2016IFAC-PapersOnLineOpen access

Automated Measurement of Berry Size in Images**This work is supported in part by the National Grape and Wine Initiative and the US Department of Agriculture under grant number 20126702119958.

Omeed Mirbod, Luke Yoder, Stephen Nuske

Open full text 19 citations

Abstract

: Knowledge on berry size in grape vineyards can be a great asset for growers to help manage their crop whether for yield assessment or grape quality control. Having the ability to size berries of an entire field would allow growers to effectively monitor their vineyards at various stages of the growing season. Manual methods for determining berry size distribution of an entire field can be time consuming and rely on small sample sets which can lead to inaccuracies. This paper introduces an automated imaging system that measures diameter of grapes for every vine in an entire vineyard and generates a comprehensive map showing berry size variability which until now has not been available to growers. Believed to be the first example of mapping berry size across commercial vineyard blocks, this system uses computer vision techniques to locate and size the berries identifying submillimeter berry diameter differences. Maps of variability in berry size are shown to correlate with canopy size and yield. Diameter estimations are found to measure within 6% of manual measurements and a strong correlation is seen between estimated berry sizes and actual berry weights with r 2 = 0.96.

About this research paper

What this paper is about

: Knowledge on berry size in grape vineyards can be a great asset for growers to help manage their crop whether for yield assessment or grape quality control. Having the ability to size berries of an entire field would allow growers to effectively monitor their vineyards at various stages of the growing season. Manual methods for determining berry size distribution of an entire field can be time consuming and rely on small sample sets which can lead to inaccuracies. This paper introduces an automated imaging system that measures diameter of grapes for every vine in an entire vineyard and generates a comprehensive map showing berry size variability which until now has not been available to growers. Believed to be the first example of mapping berry size across commercial vineyard blocks, this system uses computer vision techniques to locate and size the berries identifying submillimeter berry diameter differences. Maps of variability in berry size are shown to correlate with canopy size and yield. Diameter estimations are found to measure within 6% of manual measurements and a strong correlation is seen between estimated berry sizes and actual berry weights with r 2 = 0.96.

Why it matters

OpenAlex reports 19 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

: Knowledge on berry size in grape vineyards can be a great asset for growers to help manage their crop whether for yield assessment or grape quality control. Having the ability to size berries of an entire field would allow growers to effectively monitor their vineyards at various stages of the growing season. Manual methods for determining berry size distribution of an entire field can be time consuming and rely on small sample sets which can lead to inaccuracies. This paper introduces an automated imaging system that measures diameter of grapes for every vine in an entire vineyard and generates a comprehensive map showing berry size variability which until now has not been available to growers. Believed to be the first example of mapping berry size across commercial vineyard blocks, this system uses computer vision techniques to locate and size the berries identifying submillimeter berry diameter differences. Maps of variability in berry size are shown to correlate with canopy size and yield. Diameter estimations are found to measure within 6% of manual measurements and a strong correlation is seen between estimated berry sizes and actual berry weights with r 2 = 0.96.

Key concepts: Berry, Vineyard, Vine, Wine grape, Canopy, Yield (engineering), Mathematics, Geography

Related papers

Back to paper searchBrowse research topicsOriginal source
Automated Measurement of Berry Size in Images**This work is supported in part by the National Grape and Wine Initiative and the US Department of Agriculture under grant number 20126702119958. — Research Paper | ScholarLens