Automated image analysis framework for high-throughput determination of grapevine berry sizes using conditional random fields

Abstract

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. 1

Model-assisted phenotyping by digital images in papaya breeding program

“…Thus, fruit occlusion can decrease their visible area and make their detection in the image difficult. Fruit occlusion has been widely commented on in research studies of fruit detection through methodologies based on digital images (Aggelopoulou et al, 2011;Dorj et al, 2013;Payne et al, 2013;Roscher et al, 2014).…”

“…In apple trees, Zhou et al (2012) validated an algorithm used to estimate the production of light green and red fruits, and found correlations of 0.80 and 0.85 compared to manual counting. In vines, Roscher et al (2014) developed an algorithm for estimating the number and size of berries at different development stages. The algorithm identified the berries with an average difference in diameter of 1 mm and a correlation of 0.88 compared to measurements taken using a caliper.…”

“…The authors obtained similar average results for measurements performed manually. Algorithms of digital image processing have been validated with a high degree of accuracy to estimate the number of commercial fruits in apple-tree (Aggelopoulou et al, 2011), mango (Payne et al, 2013) and vine (Roscher et al, 2014). Likewise, processing algorithms have been validated to estimate length, diameter and volume of fruit in watermelon (Koc, 2007), orange (Khojastehnazhand et al, 2009) and cantaloupe (Rashidi et al, 2009).…”

“…It is expected that the application of these methodologies allows for high performance phenotyping in order to increase the number of assessed genotypes, improving the acquisition and analysis of data and minimizing the experimental error (Li et al, 2014;Roscher et al, 2014). A methodology based on digital image analysis was validated by Ferreira et al (2012) to measure plant height, stem base diameter and diameter at breast height in Australian red cedar (Toona ciliata).…”

Detection of Disease Symptoms on Hyperspectral 3d Plant Models

“…A common application for analyzing 3D point clouds of plants is the segmentation of a plant into different organs like leaves, stems and fruits like berries (Paulus et al, 2013b;Roscher et al, 2014).…”