For more than half of the world’s population, rice (Oryza sativa L.), the most saltsensitive cereal, is a dietary staple. Soil salinity is a major constraint to rice production worldwide. Thus, to feed 9 billion people by 2050, we need to increase rice production while facing the challenges of rapid global environmental changes. To meet some of these challenges, there is a vital requirement to significantly increase rice production in salinized land and improve photosynthetic efficiency. Exposure of plants to soil salinity rapidly reduces their growth and transpiration rates (TRs) due to the ‘osmotic component’ of salt stress (sensu Munns and Tester), which is hypothesized to be related to sensing and signaling mechanisms. Over time, toxic concentrations of Na+ and Cl− accumulate in the cells of the shoot, known as the ‘ionic component’ of salt stress, which causes premature leaf senescence. Both osmotic and ionic components of salinity stress are likely to impact yield. Despite significant advances in our understanding of the ionic components of salinity tolerance, little is known about the early responses of plants to salinity stress. In my PhD project, the aim was to analyze naturally occurring variation in salinity tolerance of rice and identify key genes related to higher salinity tolerance using high-throughput phenomics and field trials. I used a forward genetics approach, with two rice diversity panels (indica and aus) and recently published sequencing data (McCouch et al., 2017). Indica and aus were phenotyped under controlled conditions, while the indica diversity panel was also further studied under field conditions for salinity tolerance. I also examined previously unexplored traits associated with salinity tolerance, in particular the effects of salinity on transpiration and transpiration use efficiency. The non-destructive high-throughput experiments conducted under controlled conditions gave insights into the understudied shoot ion-independent component of salinity tolerance. In parallel, the field experiments increased our understanding of the genetic control of further components of salinity tolerance, including the maintenance of yield under saline conditions. Importantly, this project also aimed to improve the current association methods of GWAS by exploring and testing novel Mixed Linear Models. One major benefit of this Ph.D. project was the development of a more holistic approach that recognizes the complexity of the genotype–phenotype interaction. The purpose of my work was to shed more light on the genetic mechanisms of salinity tolerance in rice and discover genes associated with traits contributing to higher photosynthetic activity under both controlled and field conditions. This will ultimately lead to further exploration of the genetic diversity present in the PRAY indica panel, in order to develop higher yielding rice varieties.

Indiana University-Purdue University Indianapolis (IUPUI) / Forensic DNA phenotyping is an up and coming area in forensic DNA analyses that enables the prediction of physical appearance of an individual from DNA left at a crime scene. At present, there has been substantial work performed in understanding what genes/markers are required to produce a reliable prediction of categorical eye and hair color from the DNA of an individual of interest. These pigmentation markers (variants from HERC2, OCA2, TYR, SLC24A4, SLC45A2, IRF4 to name a few) are at the core of several prediction systems for eye and hair color such as IrisPlex, HIrisPlex, and the Snipper 2.5 suite. The contribution of these markers towards prediction in most cases however, only factors in an independent effect and do not take into account potential interactions or epistasis in the production of the final phenotypic color. Epistasis is a phenomenon that occurs when a gene’s effect relies on the presence of ‘modifier genes’, and can display different effects (enhance/repress a particular color) in genotype combinations rather than individually. In an effort to detect such epistatic interactions and their influence on hair color prediction models, for this current study, 872 individuals were genotyped at 61 associative and predictive pigmentation markers from several diverse population subsets. Individuals were phenotypically evaluated for eye and hair color by three separate independent assessments. Several analyses were performed using statistical approaches such as multifactor dimensionality reduction (MDR) for example, in an effort to detect if there are any SNP- SNP epistatic interactions present that could potentially enhance eye and hair color prediction model performances. The ultimate goal of this study was to assess what SNP-SNP combinations amongst these known pigmentation genes should be included as an additional variable in future prediction models and how much they can potentially enhance overall pigmentation prediction model performance. The second part of the project involved the analyses of several differentially expressed candidate genes between different hair color follicles of the same individual using quantitative Real Time PCR. We looked at 26 different genes identified through a concurrent non-human primate study being performed in the laboratory. The purpose of this study was to gain more insight on the level of differentially expressed mRNA between different hair color follicles within the same human individual. Data generated from this part of the project will act as a pilot study or ‘proof of principle’ on the mRNA expression of several pigmentation associated genes on individual beard hair of varying phenotypic colors. This analysis gives a first glimpse at expression levels that remain constant or differentiate between hairs of the same individual, therefore limiting the contribution of individual variation.

Indiana University-Purdue University Indianapolis (IUPUI) / Modern DNA profiling techniques have increased in sensitivity allowing for higher success in producing a DNA profile from limited evidence sources. However, this can lead to the amplification of more DNA profiles that do not get a hit on a suspect or DNA database and more mixture profiles. The work here aims to address or improve these consequences of current DNA profiling techniques. Based on allele-specific PCR and quantitative color measurements, a 24-SNP forensic phenotypic profile (FPP) assay was designed to simultaneously predict eye color, hair color, skin color, and ancestry, with the potential for age marker incorporation. Bayesian Networks (BNs) were built for model predictions based on a U.S sample population of 200 individuals. For discrete pigmentation traits using an ancestry influenced pigmentation prediction model, AUC values were greater than 0.65 for the eye, hair, and skin color categories considered. For ancestry using an all SNPs prediction model, AUC values were greater than 0.88 for the 5 continental ancestry categories considered. Quantitative pigmentation models were also built with prediction output as RGB values; the average amount of error was approximately 7% for eye color, 12% for hair color, and 8% for skin color. A novel sequencing method, methyl-RADseq, was developed to aid in the discovery of candidate age-informative CpG sites to incorporate into the FPP assay. There were 491 candidate CpG sites found that either increased or decreased with age in three forensically relevant xii fluids with greater than 70% correlation: blood, semen, and saliva. The effects of exogenous microbial DNA on human DNA profiles were analyzed by spiking human DNA with differing amounts of microbial DNA using the Promega PowerPlex® 16 HS kit. Although there were no significant effects to human DNA quantitation, two microbial species, B. subtilis and M. smegmatis, amplified an allelic artifact that mimics a true allele (‘5’) at the TPOX locus in all samples tested, interfering with the interpretation of the human profile. Lastly, the number of contributors of theoretically generated 2-, 3-, 4-, 5-, and 6-person mixtures were evaluated via allele counting with the Promega PowerPlex® Fusion 6C system, an amplification kit with the newly expanded core STR loci. Maximum allele count in the number of contributors for 2- and 3-person mixtures was correct in 99.99% of mixtures. It was less accurate in the 4-, 5-, and 6-person mixtures at approximately 90%, 57%, and 8%, respectively. This work provides guidance in addressing some of the limitations of current DNA technologies.

Indiana University-Purdue University Indianapolis (IUPUI) / In the forensic science community, there is a vast need for tools to help assist investigations when standard DNA profiling methods are uninformative. Methods such as Forensic DNA Phenotyping (FDP) and proteomics aims to help this problem and provide aid in investigations when other methods have been exhausted. FDP is useful by providing physical appearance information, while proteomics allows for the examination of difficult samples, such as hair, to infer human identity and ancestry. To create a “biological eye witness” or develop informative probability of identity match statistics through proteomically inferred genetic profiles, it is necessary to constantly strive to improve these methods. Currently, two developmentally validated FDP prediction assays, ‘HIrisPlex’ and ‘HIrisplex-S’, are used on the capillary electrophoresis to develop a phenotypic prediction for eye, hair, and skin color based on 41 variants. Although highly useful, these assays are limited in their ability when used on the CE due to a 25 variant per assay cap. To overcome these limitations and expand the capacities of FDP, we successfully designed and validated a massive parallel sequencing (MPS) assay for use on both the ThermoFisher Scientific Ion Torrent and Illumina MiSeq systems that incorporates all HIrisPlex-S variants into one sensitive assay. With the migration of this assay to an MPS platform, we were able to create a semi-automated pipeline to extract SNP-specific sequencing data that can then be easily uploaded to the freely accessible online phenotypic prediction tool (found at https://hirisplex.erasmusmc.nl) and a mixture deconvolution tool with built-in read count thresholds. Based on sequencing reads counts, this tool can be used to assist in the separation of difficult two-person mixture samples and outline the confidence in each genotype call. In addition to FDP, proteomic methods, specifically in hair protein analysis, opens doors and possibilities for forensic investigations when standard DNA profiling methods come up short. Here, we analyzed 233 genetically variant peptides (GVPs) within hair-associated proteins and genes for 66 individuals. We assessed the proteomic methods ability to accurately infer and detect genotypes at each of the 233 SNPs and generated statistics for the probability of identity (PID). Of these markers, 32 passed all quality control and population genetics criteria and displayed an average PID of 3.58 x 10-4. A population genetics assessment was also conducted to identify any SNP that could be used to infer ancestry and/or identity. Providing this information is valuable for the future use of this set of markers for human identification in forensic science settings.

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第19765号 / 農博第2161号 / 新制||農||1039(附属図書館) / 学位論文||H28||N4981(農学部図書室) / 32801 / 京都大学大学院農学研究科応用生物科学専攻 / (主査)教授 佐藤 健司, 教授 澤山 茂樹, 准教授 田川 正朋 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

Breeding compact cotton plants is desirable for many reasons, but current research for this is restricted by manual data collection. Using unmanned aircraft system imagery shows potential for high-throughput automation of this process. Using multispectral orthomosaics and ground truth measurements, I developed supervised models with a wide range of hyperparameters to predict three compactness traits. Extreme gradient boosting using a feature matrix as input was able to predict the height-related metric with R2=0.829 and RMSE=0.331. The breadth metrics require higher-detailed data and more complex models to predict accurately.

Salt stress is a global problem that limits agricultural production. The early responses to salinity, independent of toxic shoot-ion accumulation, are still largely unknown. Here, optimised salt treatment and high-throughput phenotyping protocols were developed and used to examine the natural variation in the early responses to salt stress of 191 Arabidopsis thaliana accessions. Common and novel traits of plants grown under salt treatment were captured through time using RGB and chlorophyll fluorescence imaging. Phenotypic data was combined with the Arabidopsis 10M SNP markers for genome-wide association studies to identify genetic components underlying the early responses to salt stress. The most promising candidate loci were selected based on association strength, allele frequency and number of traits associating to the same locus. In silico analysis highlighted interesting allelic variations across the identified loci, and by phenotypically characterising the candidate gene mutants under salt stress, the associations were experimentally validated. This work comprises a detailed study of the natural variation in the early responses to salt stress, which can give insights into the mechanisms contributing to salinity tolerance and provide the fundaments for crop improvements under saline conditions across the globe.

This dissertation investigates the utility of deep learning and machine learning approaches for livestock management and quantitative genetic modeling of rice grain size under climate change. Monitoring the live body weight of animals is crucial to support farm management decisions due to its direct relationship with animal growth, nutritional status, and health. However, conventional manual weighing methods are time consuming and can cause potential stress to animals. While there is a growing trend towards the use of three-dimensional cameras coupled with computer vision techniques to predict animal body weight, their validation with deep learning models as well as large-scale data collected in commercial environments is still limited. Therefore, the first two research chapters show how deep learning-based computer vision systems can enable accurate live body weight prediction for dairy cattle and pigs. These studies also address the challenges of managing large, complex phenotypic data and highlight the potential of deep learning models to automate data processing and improve prediction accuracy in an industry-scale commercial setting. The dissertation then shifts the focus to crop resilience, particularly in rice, where the asymmetric increase in average nighttime temperatures relative to the increase in average daytime temperatures due to climate change is reducing grain yield and quality in rice. Through the use of deep learning and machine learning models, the last two chapters explore how metabolic data can be used in quantitative genetic modeling in rice under environmental stress conditions such as high night temperatures. These studies showed that the integration of metabolites and genomics provided an improvement in the prediction of rice grain size-related traits, and certain metabolites were identified as potential candidates for improving multi-trait genomic prediction. Further research showed that metabolic accumulation was low to moderately heritable, and genomic prediction accuracies were consistent with expected genomic heritability estimates. Genomic correlations between control and high night temperature conditions indicated genotype-by-environment interactions in metabolic accumulation and the effectiveness of genomic prediction models for metabolic accumulation varied across metabolites. Joint analysis of multiple metabolites improved the accuracy of genomic prediction by exploiting correlations between metabolite accumulation. Overall, this dissertation highlights the potential of integrating digital technologies and multi-omic data to advance data analytics in agriculture, with applications in livestock management and quantitative genetic modeling of rice. / Doctor of Philosophy / This dissertation explores the application of deep learning and machine learning to computer vision-based livestock management and quantitative genetic modeling of rice grain size under climate change. The first half of the research chapters illustrate how computer vision systems can enable digital phenotyping of dairy cows and pigs, which is critical for informed management decisions and quantitative genetic analysis. These studies address the challenges of managing large-scale, complex phenotypic data and highlight the potential of deep learning models to automate data processing and improve prediction accuracy. Chapter 3 showed that a deep learning-based segmentation, Mask R-CNN, improved the prediction performance of cow body weight from longitudinal depth video data. Among the image features, volume followed by width correlated best with body weight. Chapter 4 showed that efficient deep learning-based supervised learning models are a promising approach for predicting pig body weight from industry-scale depth video data. Although the sparse design, which simulates budget and time constraints by using a subset of the data for training, resulted in some performance loss compared to the full design, the Vision Transformer models effectively mitigated this loss. The second half of the research chapters focuses on integrating metabolomic and genomic data to predict grain traits and metabolic content in rice under climate change. Through the use of machine learning models, these studies investigate how combining genomic and metabolic data can improve predictions, particularly under high night temperature stress in rice. Chapter 5 showed that the integration of metabolites and genomics improved the prediction of rice grain size-related traits, and certain metabolites were identified as potential candidates for improving multi-trait genomic prediction. Chapter 6 showed that metabolic accumulation was low to moderately heritable. Genomic correlations between control and high night temperature conditions indicated genotype-by-environment interactions in metabolic accumulation, and the effectiveness of genomic prediction models for metabolic accumulation varied across metabolites. Joint analysis of multiple metabolites improved the accuracy of genomic prediction by exploiting correlations between metabolite accumulation. Overall, the dissertation provides insight into how cutting-edge methods can be used to improve livestock management and multi-omic quantitative genetic modeling for breeding.

This report presents the results of investigations into whether, and how well, Artificially Intelligent (AI) algorithms can be used to estimate the height of plants by using images from regular surveillance cameras, setup over one of Svegros basil farms. The project is of great economical importance as too tall basil plants will not fit the shelves at stores and too small plants will disappoint customers. This is a part of a bigger movement at Svegro to automate the monitoring and caring for the growing plants, aiming at lowering energy consumption and minimizing waste. To measure the heights, rulers (Robel poles) were placed behind the plants that moved on conveyor belts under cameras so the plants’ heights could manually be established from the number of visible lines on the Robel pole, not covered by the plant. The research problem was to engineer an AI based solution to predict how many lines were visible above the plant. After two months of gathering images and manually annotating them, three Convolutional Neural Network (CNN) models of varying complexity were trained on the images of individual Robel poles from the basil field. Results obtained with Grad-CAM showed that the networks do not learn to count the lines but to correlate the leafs size and shape to the height. The best score was a Mean Absolute Error of 0.74 and a Mean Squared Error of 0.83, where a MAE of 2.53 and MSE of 11.11 corresponded to just predicting the data sets median. This was achieved with EfficientNet0B. The results were compared with a human being’s performance which showed that the human still performed better but due to the noisy data, the results are considered impressive and the score exceeded the expectations of the team at Svegro so the final model is now used there today. It was also shown that reasonably good results could be obtained even without the Robel pole in the training images, meaning the Svegro team could stop setting out the Robel poles but with a slight loss in precision. Suggestions for improvements, like changing the design of the Robel poles, are presented to aid future research to fully automate the process with higher accuracy. / I denna rapport presenteras resultaten från undersökningen av huruvida en Artificiellt Intelligent (AI) algoritm kunde användas för att estimera höjden på plantor från bilder tagna med övervakningskameror som satts över en av Svegros basilikaodlingar. Projektet är av stor ekonomisk vikt eftersom basilikan inte får vara för lång för att inte passa i hyllorna i butiker eller för korta för att göra konsumenterna missnöjda. Detta är en del av ett större projekt som innebär övergång till automation av övervakandet och odlandet hos Svegro med förhoppningen om att kunna minska energiförbrukningen och svinnet. För att mäta höjden placerades linjaler (Robel-pinnar) bakom plantorna som rörde sig längs ett stort rullband under kameror så att plantornas höjd manuellt kunde bestämmas från antalet sträck på linjalen som täcktes av plantan. Forskningsuppdraget blev därmed att ta fram en AI som kunde uppskatta hur många linjer som syntes. Efter två månaders samlande av data samt manuellt annoterande av dem testades tre CNNs (Convolutional Neural Network) med olika komplexitet genom att tränas på bilderna av individuella Robel-pinnar från basilikafältet. Resultat som erhölls med Grad-CAM visade att nätverken inte lär sig räkna linjerna utan istället korrelerar basilikabladen form och storlek till höjden. Det bästa resultatet som erhölls var ett MAE (Mean Absolute Error) på 0.74 samt MSE (Mean Square Error) på 0.83, där ett MAE på 2.53 och ett MSE på 11.11 hade motsvarat gissande på datasettets median. Detta resultat erhölls med EfficientNet0B. Resultatet gämfördes med en människas prestation vilket visade att människan presterade bättre, men på grund av osäkerhet i datan ansågs resultaten vara imponerande och överträffade förväntningarna från teamet på Svegro som idag använder modellen. Det visades även att tillfredsställande resultat kunde erhållas med bilder som inte innehöll Robel-pinnen vilket innebär att teamet på Svegro skulle kunna sluta sätta ut Robel-pinnarna i krukorna men då med en liten förlust i precision. Förslag på förbättringar, som att förbättra desingnen på Robel-pinnarna, tas också upp för att hjälpa framtida forskning att snabbare komma till resultat som kan leda till en fullständigt automatiserad process med bättre noggrannhet.

<p>Plant root phenotyping technologies play an important role in breeding, plant protection, and other plant science research projects. The root phenotyping customers urgently need technologies that are low-cost, in situ, non-destructive to the roots, and suitable for the natural soil environment. Many recently developed root phenotyping methods such as minirhizotron, CT, and MRI scanners have their unique advantages in observing plant roots, but they also have disadvantages and cannot meet all the critical requirements simultaneously. The study in this paper focuses on the development of a new plant root phenotyping robot that is minimally invasive to plants and working in situ inside natural soil, called “MISIRoot”. The MISIRoot system (patent pending) mainly consists of an industrial-level robotic arm, a mini-size camera with lighting set, a plant pot holding platform, and the image processing software for root recognition and feature extraction. MISIRoot can take high-resolution color images of the roots in soil with minimal disturbance to the root and reconstruct the plant roots’ three-dimensional (3D) structure at an accuracy of 0.1 mm. In a test assay, well-watered and drought-stressed groups of corn plants were measured by MISIRoot at V3, V4, and V5 stages. The system successfully acquired the RGB color images of the roots and extracted the 3D points cloud data which showed the locations of the detected roots in the soil. The plants measured by MISIRoot and plants not measured (controls) were carefully compared with Purdue’s Lilly 13-4 Hyperspectral Imaging Facility (reference). No significant differences were found between the two groups of plants at different growth stages. Therefore, it was concluded that MISIRoot measurements had no significant disturbance to the corn plant’s growth.</p>