Extracting complex lesion phenotypes in Zea mays

Abstract

Complex phenotypes are of growing importance in agriculture and medicine. In Zea mays, the most widely produced crop in the world (United States Department of Agriculture. World Agricultural Production. United States Department of Agriculture, Foreign Agricultural Service, Washington, 2015), the disease lesion mimic mutants produce regions of discolored or necrotic tissue in otherwise healthy plants. These mutants are of particular interest due to their apparent action on immune response pathways, providing insight into how plants protect against infectious agents. These phenotypes vary considerably as a function of genotype and environmental conditions, making them a rich, though challenging, phenotypic problem. To segment and quantitate these lesions, we present a novel cascade of adaptive algorithms able to accurately segment the diversity of Z. mays lesions. First, multiresolution analysis of the image allows for salient features to be detected at multiple scales. Next, gradient vector diffusion enhances relevant gradient vectors while suppressing noise. Finally, an active contour algorithm refines the lesion boundary, producing a final segmentation for each lesion. We compare the results from this cascade with manual segmentations from human observers, demonstrating that our algorithm is comparable to humans while having the potential to speed analysis by several orders of magnitude.

Back into the wild—Apply untapped genetic diversity of wild relatives for crop improvement

“…With the availability of increasing numbers of SNPs and phenotypic data, researchers have been able to validate and fine‐map previously identified genes and to discover novel genomic regions underlying valuable agronomic traits in CWRs by association mapping (Li et al., ; Qi et al., ; Xu et al., ; Zhou et al., ). However, the development of a high‐throughput phenotyping pipeline remains challenging, especially in the field (Kelly et al., ). Some of the genomic regions associated with domestication traits have enhanced our understanding of their genetic basis, and will encourage further investigation to see whether allelic variation in those regions in CWRs can additionally benefit crop improvement.…”

An opinion on imaging challenges in phenotyping field crops

“…Such destructive sampling speeds image collection and facilitates more consistent image composition and lighting, permitting simpler algorithmic approaches. This approach has been used to size and count kernels, measure ear dimensions, or identify leaf lesions [21,[34][35][36] and (Nathan Miller, personal communication). Specialized laboratory equipment, such as microscopes and systems to image roots grown in transparent media, is used to generate image series for morphometric measurements and 3D reconstruction [20,37].…”

“…b A production agriculture field planted with a single variety of maize. In this field, average leaf angle could probably be estimated from measuring the angle between a vertical axis and planes defined by each leaf (especially those in the upper half of the field), similar to the work of reference [11] tion, and extract phenotypic information from the images [20,21]. We selected these challenges based on three sources of information.…”

Special issue on computer vision and image analysis in plant phenotyping

“…Six of them [1,3,11,13,18,21] were developed from conference contributions at CVPPP 2014 [2,4,12,14,19,20]. Two other papers received by the open call already appeared in a preceding or an adjacent issue of this journal and are also part of this special issue [9,22].…”

“…When we move to the field the ultimate and most useful frontier, things become exceedingly more complex. To motivate further research into this area, Kelly et al [9] discuss an interesting set of challenges when phenotyping field crops.…”