Transfer Learning from Synthetic Data Applied to Soil–Root Segmentation in X-Ray Tomography Images

One of the most challenging computer vision problems in the plant sciences is the segmentation of roots and soil in X-ray tomography. So far, this has been addressed using classical image analysis methods. In this paper, we address this soil-root segmentation problem in X-ray tomography using a variant of supervised deep learning-based classification called transfer learning where the learning stage is based on simulated data. The robustness of this technique, tested for the first time with this plant science problem, is established using soil-roots with very low contrast in X-ray tomography. We also demonstrate the possibility of efficiently segmenting the root from the soil while learning using purely synthetic soil and roots

Seeing Genes: Multiscale Phenotyping Reveals Gene Functions in Maize Pollen

Plant sexual reproduction requires a broad array of molecular mechanisms to proceed successfully. Some of these mechanisms are well-studied, but our knowledge of them as a whole is fundamentally incomplete. Pollen tube growth is a key part of this process, facilitating the delivery of the sperm cells to the female ovule. In this work we examine pollen function on several scales in the model organism Zea mays. First, we characterize two homeologous genes in the nop family that promote pollen tube growth. These genes are highly expressed in maize pollen. The proteins encoded by these genes contain predicted functional domains involved in calcium and phosphoinositide binding and membrane organization, key mechanisms in pollen tube growth. Mutations in nop genes led to a reduction in pollen fitness, demonstrated by reduced transmission of mutant alleles when heterozygous mutant plants were crossed through the male, but not through the female. Examinations of nop mutant pollen tubes revealed shorter tubes in mutants than in wild type pollen. In one nop mutant, tube lengths were particularly sensitive to chemicals that interfered with phosphoinositide signaling, suggesting a role for nop intimately connected to those pathways. Next, we studied a larger set of genes that were highly expressed in maize pollen. To identify these genes, we sequenced the maize pollen transcriptome at four developmental stages. Highly expressed genes in some of these stages were tested for their contributions to pollen fitness. Mutations in these genes were linked to fluorescent seed markers that could be tracked using a phenotyping system that we developed. Transmission rates of these mutant alleles were quantified, leading to the identification of several mutants that had negative effects on pollen fitness, suggesting functional roles for the genes they disrupted. One of these genes was gex2, which we linked to problems at fertilization. In this experiment, we demonstrated the utility of phenotyping screens to track kernel markers, linking high expression to contributions of genes to pollen fitness. The final section of this dissertation describes the method we developed to track kernels on a large scale. We created a novel high-throughput phenotyping system to scan maize ears. The system spins a maize ear while capturing a video, which is then processed into a flat projection of the surface of the ear. This platform creates a permanent record of the ear, which can then be measured to describe a variety of phenotypes. We designed and trained a deep-learning-based computer vision pipeline to rapidly identify kernel phenotypes. Our system identifies fluorescent and non-fluorescent kernels accurately, expanding our ability to study the contributions of many genes to pollen function. This dissertation describes an effort to understand the function of genes, molecules that are invisible to the human eye. By building on the work of many scientists, we present several ways to "see" gene functions through genetic assays, phenotyping, and sequencing. These methods describe several scales of molecular function, from single genes to entire sets of genes. Ultimately, we hope to lay the groundwork for future studies that take advantage of multiscale phenotyping to uncover the molecular mechanisms of plant sexual reproduction

Archaeobotanical applications of microCT imaging

This thesis explores the ways in which the three-dimensional and non-destructive imaging technique of microCT can be applied to archaeobotanical materials to extract additional information previously inaccessible using traditional two-dimensional techniques. Across a series of eight publications, two microCT imaging protocols focusing on the imaging and analysis of two distinct types of archaeobotanical remains are presented along with archaeological case studies to which they have been successfully applied. Both protocols seek to utilise the relatively new imaging technique of microCT in order to explore the histories of some of the world's most important, yet in some cases understudied food crops including rice (Oryza sativa) in Island Southeast Asia, sorghum (Sorghum bicolor) and pearl millet (Pennisetum glaucum) in Africa, and taro (Colocasia esculenta), sweet potato (Ipomoea batatas), and yams (Dioscoreaceae) in Southeast Asia and the Pacific. The first protocol outlines how organic cereal tempers can be virtually extracted from inside pottery sherds through the use of microCT scanning and 3D digital segmentation techniques. These extracted digital remains can then be taxonomically identified and their domesticated status assessed using the morphological information only accessible with the penetrative X-rays of microCT. This protocol has been successfully applied to extract new rice and sorghum assemblages from previously excavated pottery sherds and their analysis has expanded our knowledge of the dispersal and early cultivation histories of these staple food crops. The second protocol uses microCT to build the first virtual reference collection of a greatly understudied type of archaeobotanical evidence, archaeological parenchyma. This protocol was developed by imaging samples of important root crops in the Southeast Asia and Pacific region from Jon Hather's parenchyma reference collection and applying his taxonomic identification method developed in the 1980s and 90s. Here his method is updated and adapted to include the added three-dimensional contextual information provided by microCT scanning as well as the greater range of anatomical variation captured both within and between species. The microCT datasets of these reference samples will form part of the first publicly accessible, online and virtual, archaeological parenchyma reference collection, which will hopefully encourage wider adoption and application of the technique. Both archaeobotanical microCT protocols presented here demonstrate the enormous potential of the technique to expand on our current sources of archaeobotanical evidence. The digital nature of the datasets presents the possibility of increasing analytical efficiency in the future with the development of automated archaeobotanical analyses