Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields

12/15/2017
by   Ribana Roscher, et al.
0

The berry size is one of the most important fruit traits in grapevine breeding. Non-invasive, image-based phenotyping promises a fast and precise method for the monitoring of the grapevine berry size. In the present study an automated image analyzing framework was developed in order to estimate the size of grapevine berries from images in a high-throughput manner. The framework includes (i) the detection of circular structures which are potentially berries and (ii) the classification of these into the class 'berry' or 'non-berry' by utilizing a conditional random field. The approach used the concept of a one-class classification, since only the target class 'berry' is of interest and needs to be modeled. Moreover, the classification was carried out by using an automated active learning approach, i.e no user interaction is required during the classification process and in addition, the process adapts automatically to changing image conditions, e.g. illumination or berry color. The framework was tested on three datasets consisting in total of 139 images. The images were taken in an experimental vineyard at different stages of grapevine growth according to the BBCH scale. The mean berry size of a plant estimated by the framework correlates with the manually measured berry size by 0.88.

READ FULL TEXT

page 5

page 7

page 9

page 13

page 18

page 20

research
10/13/2021

High-throughput Phenotyping of Nematode Cysts

The beet cyst nematode (BCN) Heterodera schachtii is a plant pest respon...
research
09/30/2022

Automated Characterization of Catalytically Active Inclusion Body Production in Biotechnological Screening Systems

We here propose an automated pipeline for the microscopy image-based cha...
research
04/10/2013

Detecting Directionality in Random Fields Using the Monogenic Signal

Detecting and analyzing directional structures in images is important in...
research
10/23/2020

High-Throughput Image-Based Plant Stand Count Estimation Using Convolutional Neural Networks

The future landscape of modern farming and plant breeding is rapidly cha...
research
03/25/2016

An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection

This paper proposes a novel selective autoencoder approach within the fr...
research
06/23/2019

Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat

Crop production needs to increase in a sustainable manner to meet the gr...

Please sign up or login with your details

Forgot password? Click here to reset