Automated estimation of leaf area development in sweet pepper plants from image analysis

Abstract

High-throughput automated plant phenotyping has recently received a lot of attention. Leaf area is an important characteristic in understanding plant performance, but time-consuming and destructive to measure accurately. In this research, we describe a method to use a histogram of image intensities to automatically measure plant leaf area of tall pepper (Capsicum annuum L.) plants in the greenhouse. With a device equipped with several cameras, images of plants were recorded at 5-cm intervals over a height of 3 m, at a recording distance of less than 60 cm. The images were reduced to a small set of principal components that defined the design matrix in a regression model for predicting manually measured leaf area as obtained from destructive harvesting. These regression calibrations were performed for six different developmental times. In addition, development of leaf area was investigated by fitting linear relations between predicted leaf area and time, with special attention given to the genotype by time interaction and its genetic basis in the form of quantitative trait loci (QTLs). The experiment comprised parents, F1 progeny and eight genotypes of a recombinant inbred population of pepper. Although the current trial contained a limited number of genotypes, an earlier identified QTL related to leaf area growth could be confirmed. Therefore, image analysis, as presented in this paper, provides a powerful and efficient way to study and identify the genetic basis of growth and developmental processes in plants.