Advanced Imaging for Quantitative Evaluation of Aphanomyces Root Rot Resistance in Lentil.

Afef Marzougui,Dorrie Main,Sindhuja Sankaran,Yu Ma,Chongyuan Zhang,Rebecca J Mcgee,Clarice J Coyne

doi:10.3389/fpls.2019.00383

Abstract

Aphanomyces root rot (ARR) is a soil-borne disease that results in severe yield losses in lentil. The development of resistant cultivars is one of the key strategies to control this pathogen. However, the evaluation of disease severity is limited to visual scores that can be subjective. This study utilized image-based phenotyping approaches to evaluate Aphanomyces euteiches resistance in lentil genotypes in greenhouse (351 genotypes from lentil single plant/LSP derived collection and 191 genotypes from recombinant inbred lines/RIL using digital Red-Green-Blue/RGB and hyperspectral imaging) and field (173 RIL genotypes using unmanned aerial system-based multispectral imaging) conditions. Moderate to strong correlations were observed between RGB, multispectral, and hyperspectral derived features extracted from lentil shoots/roots and visual scores. In general, root features extracted from RGB imaging were found to be strongly associated with disease severity. With only three root traits, elastic net regression model was able to predict disease severity across and within multiple datasets (R2 = 0.45–0.73 and RMSE = 0.66–1.00). The selected features could represent visual disease scores. Moreover, we developed twelve normalized difference spectral indices (NDSIs) that were significantly correlated with disease scores: two NDSIs for lentil shoot section – computed from wavelengths of 1170, 1160, 1270, and 1280 nm (0.12 ≤ |r| ≤ 0.24, P < 0.05) and ten NDSIs for lentil root sections – computed from wavelengths in the range of 630–670, 700–840, and 1320–1530 nm (0.10 ≤ |r| ≤ 0.50, P < 0.05). Root-derived NDSIs were more accurate in predicting disease scores with an R2 of 0.54 (RMSE = 0.86), especially when the model was trained and tested on LSP accessions, compared to R2 of 0.25 (RMSE = 1.64) when LSP and RIL genotypes were used as train and test datasets, respectively. Importantly, NDSIs – computed from wavelengths of 700, 710, 730, and 790 nm – had strong positive correlations with disease scores (0.35 ≤r ≤ 0.50, P < 0.0001), which was confirmed in field phenotyping with similar correlations using vegetation index with red edge wavelength (normalized difference red edge, 0.36 ≤ |r| ≤ 0.57, P < 0.0001). The adopted image-based phenotyping approaches can help plant breeders to objectively quantify ARR resistance and reduce the subjectivity in selecting potential genotypes.

Highlights

Lentil (Lens culinaris Medik.) is a leguminous crop grown worldwide that serves as an important source of protein for human consumption and animal feed (Infantino et al, 2006; Hamwieh et al, 2009)
The objectives of this study were to: (1) evaluate the relationship between disease visual scores and digital features extracted from RGB imaging and hyperspectral imaging (550–1700 nm) using two independent panels of lentil genotypes in greenhouse conditions; (2) develop models for selecting most relevant features for Aphanomyces root rot (ARR) severity; and (3) investigate the performance of unmanned aerial system (UAS)-based multispectral imaging in field conditions for ARR detection
The susceptibility of lentil to soil-borne pathogens such as A. euteiches could lead to severe losses in production (Gaulin et al, 2007)

Summary

Introduction

Lentil (Lens culinaris Medik.) is a leguminous crop grown worldwide that serves as an important source of protein for human consumption and animal feed (Infantino et al, 2006; Hamwieh et al, 2009). Soil-borne fungal diseases are one of the limiting factors negatively affecting plant development and seed yield in pulses (Gossen et al, 2016) These pathogens can attack their host at any stage causing great loss in yield (Infantino et al, 2006). The domestication of lentils has led to a loss in genetic diversity, including the loss of some important traits contributing to disease resistance (Ford et al, 1999; Khazaei et al, 2016) For these reasons, the development of disease-resistant cultivars is a critical need for crop protection against this pathogen (Ford et al, 1999; Infantino et al, 2006; Le May et al, 2017)

Objectives

Methods

Findings

Conclusion