An Improved Method for Measuring Quantitative Resistance to the Wheat PathogenZymoseptoria triticiUsing High-Throughput Automated Image Analysis

Abstract

Zymoseptoria tritici causes Septoria tritici blotch (STB) on wheat. An improved method of quantifying STB symptoms was developed based on automated analysis of diseased leaf images made using a flatbed scanner. Naturally infected leaves (n = 949) sampled from fungicide-treated field plots comprising 39 wheat cultivars grown in Switzerland and 9 recombinant inbred lines (RIL) grown in Oregon were included in these analyses. Measures of quantitative resistance were percent leaf area covered by lesions, pycnidia size and gray value, and pycnidia density per leaf and lesion. These measures were obtained automatically with a batch-processing macro utilizing the image-processing software ImageJ. All phenotypes in both locations showed a continuous distribution, as expected for a quantitative trait. The trait distributions at both sites were largely overlapping even though the field and host environments were quite different. Cultivars and RILs could be assigned to two or more statistically different groups for each measured phenotype. Traditional visual assessments of field resistance were highly correlated with quantitative resistance measures based on image analysis for the Oregon RILs. These results show that automated image analysis provides a promising tool for assessing quantitative resistance to Z. tritici under field conditions.

A fungal avirulence factor encoded in a highly plastic genomic region triggers partial resistance to septoria tritici blotch

“…27 cm and then opened, in the case of the 2 × 3 pot arrays, or completely removed in the case of the square pots, leaving the supporting bags intact in the latter case. For symptom quantification, second or third leaves were mounted on paper sheets, scanned with a flatbed scanner (CanoScan LiDE 220) and analysed using automated image analysis (Stewart et al ., 2016). Data analysis and plotting was performed using RS tudio v.1.0.143.…”

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

“…Issues with image acquisition and differentiating diseased vs. healthy areas There is subjectivity in determining the edges of some symptoms (Barbedo 2014;Stewart et al 2016). Leaves are not always flat causing perspective problems (Barbedo 2014), or require flattening (Clément et al 2015).…”

“…Optical sensors perform non-invasively and have been developed and used to support disease detection, classification and severity measurement. Precision agriculture and plant phenotyping for resistance breeding already benefit from these technologies (Fiorani and Schurr 2013;Kruse et al 2014;Stewart et al 2016;Mahlein et al 2018). Although other sensor-based methods of Fig.…”

The genetic basis of local adaptation for pathogenic fungi in agricultural ecosystems

“…Other contributors to its continuing emergence are a steady increase in fungicide resistance relative to other fungal pathogens (Cools & Fraaije ; Stewart et al . ) and an ability to quickly evolve virulence on resistant cultivars (Cowger et al . ; McDonald & Mundt ).…”