A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google's Colaboratory.

X-ray micro-computed tomography (X-ray µCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray µCT images, using low-cost resources in Google's Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences.

Genomic-environmental associations in wild cranberry (Vaccinium macrocarpon Ait.).

Understanding the genetic basis of local adaptation in natural plant populations, particularly crop wild relatives, may be highly useful for plant breeding. By characterizing genetic variation for adaptation to potentially stressful environmental conditions, breeders can make targeted use of crop wild relatives to develop cultivars for novel or changing environments. This is especially appealing for improving long-lived woody perennial crops such as the American cranberry (Vaccinium macrocarpon Ait.), the cultivation of which is challenged by biotic and abiotic stresses. In this study, we used environmental association analyses in a collection of 111 wild cranberry accessions to identify potentially adaptive genomic regions for a range of bioclimatic and soil conditions. We detected 126 significant associations between SNP marker loci and environmental variables describing temperature, precipitation, and soil attributes. Many of these markers tagged genes with functional annotations strongly suggesting a role in adaptation to biotic or abiotic conditions. Despite relatively low genetic variation in cranberry, our results suggest that local adaptation to divergent environments is indeed present, and the identification of potentially adaptive genetic variation may enable a selective use of this germplasm for breeding more stress-tolerant cultivars.

Genetic loci mediating circadian clock output plasticity and crop productivity under barley domestication.

Circadian clock rhythms are shown to be intertwined with crop adaptation. To realize the adaptive value of changes in these rhythms under crop domestication and improvement, there is a need to compare the genetics of clock and yield traits. We compared circadian clock rhythmicity based on Chl leaf fluorescence and transcriptomics among wild ancestors, landraces, and breeding lines of barley under optimal and high temperatures. We conducted a genome scan to identify pleiotropic loci regulating the clock and field phenotypes. We also compared the allelic diversity in wild and cultivated barley to test for selective sweeps. We found significant loss of thermal plasticity in circadian rhythms under domestication. However, transcriptome analysis indicated that this loss was only for output genes and that temperature compensation in the core clock machinery was maintained. Drivers of the circadian clock (DOC) loci were identified via genome-wide association study. Notably, these loci also modified growth and reproductive outputs in the field. Diversity analysis indicated selective sweep in these pleiotropic DOC loci. These results indicate a selection against thermal clock plasticity under barley domestication and improvement and highlight the importance of identifying genes underlying for understanding the biochemical basis of crop adaptation to changing environments.

Multi-trait Improvement by Predicting Genetic Correlations in Breeding Crosses.

The many quantitative traits of interest to plant breeders are often genetically correlated, which can complicate progress from selection. Improving multiple traits may be enhanced by identifying parent combinations - an important breeding step - that will deliver more favorable genetic correlations (rG ). Modeling the segregation of genomewide markers with estimated effects may be one method of predicting rG in a cross, but this approach remains untested. Our objectives were to: (i) use simulations to assess the accuracy of genomewide predictions of rG and the long-term response to selection when selecting crosses on the basis of such predictions; and (ii) empirically measure the ability to predict genetic correlations using data from a barley (Hordeum vulgare L.) breeding program. Using simulations, we found that the accuracy to predict rG was generally moderate and influenced by trait heritability, population size, and genetic correlation architecture (i.e., pleiotropy or linkage disequilibrium). Among 26 barley breeding populations, the empirical prediction accuracy of rG was low (-0.012) to moderate (0.42), depending on trait complexity. Within a simulated plant breeding program employing indirect selection, choosing crosses based on predicted rG increased multi-trait genetic gain by 11-27% compared to selection on the predicted cross mean. Importantly, when the starting genetic correlation was negative, such cross selection mitigated or prevented an unfavorable response in the trait under indirect selection. Prioritizing crosses based on predicted genetic correlation can be a feasible and effective method of improving unfavorably correlated traits in breeding programs.

Evaluating Methods of Updating Training Data in Long-Term Genomewide Selection.

Genomewide selection is hailed for its ability to facilitate greater genetic gains per unit time. Over breeding cycles, the requisite linkage disequilibrium (LD) between quantitative trait loci and markers is expected to change as a result of recombination, selection, and drift, leading to a decay in prediction accuracy. Previous research has identified the need to update the training population using data that may capture new LD generated over breeding cycles; however, optimal methods of updating have not been explored. In a barley (Hordeum vulgare L.) breeding simulation experiment, we examined prediction accuracy and response to selection when updating the training population each cycle with the best predicted lines, the worst predicted lines, both the best and worst predicted lines, random lines, criterion-selected lines, or no lines. In the short term, we found that updating with the best predicted lines or the best and worst predicted lines resulted in high prediction accuracy and genetic gain, but in the long term, all methods (besides not updating) performed similarly. We also examined the impact of including all data in the training population or only the most recent data. Though patterns among update methods were similar, using a smaller but more recent training population provided a slight advantage in prediction accuracy and genetic gain. In an actual breeding program, a breeder might desire to gather phenotypic data on lines predicted to be the best, perhaps to evaluate possible cultivars. Therefore, our results suggest that an optimal method of updating the training population is also very practical.