A Review on High Throughput Phenotyping for Vegetable Crops

Abstract

Abstract Conventional phenotyping approaches for vegetable crops such as Solanaceae, Bulb, and Root crops have contributed significantly to the development of numerous varieties. Despite this, traditional phenotyping procedures are insufficient because of the longer time required to produce a variety, poor genetic gain, environmental influences, and other externalities that impact phenotype-based selection. A novel recent approach of high throughput phenotyping (HTP) is regarded a potential tool for addressing the problems of traditional phenotyping. The advent of sensor, computer vision, automation, and sophisticated machine learning technologies sparked the creation of high-throughput phenotyping technology in the prior decade. HTP platforms are being used to conduct non-destructive evaluations of the whole plant system in a variety of crops. HTP provides precise measurements and suggests the collection of high-quality and accurate data, which is required for standardizing phenotyping for genetic dissection and genomic assisted breeding techniques such as genome-wide association studies (GWAS), linkage mapping, marker-assisted selection (MAS), and genomic selection (GS). The rest of this chapter examines the application of high-throughput phenotyping tools in genomic-assisted breeding for vegetable crops.