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.

Evaluating Neural Radiance Fields for 3D Plant Geometry Reconstruction in Field Conditions.

We evaluate different Neural Radiance Field (NeRF) techniques for the 3D reconstruction of plants in varied environments, from indoor settings to outdoor fields. Traditional methods usually fail to capture the complex geometric details of plants, which is crucial for phenotyping and breeding studies. We evaluate the reconstruction fidelity of NeRFs in 3 scenarios with increasing complexity and compare the results with the point cloud obtained using light detection and ranging as ground truth.

Rapid Estimation of the Intermolecular Electronic Couplings and Charge-Carrier Mobilities of Crystalline Molecular Organic Semiconductors through a Machine Learning Pipeline.

Organic semiconductors (OSC) offer tremendous potential across a wide range of (opto)electronic applications. OSC development, however, is often limited by trial-and-error design, with computational modeling approaches deployed to evaluate and screen candidates through a suite of molecular and materials descriptors that generally require hours to days of computational time to accumulate. Such bottlenecks slow the pace and limit the exploration of the vast chemical space comprising OSC.

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.

In-Droplet Electromechanical Cell Lysis and Enhanced Enzymatic Assay Driven by Ion Concentration Polarization.

Droplets enable the encapsulation of cells for their analysis in isolated domains. The study of molecular signatures (including genes, proteins, and metabolites) from a few or single cells is critical for identifying key subpopulations. However, dealing with biological analytes at low concentrations requires long incubation times and amplification to achieve the requisite signal strength.

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.

Self-supervised maize kernel classification and segmentation for embryo identification.

Introduction: Computer vision and deep learning (DL) techniques have succeeded in a wide range of diverse fields. Recently, these techniques have been successfully deployed in plant science applications to address food security, productivity, and environmental sustainability problems for a growing global population. However, training these DL models often necessitates the large-scale manual annotation of data which frequently becomes a tedious and time-and-resource- intensive process.

"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.

Electrokinetic Enrichment and Label-Free Electrochemical Detection of Nucleic Acids by Conduction of Ions along the Surface of Bioconjugated Beads.

In this paper, we report a method to integrate the electrokinetic pre-enrichment of nucleic acids within a bed of probe-modified microbeads with their label-free electrochemical detection. In this detection scheme, hybridization of locally enriched target nucleic acids to the beads modulates the conduction of ions along the bead surfaces. This is a fundamental advancement in that this mechanism is similar to that observed in nanopore sensors, yet occurs in a bed of microbeads with microscale interstices.

A computational framework for transmission risk assessment of aerosolized particles in classrooms.

Unlabelled: Infectious airborne diseases like the recent COVID-19 pandemic render confined spaces high-risk areas. However, in-person activities like teaching in classroom settings and government services are often expected to continue or restart quickly. It becomes important to evaluate the risk of airborne disease transmission while accounting for the physical presence of humans, furniture, and electronic equipment, as well as ventilation.

Electronic, redox, and optical property prediction of organic π-conjugated molecules through a hierarchy of machine learning approaches.

Accelerating the development of π-conjugated molecules for applications such as energy generation and storage, catalysis, sensing, pharmaceuticals, and (semi)conducting technologies requires rapid and accurate evaluation of the electronic, redox, or optical properties. While high-throughput computational screening has proven to be a tremendous aid in this regard, machine learning (ML) and other data-driven methods can further enable orders of magnitude reduction in time while at the same time providing dramatic increases in the chemical space that is explored. However, the lack of benchmark datasets containing the electronic, redox, and optical properties that characterize the diverse, known chemical space of organic π-conjugated molecules limits ML model development.

Optimization framework for patient-specific modeling under uncertainty.

Estimating a patient-specific computational model's parameters relies on data that is often unreliable and ill-suited for a deterministic approach. We develop an optimization-based uncertainty quantification framework for probabilistic model tuning that discovers model inputs distributions that generate target output distributions. Probabilistic sampling is performed using a surrogate model for computational efficiency, and a general distribution parameterization is used to describe each input.

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.

Protocols for In Vivo Doubled Haploid (DH) Technology in Maize Breeding: From Haploid Inducer Development to Haploid Genome Doubling.

Doubled haploid (DH) technology reduces the time required to obtain homozygous genotypes and accelerates plant breeding among other advantages. It is established in major crop species such as wheat, barley, maize, and canola. DH lines can be produced by both in vitro and in vivo methods and the latter is focused here.

Dissecting the Root Phenotypic and Genotypic Variability of the Iowa Mung Bean Diversity Panel.

Mung bean [ (L.) Wilczek] is a drought-tolerant, short-duration crop, and a rich source of protein and other valuable minerals, vitamins, and antioxidants. The main objectives of this research were (1) to study the root traits related with the phenotypic and genetic diversity of 375 mung bean genotypes of the Iowa (IA) diversity panel and (2) to conduct genome-wide association studies of root-related traits using the Automated Root Image Analysis (ARIA) software.

Out-of-plane faradaic ion concentration polarization: stable focusing of charged analytes at a three-dimensional porous electrode.

Ion concentration polarization (ICP) accomplishes preconcentration for bioanalysis by localized depletion of electrolyte ions, thereby generating a gradient in electric field strength that facilitates electrokinetic focusing of charged analytes by their electromigration against opposing fluid flow. Such ICP focusing has been shown to accomplish up to a million-fold enrichment of nucleic acids and proteins in single-stage preconcentrators. However, the rate at which the sample volume is swept is limited, requiring several hours to achieve these high enrichment factors.

Impedance-Based Nanoporous Anodized Alumina/ITO Platforms for Label-Free Biosensors.

We report an experimental and computational approach for the fabrication and characterization of a highly sensitive and responsive label-free biosensor that does not require the presence of redox couples in electrolytes for sensitive electrochemical detection. The sensor is based on an aptamer-functionalized transparent electrode composed of nanoporous anodized alumina (NAA) grown on indium tin oxide (ITO)-covered glass. Electrochemical impedance changes in a thrombin binding aptamer (TBA)-functionalized NAA/ITO/glass electrode due to specific binding of α-thrombin are monitored for protein detection.

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.

Polarized X-ray scattering measures molecular orientation in polymer-grafted nanoparticles.

Polymer chains are attached to nanoparticle surfaces for many purposes, including altering solubility, influencing aggregation, dispersion, and even tailoring immune responses in drug delivery. The most unique structural motif of polymer-grafted nanoparticles (PGNs) is the high-density region in the corona where polymer chains are stretched under significant confinement, but orientation of these chains has never been measured because conventional nanoscale-resolved measurements lack sensitivity to polymer orientation in amorphous regions. Here, we directly measure local chain orientation in polystyrene grafted gold nanoparticles using polarized resonant soft X-ray scattering (P-RSoXS).

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.

UAS-Based Plant Phenotyping for Research and Breeding Applications.

Unmanned aircraft system (UAS) is a particularly powerful tool for plant phenotyping, due to reasonable cost of procurement and deployment, ease and flexibility for control and operation, ability to reconfigure sensor payloads to diversify sensing, and the ability to seamlessly fit into a larger connected phenotyping network. These advantages have expanded the use of UAS-based plant phenotyping approach in research and breeding applications. This paper reviews the state of the art in the deployment, collection, curation, storage, and analysis of data from UAS-based phenotyping platforms.

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.

Iowa Urban FEWS: Integrating Social and Biophysical Models for Exploration of Urban Food, Energy, and Water Systems.

Most people in the world live in urban areas, and their high population densities, heavy reliance on external sources of food, energy, and water, and disproportionately large waste production result in severe and cumulative negative environmental effects. Integrated study of urban areas requires a system-of-systems analytical framework that includes modeling with social and biophysical data. We describe preliminary work toward an integrated urban food-energy-water systems (FEWS) analysis using co-simulation for assessment of current and future conditions, with an emphasis on local (urban and urban-adjacent) food production.

Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping.

The accuracy of trait measurements greatly affects the quality of genetic analyses. During automated phenotyping, trait measurement errors, i.e.