Contour-Based Detection and Quantification of Tar Spot Stromata Using Red-Green-Blue (RGB) Imagery

Abstract

Quantifying symptoms of tar spot of corn has been conducted through visual-based estimations of the proportion of leaf area covered by the pathogenic structures generated by Phyllachora maydis (stromata). However, this traditional approach is costly in terms of time and labor, as well as prone to human subjectivity. An objective and accurate method, which is also time and labor-efficient, is of an urgent need for tar spot surveillance and high-throughput disease phenotyping. Here, we present the use of contour-based detection of fungal stromata to quantify disease intensity using Red-Green-Blue (RGB) images of tar spot-infected corn leaves. Image blocks (n = 1,130) generated by uniform partitioning the RGB images of leaves, were analyzed for their number of stromata by two independent, experienced human raters using ImageJ (visual estimates) and the experimental stromata contour detection algorithm (SCDA; digital measurements). Stromata count for each image block was then categorized into five classes and tested for the agreement of human raters and SCDA using Cohen's weighted kappa coefficient (κ). Adequate agreements of stromata counts were observed for each of the human raters to SCDA (κ = 0.83) and between the two human raters (κ = 0.95). Moreover, the SCDA was able to recognize “true stromata,” but to a lesser extent than human raters (average median recall = 90.5%, precision = 89.7%, and Dice = 88.3%). Furthermore, we tracked tar spot development throughout six time points using SCDA and we obtained high agreement between area under the disease progress curve (AUDPC) shared by visual disease severity and SCDA. Our results indicate the potential utility of SCDA in quantifying stromata using RGB images, complementing the traditional human, visual-based disease severity estimations, and serve as a foundation in building an accurate, high-throughput pipeline for the scoring of tar spot symptoms.

Monitoring tar spot disease in corn at different canopy and temporal levels using aerial multispectral imaging and machine learning

“…In addition, it is also time-consuming and costly to train personnel and improve visual assessment accuracy. Digital phenotyping technologies offer an opportunity to enhance the objectivity and efficiency of plant disease detection and quantification ( Lee et al., 2021 ).…”

“…Along with these platforms, different sensors and imagers, such as red-green-blue or RGB, multispectral, hyperspectral, and thermal cameras, are deployed to collect data ( Zhang et al., 2019a ; Deng et al., 2020 ; Tetila et al., 2020 ). Vegetation indices (VIs), texture, thermal, and morphological features (e.g., canopy cover and volume and contour) are extracted from data for plant disease monitoring ( Zhang et al., 2019a ; Lee et al., 2021 ; Vishnoi et al., 2021 ). Machine learning algorithms are commonly applied to data collected or features extracted to automatically identify, classify, and quantify plant diseases ( Johannes et al., 2017 ; Wang et al., 2020 ; Fernández-Campos et al., 2021 ; Gao et al., 2021 ).…”