decisions to accomplish optimal quantity and efficiency, and supporting the winegrower with decision-making. A Kinect v2 system on-board to an on-ground electric vehicle was capable of producing precise 3D point clouds of vine rows under six diferent management cropping systems. The generated models demonstrated strong consistency between 3D images and vine structures from the actual physical parameters when average values were calculated. Correlations of Kinect branch volume with pruning weight (dry biomass) resulted in high coefficients of determination (R2 = 0.80). In the study of vineyard yield correlations, the measured volume was found to have a good power law relationship (R2 = 0.87). However due to low capability of most depth cameras to properly build 3-D shapes of small details the results for each treatment when calculated separately were not consistent. Nonetheless, Kinect v2 has a tremendous potential as a 3D sensor in agricultural applications for proximal sensing operations, benefiting from its high frame rate, low price in comparison with other depth cameras, and high robustness.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainAGL2017-83325-C4-3-RUnspecifiedCSICRYC-2016-20355

More information

Item ID: 65670
DC Identifier: http://oa.upm.es/65670/
OAI Identifier: oai:oa.upm.es:65670
DOI: 10.3390/s20236912
Official URL: https://www.mdpi.com/1424-8220/20/23/6912
Deposited by: Profesor Constantino Valero Ubierna
Deposited on: 09 Dec 2020 11:11
Last Modified: 09 Dec 2020 11:11
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM