RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation

We present RootPainter, a GUI-based software tool for the rapid training of deep neural networks for use in biological image analysis. RootPainter facilitates both fully-automatic and semiautomatic image segmentation. We investigate the effectiveness of RootPainter using three plant image datasets, evaluating its potential for root length extraction from chicory roots in soil, biopore counting and root nodule counting from scanned roots. We also use RootPainter to compare dense annotations to corrective ones which are added during the training based on the weaknesses of the current model

Mehitamata õhusõiduki rakendamine põllukultuuride saagikuse ja maa harimisviiside tuvastamisel

A Thesis for applying for the degree of Doctor of Philosophy in Environmental Protection.Väitekiri filosoofiadoktori kraadi taotlemiseks keskkonnakaitse erialal.This thesis aims to examine how machine learning (ML) technologies have aided significant advancements in image analysis in the area of precision agriculture. These multimodal computing technologies extend the use of machine learning to a broader spectrum of data collecting and selection for the advancement of agricultural practices (Nawar et al., 2017) These techniques will assist complicated cropping systems with more informed decisions with less human intervention, and provide a scalable framework for incorporating expert knowledge of the PA system. (Chlingaryan et al., 2018). Complexity, on the other hand, can be seen as a disadvantage in crop trials, as machine learning models require training/testing databases, limited areas with insignificant sampling sizes, time and space-specificity, and environmental factor interventions, all of which complicate parameter selection and make using a single empirical model for an entire region impractical. During the early stages of writing this thesis, we used a relatively traditional machine learning method to address the regression problem of crop yield and biomass prediction [(i.e., random forest regression (RFR), support vector regression (SVR), and artificial neural network (ANN)] to predicted dry matter (DM) yields of red clover. It obtained favourable results, however, the choosing of hyperparameters, the lengthy algorithms selection process, data cleaning, and redundant collinearity issues significantly limited the way of the machine learning application. We will further discuss the recent trend of automated machine learning (AutoML) that has been driving further significant technological innovation in the application of artificial intelligence from its automated algorithm selection and hyperparameter optimization of the deployable pipeline model for unravelling substance problems. However, a present knowledge gap exists in the integration of machine learning (ML) technology with unmanned aerial systems (UAS) and hyperspectral-based imaging data categorization and regression applications. In this thesis, we explored a state-of-the-art (SOTA) and entirely open-source AutoML framework, Auto-sklearn, which was built on one of the most frequently used machine learning systems, Scikit-learn. It was integrated with two unique AutoML visualization tools to examine the recognition and acceptance of multispectral vegetation indices (VI) data collected from UAS and hyperspectral narrow-band VIs across a varied spectrum of agricultural management practices (AMP). These procedures incorporate soil tillage method (STM), cultivation method (CM), and manure application (MA), and are classified as four-crop combination fields (i.e., red clover-grass mixture, spring wheat, pea-oat mixture, and spring barley). Additionally, they have not been thoroughly evaluated and lack characteristics that are accessible in agriculture remote sensing applications. This thesis further explores the existing gaps in the knowledge base for several critical crop categories and cultivation management methods referring to biomass and yield analysis, as well as to gain a better understanding of the potential for remotely sensed solutions to field-based and multifunctional platforms to meet precision agriculture demands. To overcome these knowledge gaps, this research introduces a rapid, non-destructive, and low-cost framework for field-based biomass and grain yield modelling, as well as the identification of agricultural management practices. The results may aid agronomists and farmers in establishing more accurate agricultural methods and in monitoring environmental conditions more effectively.Doktoritöö eesmärk oli uurida, kuidas masinõppe (MÕ) tehnoloogiad võimaldavad edusamme täppispõllumajanduse valdkonna pildianalüüsis. Multimodaalsed arvutustehnoloogiad laiendavad masinõppe kasutamist põllumajanduses andmete kogumisel ja valimisel (Nawar et al., 2017). Selline täpsemal informatsioonil põhinev tehnoloogia võimaldab keerukate viljelussüsteemide puhul teha otsuseid inimese vähema sekkumisega, ja loob skaleeritava raamistiku täppispõllumajanduse jaoks (Chlingaryan et al., 2018). Põllukultuuride katsete korral on komplekssete masinõppemudelite kasutamine keerukas, sest alad on piiratud ning valimi suurus ei ole piisav; vaja on testandmebaase, kindlaid aja- ja ruumitingimusi ning keskkonnategureid. See komplitseerib parameetrite valikut ning muudab ebapraktiliseks ühe empiirilise mudeli kasutamise terves piirkonnas. Siinse uurimuse algetapis rakendati suhteliselt traditsioonilist masinõppemeetodit, et lahendada saagikuse ja biomassi prognoosimise regressiooniprobleem (otsustusmetsa regression, tugivektori regressioon ja tehisnärvivõrk) punase ristiku prognoositava kuivaine saagikuse suhtes. Saadi sobivaid tulemusi, kuid hüperparameetrite valimine, pikk algoritmide valimisprotsess, andmete puhastamine ja kollineaarsusprobleemid takistasid masinõpet oluliselt. Automatiseeritud masinõppe (AMÕ) uusimate suundumustena rakendatakse tehisintellekti, et lahendada põhiprobleemid automatiseeritud algoritmi valiku ja rakendatava pipeline-mudeli hüperparameetrite optimeerimise abil. Seni napib teadmisi MÕ tehnoloogia integreerimiseks mehitamata õhusõidukite ning hüperspektripõhiste pildiandmete kategoriseerimise ja regressioonirakendustega. Väitekirjas uuriti nüüdisaegset ja avatud lähtekoodiga AMÕ tehnoloogiat Auto-sklearn, mis on ühe enimkasutatava masinõppesüsteemi Scikit-learn edasiarendus. Süsteemiga liideti kaks unikaalset AMÕ visualiseerimisrakendust, et uurida mehitamata õhusõidukiga kogutud andmete multispektraalsete taimkatteindeksite ja hüperspektraalsete kitsaribaandmete taimkatteindeksite tuvastamist ja rakendamist põllumajanduses. Neid võtteid kasutatakse mullaharimisel, kultiveerimisel ja sõnnikuga väetamisel nelja kultuuriga põldudel (punase ristiku rohusegu, suvinisu, herne-kaera segu, suvioder). Neid ei ole põhjalikult hinnatud, samuti ei hõlma need omadusi, mida kasutatatakse põllumajanduses kaugseire rakendustes. Uurimus käsitleb biomassi ja saagikuse seni uurimata analüüsivõimalusi oluliste põllukultuuride ja viljelusmeetodite näitel. Hinnatakse ka kaugseirelahenduste potentsiaali põllupõhiste ja multifunktsionaalsete platvormide kasutamisel täppispõllumajanduses. Uurimus tutvustab kiiret, keskkonna suhtes kahjutut ja mõõduka hinnaga tehnoloogiat põllupõhise biomassi ja teraviljasaagi modelleerimiseks, et leida sobiv viljelusviis. Töö tulemused võimaldavad põllumajandustootjatel ja agronoomidel tõhusamalt valida põllundustehnoloogiaid ning arvestada täpsemalt keskkonnatingimustega.Publication of this thesis is supported by the Estonian University of Life Scieces and by the Doctoral School of Earth Sciences and Ecology created under the auspices of the European Social Fund

Improving soybean using remote sensing, automated irrigation, and promiscuous nodulation

Remote Sensing of Soybean Maturity Dates via Drones High-throughput phenotyping (HTP) using remote sensing is a fast developing technology, which has the capacity to reduce the time it takes to measure phenotypic traits in the field. HTP shows particular promise as a method for predicting plant maturity. Maturity is the date where 95% of the pods reached mature color (R8 growth stage) and is commonly recorded on all yield plots in breeding programs by periodically walking through experiments and visually estimating maturity dates. Precise maturity dating is a time critical task; therefore, satellites and other previously developed methods of remote sensing would not be applicable to this research. To combat the limitations of other methods of remote sensing, we constructed a two-camera mounted Unmanned Aerial Vehicle (UAV) platform with the capacity to capture visible and near-infrared (NIR) images. This study was done in three broad steps: the acquisition of multi-spectral images using UAVs, constructing composite images of the visible and (NIR) images, and extracting digital values to build a model to predict maturity dates from images. Using these procedures, we were able to develop a binary prediction model from the multi-spectral image data and achieved over 91% accuracy in classifying soybean maturity. The maturity model was validated in an independent breeding trial with a different plot type. These results show that remote sensing can be effectively used to estimate the maturity of plots, but the analysis of images needs to be more efficient before it can be used routinely. Automated Greenhouse SCN Screening System Heterodera glycine (Ichinohe 1952) or soybean cyst nematode (SCN) is a pest of economic importance to soybean (Glycine max (L.) Merr.) in the USA and around the world. From 2003-2009, SCN was estimated to reduce soybean yields more than any other disease or pest in the U.S.A. Methods of control include crop rotation and nematicides, but the most effective form of control is the use of resistant soybean cultivars. The current, established greenhouse screening method uses soil-filled crocks suspended in thermoregulated water baths to control the soil temperature. No current screening method controls the soil moisture to maintain optimal levels for SCN survival and propagation. With the use of soil moisture probes that automatically controlled an irrigation system, we were able to maintain the moisture levels at a constant level. Reproduction of the SCN was improved, with a significant increase in the number of cysts counted on the soybean roots. Overall, these results demonstrate that maintaining soil moisture increases the effectiveness of greenhouse screening methods for SCN. Promiscuous Nodulation Soybean (Glycine max.) is an important source of oil and protein for the U.S.A. and has the potential to be a staple crop in Africa because of its high protein seed and the benefits of nitrogen fixation from the symbiotic relationship with rhizobium bacteria. Soybean has a natural relationship with Bradyrhizobium japonicum, which is not indigenous to the tropical soils in Africa. For soybean to fix nitrogen with B. japonicum, inoculants of this bacteria would be needed, which are generally not available to small-holder African farmers. The cowpea strain of rhizobium bacteria is indigenous to the soils throughout Africa, although it does not nodulate most US soybean cultivars. Some soybean accessions from the USDA Soybean Germplasm Collection can nodulate with the cowpea strain and these are called promiscuous nodulators. The objective of this study was to identify additional accessions from the germplasm collection that are promiscuous nodulators. By screening plants in inoculated pots in a greenhouse, 415 accessions were evaluated for their ability to nodulate and if the nodules were effective. Of the lines tested, 200 were able to form effective, nodules and 42 lines showed no foliar signs of chlorosis due to nitrogen deficiency. Accessions that stood out were PI 429330 (Nigeria) for the highest number of nodules produced, and PI 281883C (Indonesia) for the one of the highest average nodule weights

Genotyping and phenotyping the common pea and its wild relatives

In 1868, three men, Gregor Mendel, Charles Darwin and Friedrich Miescher made significant contributions in genetic inheritance, plant domestication and DNA extraction respectively. 150 years later, this thesis aims to better understand pea domestication through genotyping and phenotyping the common pea and its wild relatives. Peas (Pisum sativum) are a cool season legume important to food security due to their ability to fix nitrogen and produce nutritious food and animal fodder. A core collection of 350 accessions of wild, landrace and cultivated material was developed from the John Innes Pisum Collection. To characterise these accessions, image analysis, a modern phenotyping method was used. Current tools require user expertise, are not cross platform, are not applicable to certain plants or phenotypes. Here, MktStall, a novel multi-organ image analysis is presented, which requires no computational expertise. Pea is a large (4.5Gb) and highly repetitive genome. Here, the first publicly accessible pea genome reference is announced. In combination with a genotyping by sequencing (GBS) approach of this core collection a genome-wide association study (GWAS) was performed using on seed weight, plant height, leaflet margin, seed shape and pod shape. The results in this thesis show statistically significant differences in plant height in cultivars and leaflet length, perimeter and area in landraces in addition to identifying statistically significant loci for leaflet teeth, seed perimeter and seed eccentricity. Furthermore, potential candidate genes have be identified with roles in carbohydrate metabolism known to cause seed wrinkledness and POWERDRESS known to increase leaf area. The combination of novel contributions results in new tools, genomic resources and additional knowledge of pea domestication which can be used in marker assisted selection and improved breeding practices for an important crop for food security

Evaluating the impacts of waterlogging stress on cowpea (Vigna unguiculata L.) growth traits and physiological performance

The progressive increase in the global population and the rapidly changing climate have put unprecedented pressure on crop production. Cowpea is one of the world’s most important leguminous crops, contributing to food security and environmental sustainability. However, cowpea productivity is limited due to waterlogging stress. The main objective of this study was to explore physiological and biochemical mechanisms to understand how cowpea genotypes respond to waterlogging stress. Four studies were conducted in controlled and field conditions to achieve these objectives. Study 1 characterized the waterlogging tolerance of 30 cowpea genotypes in a controlled environment using 24 morphophysiological parameters with waterlogging tolerance coefficients and multivariate analysis methods. 10% of the genotypes exhibited high tolerance to waterlogging stress, and the genotypes UCR 369 and EpicSelect.4 were identified as the most and least waterlogging tolerant, respectively. Study 2 evaluated the key parameters influencing carbon fixation of UCR 369 and EpicSelect.4 at the reproductive stage. The less tolerant EpicSelect.4 experienced high downregulation of stomatal and non-stomatal limiting factors during waterlogging and recovery, resulting in decreased carbon assimilation rates. UCR 369 rapidly developed adventitious roots, maintained biomass, and restored pigments and metabolites to sustain photosynthesis. A two-year field experiment was conducted in study 3 to quantify the effects of waterlogging on the yields, physiology, and biochemistry of cowpeas at different growth stages. The most apparent impact of waterlogging stress occurred at the reproductive stage, followed by the vegetative and maturity growth stages. Studies suggest that diverse cowpea genotypes have distinct physiological and biochemical mechanisms in response to waterlogging stress. In addition, the tolerant genotypes and traits identified herein can be used in genetic engineering and cowpea breeding programs that integrate increased yield with waterlogging stress tolerance

Plant Biodiversity and Genetic Resources

The papers included in this Special Issue address a variety of important aspects of plant biodiversity and genetic resources, including definitions, descriptions, and illustrations of different components and their value for food and nutrition security, breeding, and environmental services. Furthermore, comprehensive information is provided regarding conservation approaches and techniques for plant genetic resources, policy aspects, and results of biological, genetic, morphological, economic, social, and breeding-related research activities. The complexity and vulnerability of (plant) biodiversity and its inherent genetic resources, as an integral part of the contextual ecosystem and the human web of life, are clearly demonstrated in this Special Issue, and for several encountered problems and constraints, possible approaches or solutions are presented to overcome these

Detection of loci associated with water-soluble carbohydrate accumulation and environmental adaptation in white clover (Trifolium repens L.) : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Plant Biology at Massey University, Palmerston North, New Zealand

White clover (Trifolium repens L.) is an economically important forage legume in New Zealand/Aotearoa (NZ). It provides quality forage and a source of bioavailable nitrogen fixed through symbiosis with soil Rhizobium bacteria. This thesis investigated the genetic basis of two traits of significant agronomic interest in white clover. These were foliar water-soluble carbohydrate (WSC) accumulation and soil moisture deficit (SMD) tolerance. Previously generated divergent WSC lines of white clover were characterised for foliar WSC and leaf size. Significant (p < 0.05) divergence in foliar WSC content was observed between five breeding pools. Little correlation was observed between WSC and leaf size, indicating that breeding for increased WSC content could be achieved in large and small leaf size classes of white clover in as few as 2 – 3 generations. Genotyping by sequencing (GBS) data were obtained for 1,113 white clover individuals (approximately 47 individuals from each of 24 populations). Population structure was assessed using discriminant analysis of principal components (DAPC) and individuals were assigned to 11 genetic clusters. Divergent selection created a structure that differentiated high and low WSC populations. Outlier detection methodologies using PCAdapt, BayeScan and KGD-FST applied to the GBS data identified 33 SNPs in diverse gene families that discriminated high and low WSC populations. One SNP associated with the starch biosynthesis gene, glgC was identified in a genome-wide association study (GWAS) of 605 white clover individuals. Transcriptome and proteome analyses also provided evidence to suggest that high WSC levels in different breeding pools were achieved through sorting of allelic variants of carbohydrate metabolism pathway genes. Transcriptome and proteome analyses suggested 14 gene models from seven carbohydrate gene families (glgC, WAXY, glgA, glgB, BAM, AMY and ISA3) had responded to artificial selection. Patterns of SNP variation in the AMY, glgC and WAXY gene families separated low and high WSC individuals. Allelic variants in these gene families represent potential targets for assisted breeding of high WSC levels. Overall, multiple lines of evidence corroborate the importance of glgC for increasing foliar WSC accumulation in white clover. Soil moisture deficit (SMD) tolerance was investigated in naturalised populations of white clover collected from 17 sites representing contrasting SMD across the South Island/Te Waipounamu of NZ. Weak genetic differentiation of populations was detected in analyses of GBS data, with three genetic clusters identified by ADMIXTURE. Outlier detection and environmental association analyses identified 64 SNPs significantly (p < 0.05) associated with environmental variation. Mapping of these SNPs to the white clover reference genome, together with gene ontology analyses, suggested some SNPs were associated with genes involved in carbohydrate metabolism and root morphology. A common set of allelic variants in a subset of the populations from high SMD environments may also identify targets for selective breeding, but this variation needs further investigation

Herbaceous Field Crops Cultivation

Herbaceous field crops include several hundred plant species worldly widespread for different end-uses, from food to no-food applications. Among them are included cereals, grain legumes, sugar beet, potato, cotton, tobacco, sunflower, safflower, rape, flax, soybean, alfalfa, clover spp. and other fodder crops, but only 15–20 species play a relevant role for the worldly global economy. Nowadays, to meet the food demand of the ever-increasing world population in a scenario of decreased arable lands, the development of holistic agricultural management approaches to boost contemporaneously yield and quality of herbaceous field crops is essential. Accordingly, this book represents an up-to-date collection of the current understanding of the impact of several agricultural management factors (i.e., genetic selection, planting density and arrangement, fertilization, irrigation, weed control and harvest time) on the yield and qualitative performances of 11 field crops (wheat, cardoon, potato, clary sage, basil, sugarcane, canola, cotton, tomato, lettuce and hemp). On the whole, the topics covered in this book will ensure students and academic readers, such as plant physiologists, environmental scientists, biotechnologists, botanists, soil chemists and agronomists, to get the information about the recent research advances on the eco-sustainable management cultivation of herbaceous field crops, with a particular focus on varietal development, soil nutrient and water management, weed control, etc

Modelling Plant Variety Dependent Least Limiting Water Range (LLWR)

Drought stress is a major limiting factor for yield on a global scale (Solh and van Ginkel, 2014), with drought effects being predicted to become more severe with increasing global temperatures (IPCC, 2014). Climate change is also expected to increase the frequency and severity of floods leading to root oxygen stress (Trenberth, 2011). At the same time, current agricultural practises are increasingly relying on heavy machinery leading to soil compaction and changes in soil structure (Chamen et al., 2003), reducing the rate of cell division in the root meristem, and decreasing cell expansion (Bengough and Mullins, 1990). As such, in order to reduce yield losses it is essential to understand the complex interaction between oxygen stress, water stress and mechanical stress (Mohammadi et al., 2010). The least limiting water range (LLWR) is one such model which relates the above-mentioned soil stressors in order to estimate the soil moisture range in a particular soil for which plants should be less limited in terms of growth. However, the extent to which the LLWR considers the influence of root traits in changing its boundaries is currently limited. In order to be able to assess the effects of root trait variability on the LLWR boundaries while manipulating the LLWR soil stressors a minirhizotron based system (RS) was developed. This cheap (~£10 per unit), acrylic based, A3 sized system enabled in situ imaging of roots and root hairs. Destructive sampling methods were also used to determine root border cell numbers and root tip geometry. To further optimise the process of data collection, Rcpp based image processing algorithms were developed to obtain automated estimates of the root traits of root length, root hair, root border cells and root tip eccentricity to further increase the efficiency of the RS phenotyping platform. To test how contrasting root traits influence the LLWR a plant phenotyping experiment was performed comparing four spring barley (Hordeum vulgare L.) varieties, Optic, KWS Sassy, Derkado and Golden Promise. Root growth rates both in the vertical and horizontal directions all increased with increasing water availability and decreasing substrate density. Root hair area did not vary significantly among treatments and between variaties. Root border cell count and root tip eccentricity increased with increasing substrate density but did not vary significantly across varieties. A root micro-trait based linear interaction model was developed to describe average root growth rates and it was demonstrated that root growth rates on average follow a linear patern for values >= 8 mm day-1. Root micro-traits mostly failed to correlate well with root growth rates except for a negative assosiation with root tip geometry (cor = -0.4192, p = 2e-05**)