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Related Experiment Video

Updated: Nov 27, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

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

Hugo Moreno^1,2, Victor Rueda-Ayala³, Angela Ribeiro²

¹Laboratorio de Propiedades Físicas (LPF_TRAGRALIA), ETSIAAB, Universidad Politécnica de Madrid, 28040 Madrid, Spain.

Sensors (Basel, Switzerland)

|December 8, 2020

Summary

This summary is machine-generated.

A non-destructive technique using a Microsoft Kinect v2 sensor accurately measured vine branch volume for predicting grape yield. This 3D sensing technology shows great potential for precision agriculture applications.

Keywords:

3D reconstruction Kinect v2 depth cameras vineyards woody crops

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Last Updated: Nov 27, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

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Published on: August 8, 2017

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

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Published on: October 5, 2016

Area of Science:

Agricultural Engineering
Computer Vision
Plant Phenomics

Background:

Advancements in 3D optical sensors like RGB-D cameras offer new possibilities for crop phenomics.
Non-destructive measurement techniques are crucial for improving phenotyping in agriculture.

Purpose of the Study:

To assess the utility of a Microsoft Kinect v2 sensor for non-destructively measuring vine geometric traits.
To evaluate the correlation between 3D vine volume and pruning weight (dry biomass) for yield prediction in vineyard crops.

Main Methods:

An adaptable mobile platform equipped with a Kinect v2 sensor collected depth images of grapevines.
3D point clouds of vine rows were generated under six different management cropping systems.
Vine branch volume was calculated and correlated with pruning weight and yield data.

Main Results:

Kinect-derived branch volume showed strong consistency with physical vine parameters (R² = 0.80 for pruning weight).
A good power law relationship was observed between measured volume and vineyard yield (R² = 0.87).
Inconsistent results for small details highlight limitations in current depth camera technology.

Conclusions:

The Kinect v2 system demonstrates significant potential as a low-cost, robust 3D sensor for proximal sensing in agricultural applications.
This non-destructive method aids in vineyard yield prediction, supporting strategic decision-making for growers.
Further development is needed to overcome limitations in capturing fine details for more precise individual treatment analysis.