IMAGE BASED EVALUATION FOR THE DETECTION OF CLUSTER PARAMETERS IN GRAPEVINE
Automated image interpretation is a powerful instrument for the acquisition of objective and precise phenotypic data with high throughput. Cluster length, cluster width, berry size and cluster compactness are four important phenotypic traits with impact on cluster morphology, health status and yield. For the image-based evaluation of this grapevine cluster morphology traits, the automated Cluster Analysis Tool (CAT) was developed in Matlab®. The comparison of precise reference measurements with CAT ratings on 100 cluster of Riesling and Pinot Noir showed a significant correlation of r=0.94 (0.97) for cluster width, r=0.90 (0.95) for cluster length and r=0.61 (0.23) for berry size. Variation of compactness could be detected in a crossing population calculating a compactness factor. To assess grapevine cluster morphology traits under laboratory conditions the automated image interpretation tool CAT presents a fast and user-friendly tool. The present study provides an improved and relevant phenotyping method for grapevine breeding. It could also be applied in genetic and ampelographic studies.
Kicherer, A., Roscher, R., Herzog, K., Förstner, W. and Töpfer, R. (2015). IMAGE BASED EVALUATION FOR THE DETECTION OF CLUSTER PARAMETERS IN GRAPEVINE. Acta Hortic. 1082, 335-340
automation, cluster morphology, cluster analysis tool, high-throughput phenotyping, image interpretation, Matlab