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Gehan, M. A.

Publications and source records attributed to Gehan, M. A..

3 recordsLinked to original sources

Classifying cold stress responses of inbred maize seedlings using RGB imaging

Increasing the tolerance of maize seedlings to low temperature episodes could mitigate the effects of increasing climate variability on yield. To aid progress toward this goal, we established a growth chamber-based system for subjecting seedlings of 40 maize inbred genotypes to a defined, temporary cold stress while collecting digital profile images over a 9-day time course. Image analysis performed with PlantCV software quantified shoot height, shoot area, 14 other morphological traits, and necrosis identified by color analysis. Hierarchical clustering of changes in growth rates of morphological traits and quantification of leaf necrosis over two time intervals resulted in three clusters of genotypes, which are characterized by unique responses to cold stress. For any given genotype, the set of traits with similar growth rates is unique. However, the patterns among traits are different between genotypes. Cold sensitivity was not correlated with the latitude where the inbred varieties were released suggesting potential further improvement for this trait. This work will serve as the basis for future experiments investigating the genetic basis of recovery to cold stress in maize seedlings.

plant biology

Trait components of whole plant water use efficiency are defined by unique, environmentally responsive genetic signatures in the model C4 grass Setaria

Plant growth and water use are interrelated processes influenced by the genetic control of both plant morphological and biochemical characteristics. Improving plant water use efficiency (WUE) to sustain growth in different environments is an important breeding objective that can improve crop yields and enhance agricultural sustainability. However, genetic improvements of WUE using traditional methods have proven difficult due to low throughput and environmental heterogeneity encountered in field settings. To overcome these limitations the study presented here utilizes a high-throughput phenotyping platform to quantify plant size and water use of an interspecific Setaria italica x Setaria viridis recombinant inbred line population at daily intervals in both well-watered and water-limited conditions. Our findings indicate that measurements of plant size and water use in this system are strongly correlated; therefore, a linear modeling approach was used to partition this relationship into predicted values of plant size given water use and deviations from this relationship at the genotype level. The resulting traits describing plant size, water use and WUE were all heritable and responsive to soil water availability, allowing for a genetic dissection of the components of plant WUE under different watering treatments. Linkage mapping identified major loci underlying two different pleiotropic components of WUE. This study indicates that alleles controlling WUE derived from both wild and domesticated accessions of the model C4 species Setaria can be utilized to predictably modulate trait values given a specified precipitation regime.

plant biology

Raspberry Pi Powered Imaging for Plant Phenotyping

O_LIPremise of the study: Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data.\nC_LIO_LIMethods and Results: We used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (shape, area, height, color) en masse using open-source image processing software such as PlantCV.\nC_LIO_LIConclusion: This protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.\nC_LI

plant biology