Case Study: Things To Be Considered for High-Throughput Phenotyping in Genomic Studies

Abstract

High-throughput phenotyping (HTP) enables breeders and researchers to have massive data sets accurately and objectively. It could be applied to plant breeding for screening stress tolerance and biodiversity among wild species in the gene bank, which can be a breakthrough in the phenotyping bottleneck. However, there are many factors to be considered. Thus, this study is designed to show an example of phenotyping traits using yield and image data in citrus using the Normalized Difference Vegetation Index (NDVI) and Red, Green, and Blue (RGB) images. The results using image analysis showed that R2 in linear regression ranged from 0.79 to 0.91, depending on the methods which were used in the current study. However, the results from NDVI were proven to be false, unlike those of RGB images. This means that researchers and breeders must be very cautious when dealing with new technologies to avoid being misled to the wrong conclusion when they try to associate this data with genomic data.