Multi-sensor and multi-temporal high-throughput phenotyping for monitoring and early detection of water-limiting stress in soybean.

Soybean ( [L.] Merr.) production is susceptible to biotic and abiotic stresses, exacerbated by extreme weather events.

Persistent monitoring of insect-pests on sticky traps through hierarchical transfer learning and slicing-aided hyper inference.

Introduction: Effective monitoring of insect-pests is vital for safeguarding agricultural yields and ensuring food security. Recent advances in computer vision and machine learning have opened up significant possibilities of automated persistent monitoring of insect-pests through reliable detection and counting of insects in setups such as yellow sticky traps. However, this task is fraught with complexities, encompassing challenges such as, laborious dataset annotation, recognizing small insect-pests in low-resolution or distant images, and the intricate variations across insect-pests life stages and species classes.

Out-of-Distribution Detection Algorithms for Robust Insect Classification.

Plants encounter a variety of beneficial and harmful insects during their growth cycle. Accurate identification (i.e.

Models to estimate genetic gain of soybean seed yield from annual multi-environment field trials.

Simulations demonstrated that estimates of realized genetic gain from linear mixed models using regional trials are biased to some degree. Thus, we recommend multiple selected models to obtain a range of reasonable estimates. Genetic improvements of discrete characteristics are obvious and easy to demonstrate, while quantitative traits require reliable and accurate methods to disentangle the confounding genetic and non-genetic components.

Cyber-agricultural systems for crop breeding and sustainable production.

The cyber-agricultural system (CAS) represents an overarching framework of agriculture that leverages recent advances in ubiquitous sensing, artificial intelligence, smart actuators, and scalable cyberinfrastructure (CI) in both breeding and production agriculture. We discuss the recent progress and perspective of the three fundamental components of CAS - sensing, modeling, and actuation - and the emerging concept of agricultural digital twins (DTs). We also discuss how scalable CI is becoming a key enabler of smart agriculture.

Using machine learning enabled phenotyping to characterize nodulation in three early vegetative stages in soybean.

The symbiotic relationship between soybean [ L. (Merr.)] roots and bacteria () lead to the development of nodules, important legume root structures where atmospheric nitrogen (N) is fixed into bio-available ammonia (NH) for plant growth and development.

"Canopy fingerprints" for characterizing three-dimensional point cloud data of soybean canopies.

Advances in imaging hardware allow high throughput capture of the detailed three-dimensional (3D) structure of plant canopies. The point cloud data is typically post-processed to extract coarse-scale geometric features (like volume, surface area, height, etc.) for downstream analysis.

Intelligent control of integrated on-board charger with improved power quality and reduced charging transients.

This paper proposes an intelligent control scheme for a two-stage integrated onboard electric vehicle (EV) battery charger connected to a single-phase household outlet which offers a close to ideal battery charging profile with power factor correction feature. Generally, the front-end AC-DC​ conversion stage is controlled by dual loop proportional-integral (PI) controllers, and tuning their gain constants is a difficult task. Furthermore, to achieve a close to ideal charging profile for an EV battery, the DC-DC conversion stage switches from constant current (CC) and constant voltage (CV) mode after a certain state of charge (SOC) which may lead to discontinuity in the charging current and voltage.

Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean.

Using a reliable and accurate method to phenotype disease incidence and severity is essential to unravel the complex genetic architecture of disease resistance in plants, and to develop disease resistant cultivars. Genome-wide association studies (GWAS) involve phenotyping large numbers of accessions, and have been used for a myriad of traits. In field studies, genetic accessions are phenotyped across multiple environments and replications, which takes a significant amount of labor and resources.

Raffinose Family Oligosaccharides: Friend or Foe for Human and Plant Health?

Raffinose family oligosaccharides (RFOs) are widespread across the plant kingdom, and their concentrations are related to the environment, genotype, and harvest time. RFOs are known to carry out many functions in plants and humans. In this paper, we provide a comprehensive review of RFOs, including their beneficial and anti-nutritional properties.

Comparing Early Transcriptomic Responses of 18 Soybean () Genotypes to Iron Stress.

Iron deficiency chlorosis (IDC) is an abiotic stress that negatively affects soybean ( [L.] Merr.) production.

PATRIOT: A Pipeline for Tracing Identity-by-Descent for Chromosome Segments to Improve Genomic Prediction in Self-Pollinating Crop Species.

The lowering genotyping cost is ushering in a wider interest and adoption of genomic prediction and selection in plant breeding programs worldwide. However, improper conflation of historical and recent linkage disequilibrium between markers and genes restricts high accuracy of genomic prediction (GP). Multiple ancestors may share a common haplotype surrounding a gene, without sharing the same allele of that gene.

Using Machine Learning to Develop a Fully Automated Soybean Nodule Acquisition Pipeline (SNAP).

Nodules form on plant roots through the symbiotic relationship between soybean ( L. Merr.) roots and bacteria () and are an important structure where atmospheric nitrogen (N) is fixed into bioavailable ammonia (NH) for plant growth and development.

Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications.

Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop a machine learning (ML) approach adept at soybean ( L. (Merr.

Crop yield prediction integrating genotype and weather variables using deep learning.

Accurate prediction of crop yield supported by scientific and domain-relevant insights, is useful to improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop production. We used performance records from Uniform Soybean Tests (UST) in North America to build a Long Short Term Memory (LSTM)-Recurrent Neural Network based model that leveraged pedigree relatedness measures along with weekly weather parameters to dissect and predict genotype response in multiple-environments. Our proposed models outperformed other competing machine learning models such as Support Vector Regression with Radial Basis Function kernel (SVR-RBF), least absolute shrinkage and selection operator (LASSO) regression and the data-driven USDA model for yield prediction.

Meta-GWAS for quantitative trait loci identification in soybean.

We report a meta-Genome Wide Association Study involving 73 published studies in soybean [Glycine max L. (Merr.)] covering 17,556 unique accessions, with improved statistical power for robust detection of loci associated with a broad range of traits.

Conditional Mapping Identified Quantitative Trait Loci for Grain Protein Concentration Expressing Independently of Grain Yield in Canadian Durum Wheat.

Grain protein concentration (GPC) is an important trait in durum cultivar development as a major determinant of the nutritional value of grain and end-use product quality. However, it is challenging to simultaneously select both GPC and grain yield (GY) due to the negative correlation between them. To characterize quantitative trait loci (QTL) for GPC and understand the genetic relationship between GPC and GY in Canadian durum wheat, we performed both traditional and conditional QTL mapping using a doubled haploid (DH) population of 162 lines derived from Pelissier × Strongfield.

Soybean Root System Architecture Trait Study through Genotypic, Phenotypic, and Shape-Based Clusters.

We report a root system architecture (RSA) traits examination of a larger scale soybean accession set to study trait genetic diversity. Suffering from the limitation of scale, scope, and susceptibility to measurement variation, RSA traits are tedious to phenotype. Combining 35,448 SNPs with an imaging phenotyping platform, 292 accessions (replications = 14) were studied for RSA traits to decipher the genetic diversity.

One-Pot Synthesis of Proton Exchange Membranes from Anion Exchange Membrane Precursors.

Proton exchange membranes (PEMs) play a critical role in many electrochemical devices that could solve the shortcomings of current energy storage and conversion systems. Hydrocarbon-based PEMs are an attractive alternative for replacing the state-of-the-art perfluorosulfonic acid PEMs; however, synthetic routes are generally limited to sulfonation of aromatic units (pre- or postpolymerization functionalization). Here we disclose a facile and scalable one-pot synthetic method of converting an alkyl halide functionality to a sulfonate in polymer systems.

Historic recombination in a durum wheat breeding panel enables high-resolution mapping of Fusarium head blight resistance quantitative trait loci.

The durum wheat line DT696 is a source of moderate Fusarium head blight (FHB) resistance. Previous analysis using a bi-parental population identified two FHB resistance quantitative trait loci (QTL) on chromosome 5A: 5A1 was co-located with a plant height QTL, and 5A2 with a major maturity QTL. A Genome-Wide Association Study (GWAS) of DT696 derivative lines from 72 crosses based on multi-environment FHB resistance, plant height, and maturity phenotypic data was conducted to improve the mapping resolution and further elucidate the genetic relationship of height and maturity with FHB resistance.

Mapping quantitative trait loci associated with leaf rust resistance in five spring wheat populations using single nucleotide polymorphism markers.

Growing resistant wheat (Triticum aestivum L) varieties is an important strategy for the control of leaf rust, caused by Puccinia triticina Eriks. This study sought to identify the chromosomal location and effects of leaf rust resistance loci in five Canadian spring wheat cultivars. The parents and doubled haploid lines of crosses Carberry/AC Cadillac, Carberry/Vesper, Vesper/Lillian, Vesper/Stettler and Stettler/Red Fife were assessed for leaf rust severity and infection response in field nurseries in Canada near Swift Current, SK from 2013 to 2015, Morden, MB from 2015 to 2017 and Brandon, MB in 2016, and in New Zealand near Lincoln in 2014.

High Density Mapping of Quantitative Trait Loci Conferring Gluten Strength in Canadian Durum Wheat.

Gluten strength is one of the factors that determine the end-use quality of durum wheat and is an important breeding target for this crop. To characterize the quantitative trait loci (QTL) controlling gluten strength in Canadian durum wheat cultivars, a population of 162 doubled haploid (DH) lines segregating for gluten strength and derived from cv. Pelissier × cv.