Automated, image-based disease measurement for phenotyping resistance to soybean frogeye leaf spot

Samuel C McDonald; James Buck; Zenglu Li

doi:10.1186/s13007-022-00934-7

Automated, image-based disease measurement for phenotyping resistance to soybean frogeye leaf spot

Plant Methods. 2022 Aug 16;18(1):103. doi: 10.1186/s13007-022-00934-7.

Authors

Samuel C McDonald¹, James Buck², Zenglu Li³

Affiliations

¹ Institute of Plant Breeding, Genetics, and Genomics, and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, USA.
² Department of Plant Pathology, University of Georgia, Griffin, GA, USA.
³ Institute of Plant Breeding, Genetics, and Genomics, and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, USA. zli@uga.edu.

Abstract

Background: Frogeye leaf spot is a disease of soybean, and there are limited sources of crop genetic resistance. Accurate quantification of resistance is necessary for the discovery of novel resistance sources, which can be accelerated by using a low-cost and easy-to-use image analysis system to phenotype the disease. The objective herein was to develop an automated image analysis phenotyping pipeline to measure and count frogeye leaf spot lesions on soybean leaves with high precision and resolution while ensuring data integrity.

Results: The image analysis program developed measures two traits: the percent of diseased leaf area and the number of lesions on a leaf. Percent of diseased leaf area is calculated by dividing the number of diseased pixels by the total number of leaf pixels, which are segmented through a series of color space transformations and pixel value thresholding. Lesion number is determined by counting the number of objects remaining in the image when the lesions are segmented. Automated measurement of the percent of diseased leaf area deviates from the manually measured value by less than 0.05% on average. Automatic lesion counting deviates by an average of 1.6 lesions from the manually counted value. The proposed method is highly correlated with a conventional method using a 1-5 ordinal scale based on a standard area diagram. Input image compression was optimal at a resolution of 1500 × 1000 pixels. At this resolution, the image analysis method proposed can process an image in less than 10 s and is highly concordant with uncompressed images.

Conclusion: Image analysis provides improved resolution over conventional methods of frogeye leaf spot disease phenotyping. This method can improve the precision and resolution of phenotyping frogeye leaf spot, which can be used in genetic mapping to identify QTLs for crop genetic resistance and in breeding efforts for resistance to the disease.

Keywords: Frogeye leaf spot; Image analysis; ImageJ software; Phytopathometry; Plant disease; Soybean.