Advanced Imaging for Quantitative Evaluation of Aphanomyces Root Rot Resistance in Lentil - Marzougui, Afef; Ma, Yu; Zhang, Chongyuan; McGee, Rebecca J.; Coyne, Clarice J.; Main, Dorrie; Sankaran, Sindhuja (Frontiers in Plant Science, 2019)
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 12 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 10 NDSIs for lentil root sections – computed from wavelengths in the range of 630–670; 700–840; 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. 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 red edge index (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.