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Articles 1 - 10 of 10
Full-Text Articles in Other Civil and Environmental Engineering
Statistical And Machine Learning Approaches To Describe Factors Affecting Preweaning Mortality Of Piglets, Md Towfiqur Rahman, Tami M. Brown-Brandl, Gary A. Rohrer, Sudhendu R. Sharma, Vamsi Manthena, Yeyin Shi
Statistical And Machine Learning Approaches To Describe Factors Affecting Preweaning Mortality Of Piglets, Md Towfiqur Rahman, Tami M. Brown-Brandl, Gary A. Rohrer, Sudhendu R. Sharma, Vamsi Manthena, Yeyin Shi
Biological Systems Engineering: Papers and Publications
High preweaning mortality (PWM) rates for piglets are a significant concern for the worldwide pork industries, causing economic loss and well-being issues. This study focused on identifying the factors affecting PWM, overlays, and predicting PWM using historical production data with statistical and machine learning models. Data were collected from 1,982 litters from the United States Meat Animal Research Center, Nebraska, over the years 2016 to 2021. Sows were housed in a farrowing building with three rooms, each with 20 farrowing crates, and taken care of by well-trained animal caretakers. A generalized linear model was used to analyze the various sow, …
High-Throughput Phenotyping Of Plant Leaf Morphological, Physiological, And Biochemical Traits On Multiple Scales Using Optical Sensing, Huichun Zhang, Lu Wang, Xiuliang Jin, Liming Bian, Yufeng Ge
High-Throughput Phenotyping Of Plant Leaf Morphological, Physiological, And Biochemical Traits On Multiple Scales Using Optical Sensing, Huichun Zhang, Lu Wang, Xiuliang Jin, Liming Bian, Yufeng Ge
Biological Systems Engineering: Papers and Publications
Acquisition of plant phenotypic information facilitates plant breeding, sheds light on gene action, and can be applied to optimize the quality of agricultural and forestry products. Because leaves often show the fastest responses to external environmental stimuli, leaf phenotypic traits are indicators of plant growth, health, and stress levels. Combination of new imaging sensors, image processing, and data analytics permits measurement over the full life span of plants at high temporal resolution and at several organizational levels from organs to individual plants to field populations of plants. We review the optical sensors and associated data analytics used for measuring morphological, …
Ag-Iot For Crop And Environment Monitoring: Past, Present, And Future, Nipuna Chamara, Md Didarul Islam, Geng Bai, Yeyin Shi, Yufeng Ge
Ag-Iot For Crop And Environment Monitoring: Past, Present, And Future, Nipuna Chamara, Md Didarul Islam, Geng Bai, Yeyin Shi, Yufeng Ge
Biological Systems Engineering: Papers and Publications
CONTEXT: Automated monitoring of the soil-plant-atmospheric continuum at a high spatiotemporal resolution is a key to transform the labor-intensive, experience-based decision making to an automatic, data-driven approach in agricultural production. Growers could make better management decisions by leveraging the real-time field data while researchers could utilize these data to answer key scientific questions. Traditionally, data collection in agricultural fields, which largely relies on human labor, can only generate limited numbers of data points with low resolution and accuracy. During the last two decades, crop monitoring has drastically evolved with the advancement of modern sensing technologies. Most importantly, the introduction …
Uavs For Vegetation Monitoring: Overview And Recent Scientific Contributions, Ana I. De Castro, Yeyin Shi, Joe Mari Maja, Jose M. Peña
Uavs For Vegetation Monitoring: Overview And Recent Scientific Contributions, Ana I. De Castro, Yeyin Shi, Joe Mari Maja, Jose M. Peña
Biological Systems Engineering: Papers and Publications
This paper reviewed a set of twenty-one original and innovative papers included in a special issue on UAVs for vegetation monitoring, which proposed new methods and techniques applied to diverse agricultural and forestry scenarios. Three general categories were considered: (1) sensors and vegetation indices used, (2) technological goals pursued, and (3) agroforestry applications. Some investigations focused on issues related to UAV flight operations, spatial resolution requirements, and computation and data analytics, while others studied the ability of UAVs for characterizing relevant vegetation features (mainly canopy cover and crop height) or for detecting different plant/crop stressors, such as nutrient content/deficiencies, water …
Editorial: Predictive Modeling Of Human Microbiota And Their Role In Health And Disease, Hyun-Seob Song, Stephen R. Lindemann, Dong-Yup Lee
Editorial: Predictive Modeling Of Human Microbiota And Their Role In Health And Disease, Hyun-Seob Song, Stephen R. Lindemann, Dong-Yup Lee
Biological Systems Engineering: Papers and Publications
No abstract provided.
Machine Learning Prediction Of Mechanical And Durability Properties Of Recycled Aggregates Concrete, Itzel Rosalia Nunez Vargas
Machine Learning Prediction Of Mechanical And Durability Properties Of Recycled Aggregates Concrete, Itzel Rosalia Nunez Vargas
Electronic Thesis and Dissertation Repository
Whilst recycled aggregate (RA) can alleviate the environmental footprint of concrete production and the landfilling of colossal amounts of demolition waste, there need for robust predictive tools for its effects on mechanical and durability properties. In this thesis, state-of-the-art machine learning (ML) models were deployed to predict properties of recycled aggregate concrete (RAC). A systematic review was performed to analyze pertinent ML techniques previously applied in the concrete technology field. Accordingly, three different ML methods were selected to determine the compressive strength of RAC and perform mixture proportioning optimization. Furthermore, a gradient boosting regression tree was used to study the …
Exploiting Earth Observation Data To Impute Groundwater Level Measurements With An Extreme Learning Machine, Steven Evans, Gustavious P. Williams, Norman L. Jones, Daniel P. Ames, E. James Nelson
Exploiting Earth Observation Data To Impute Groundwater Level Measurements With An Extreme Learning Machine, Steven Evans, Gustavious P. Williams, Norman L. Jones, Daniel P. Ames, E. James Nelson
Faculty Publications
Groundwater resources are expensive to develop and use; they are difficult to monitor and data collected from monitoring wells are often sporadic, often only available at irregular, infrequent, or brief intervals. Groundwater managers require an accurate understanding of historic groundwater storage trends to effectively manage groundwater resources, however, most if not all well records contain periods of missing data. To understand long-term trends, these missing data need to be imputed before trend analysis. We present a method to impute missing data at single wells, by exploiting data generated from Earth observations that are available globally. We use two soil moisture …
Predicting Escherichia Coli Loads In Cascading Dams With Machine Learning: An Integration Of Hydrometeorology, Animal Density And Grazing Pattern, Olufemi P. Abimbola, Aaron R. Mittelstet, Tiffany Messer, Elaine D. Berry, Shannon L. Bartelt-Hunt, Samuel Hansen
Predicting Escherichia Coli Loads In Cascading Dams With Machine Learning: An Integration Of Hydrometeorology, Animal Density And Grazing Pattern, Olufemi P. Abimbola, Aaron R. Mittelstet, Tiffany Messer, Elaine D. Berry, Shannon L. Bartelt-Hunt, Samuel Hansen
Biological Systems Engineering: Papers and Publications
Accurate prediction of Escherichia coli contamination in surface waters is challenging due to considerable uncertainty in the physical, chemical and biological variables that control E. coli occurrence and sources in surface waters. This study proposes a novel approach by integrating hydro-climatic variables as well as animal density and grazing pattern in the feature selection modeling phase to increase E. coli prediction accuracy for two cascading dams at the USMeat Animal Research Center (USMARC), Nebraska. Predictive models were developed using regression techniques and an artificial neural network (ANN). Two adaptive neuro-fuzzy inference system (ANFIS) structures including subtractive clustering and fuzzy c-means …
Predicting Escherichia Coli Loads In Cascading Dams With Machine Learning: An Integration Of Hydrometeorology, Animal Density And Grazing Pattern, Olufemi P. Abimbola, Aaron R. Mittelstet, Tiffany Messer, Elaine D. Berry, Shannon L. Bartelt-Hunt, Samuel Hansen
Predicting Escherichia Coli Loads In Cascading Dams With Machine Learning: An Integration Of Hydrometeorology, Animal Density And Grazing Pattern, Olufemi P. Abimbola, Aaron R. Mittelstet, Tiffany Messer, Elaine D. Berry, Shannon L. Bartelt-Hunt, Samuel Hansen
Biological Systems Engineering: Papers and Publications
Accurate prediction of Escherichia coli contamination in surface waters is challenging due to considerable uncertainty in the physical, chemical and biological variables that control E. coli occurrence and sources in surface waters. This study proposes a novel approach by integrating hydro-climatic variables as well as animal density and grazing pattern in the feature selection modeling phase to increase E. coli prediction accuracy for two cascading dams at the US Meat Animal Research Center (USMARC), Nebraska. Predictive models were developed using regression techniques and an artificial neural network (ANN). Two adaptive neuro-fuzzy inference system (ANFIS) structures including subtractive clustering and fuzzy …
High‑Throughput Analysis Of Leaf Physiological And Chemical Traits With Vis–Nir–Swir Spectroscopy: A Case Study With A Maize Diversity Panel, Yufeng Ge, Abbas Atefi, Huichun Zhang, Chenyong Miao, Raghuprakash Kastoori Ramamurthy, Brandi Sigmon, Jinliang Yang, James C. Schnable
High‑Throughput Analysis Of Leaf Physiological And Chemical Traits With Vis–Nir–Swir Spectroscopy: A Case Study With A Maize Diversity Panel, Yufeng Ge, Abbas Atefi, Huichun Zhang, Chenyong Miao, Raghuprakash Kastoori Ramamurthy, Brandi Sigmon, Jinliang Yang, James C. Schnable
Biological Systems Engineering: Papers and Publications
Hyperspectral reflectance data in the visible, near infrared and shortwave infrared range (VIS–NIR– SWIR, 400–2500 nm) are commonly used to nondestructively measure plant leaf properties. We investigated the usefulness of VIS–NIR–SWIR as a high-throughput tool to measure six leaf properties of maize plants including chlorophyll content (CHL), leaf water content (LWC), specific leaf area (SLA), nitrogen (N), phosphorus (P), and potassium (K). This assessment was performed using the lines of the maize diversity panel. Data were collected from plants grown in greenhouse condition, as well as in the field under two nitrogen application regimes. Leaf-level hyperspectral data were collected with …