Life Sciences Commons^™

A Deep Learning-Based Model For Plant Lesion Segmentation, Subtype Identification, And Survival Probability Estimation, Muhammad Shoaib, Babar Shah, Tariq Hussain, Akhtar Ali, Asad Ullah, Fayadh Alenezi, Tsanko Gechev, Farman Ali, Ikram Syed

All Works

Plants are the primary source of food for world’s population. Diseases in plants can cause yield loss, which can be mitigated by continual monitoring. Monitoring plant diseases manually is difficult and prone to errors. Using computer vision and artificial intelligence (AI) for the early identification of plant illnesses can prevent the negative consequences of diseases at the very beginning and overcome the limitations of continuous manual monitoring. The research focuses on the development of an automatic system capable of performing the segmentation of leaf lesions and the detection of disease without requiring human intervention. To get lesion region segmentation, we …

Image Analysis And Machine Learning In Agricultural Research, Xinzheng Chen

Doctor of Plant Health Program: Dissertations and Student Research

Agricultural research has been a focus for academia and industry to improve human well-being. Given the challenges in water scarcity, global warming, and increased prices of fertilizer, and fossil fuel, improving the efficiency of agricultural research has become even more critical. Data collection by humans presents several challenges including: 1) the subjectiveness and reproducibility when doing the visual evaluation, 2) safety when dealing with high toxicity chemicals or severe weather events, 3) mistakes cannot be avoided, and 4) low efficiency and speed.

Image analysis and machine learning are more versatile and advantageous in evaluating different plant characteristics, and this could …

Identifying Early-Life Behavior To Predict Mothering Ability In Swine Utilizing Nutrack System, Savannah Millburn

Department of Animal Science: Dissertations, Theses, and Student Research

Early recognition of indicator traits for swine reproduction and longevity supports economical selection decision making. Gilt activity is a key variable impacting a sow’s herd life and productivity. The purpose of this study was to examine early- life behaviors contributing to farrowing traits including gestation length (GL), number born alive (NBA), number weaned (NW), and herd life (HL). Herd life was a binary trait representing if a gilt was culled after one parity. Beginning at approximately 20 weeks of age, video recordings were taken on 480 gilts for 7 consecutive days and processed using the NUtrack system. Activity traits include …

Causal Forest Approach For Site-Specific Input Management Via On-Farm Precision Experimentation, Shunkei Kakimoto

Department of Agricultural Economics: Dissertations, Theses, and Student Research

Estimating site-specific crop yield response to changes to input (e.g., seed, fertilizer) management is a critical step in making economically optimal site-specific input management recommendations. Past studies have attempted to estimate yield response functions using various Machine Learning (ML) methods, including the Random Forest (RF), Boosted Random Forest (BRF), and Convolutional Neural Network (CNN) methods. This study proposes use of the Causal Forest (CF) model, which is one of the emerging ML methods that comprise “Causal Machine Learning.” Unlike previous yield-prediction-oriented ML methods, CF focuses strictly on estimating heterogeneous treatment effects (changes in yields that result from changes in input …

Non-Destructive Classification And Quality Evaluation Of Proso Millet Cultivars Using Nir Hyperspectral Imaging With Machine Learning, Laruen E. Doyle, Julia R. Loeb, Nader Ekramirad, Dipak K. Santra, Akinbode A. Adedeji

Department of Agronomy and Horticulture: Faculty Publications

Millet is a small-seeded cereal crop with big potential and remarkable characteristics such as high drought resistance, short growing time, low water footprint, and the ability to grow in acidic soil. There is a need to develop nondestructive methods for differentiation and evaluation of the quality attributes of different of proso millet cultivars grown in the U.S. Current methods of cultivar classification are either subjective or destructive, time consuming, not allowing for the whole population to be tested, and requiring trained operators and special equipment. In this study, the feasibility of using near-infrared (NIR) hyperspectral imaging (900-1700 nm) to predict …

Performance Of Machine Learning Classifiers In Classifying Stunting Among Under-Five Children In Zambia, Obvious Nchimunya Chilyabanyama, Roma Chilengi, Roma Chilengi, Michelo Simuyandi, Caroline C. Chisenga, Masuzyo Chirwa, Kalongo Hamusonde, Rakesh Kumar Saroj, Najeeha Talat Iqbal, Innocent Ngaruye

Department of Paediatrics and Child Health

Stunting is a global public health issue. We sought to train and evaluate machine learning (ML) classification algorithms on the Zambia Demographic Health Survey (ZDHS) dataset to predict stunting among children under the age of five in Zambia. We applied Logistic regression (LR), Random Forest (RF), SV classification (SVC), XG Boost (XgB) and Naïve Bayes (NB) algorithms to predict the probability of stunting among children under five years of age, on the 2018 ZDHS dataset. We calibrated predicted probabilities and plotted the calibration curves to compare model performance. We computed accuracy, recall, precision and F1 for each machine learning algorithm. …

Nabat Ml: Utilizing Deep Learning To Enable Crowdsourced Development Of Automated, Scalable Solutions For Documenting North American Bat Populations, Ali Khalighifar, Benjamin S. Gotthold, Erin Adams, Jenny Barnett, Laura O. Beard, Eric R. Britzke, Paul A. Burger, Kimberly Chase, Zackary Cordes, Paul M. Cryan, Emily Emily, Christopher T. Fill, Scott E. Gibson, G. Scott Haulton, Kathryn M. Irvine, Lara S. Katz, William L. Kendall, Christen A. Long, Oisin Mac Aodha, Tessa Mcburney, Sara Mccarthy, Matthew W. Mckown, Joy O'Keefe, Lucy D. Patterson, Kristopher A. Pitcher, Matthew Rustand, Jordi L. Segers, Kyle Seppanen, Jeremy L. Siemers, Christian Stratton, Bethany R. Straw, Theodore J. Weller, Brian E. Reichert

Nebraska Cooperative Fish and Wildlife Research Unit: Staff Publications

1. Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to use its technology infrastructure to assess status and trends of bat populations, while developing innovative and community-driven conservation solutions.

2. Here, we present NABat ML, an automated machine-learning algorithm that improves the scalability and scientific transparency of NABat acoustic monitoring. This model combines signal processing techniques and convolutional neural networks (CNNs) to detect and classify recorded bat echolocation calls. We developed our CNN model with internet-based computing resources (‘cloud environment’), and …

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Where To Invest Project Efforts For Greater Benefit: A Framework Formanagement Performance Mapping With Examples For Potato Seed Health, C. E. Buddenhagen, Y. Xing, J. L. Andrade-Piedra, G. A. Forbes, P. Kromann, I. Navarrete, S. Thomas-Sharma, Robin A. Choudhury, K. F. Andersen Onofre, E. Schulte-Geldermann

School of Earth, Environmental, and Marine Sciences Faculty Publications and Presentations

Policymakers and donors often need to identify the locations where technologies are most likely to have important effects, to increase the benefits from agricultural development or extension efforts. Higher-quality information may help to target the high-benefit locations, but often actions are needed with limited information. The value of information (VOI) in this context is formalized by evaluating the results of decision making guided by a set of specific information compared with the results of acting without considering that information. We present a framework for management performance mapping that includes evaluating the VOI for decision making about geographic priorities in regional …

Individual Beef Cattle Identification Using Muzzle Images And Deep Learning Techniques, Guoming Li, Galen E. Erickson, Yijie Xiong

Department of Animal Science: Faculty Publications

The ability to identify individual animals has gained great interest in beef feedlots to allow for animal tracking and all applications for precision management of individuals. This study assessed the feasibility and performance of a total of 59 deep learning models in identifying individual cattle with muzzle images. The best identification accuracy was 98.7%, and the fastest processing speed was 28.3 ms/image. A dataset containing 268 US feedlot cattle and 4923 muzzle images was published along with this article. This study demonstrates the great potential of using deep learning techniques to identify individual cattle using muzzle images and to support …

Computational Investigations Into Binding Dynamics Of Tau Protein Antibodies: Using Machine Learning And Biophysical Models To Build A Better Reality, Katherine Lee

University Scholar Projects

Misregulation of post-translational modifications of microtubule-associated protein tau is implicated in several neurodegenerative diseases including Alzheimer’s disease. Hyperphosphorylation of tau promotes aggregation of tau monomers into filaments which are common in tau-associated pathologies. Therefore, tau is a promising target for therapeutics and diagnostics. Recently, high-affinity, high-specificity single-chain variable fragment (scFv) antibodies against pThr-231 tau were generated and the most promising variant (scFv 3.24) displayed 20-fold increased binding affinity to pThr-231 tau compared to the wild-type. The scFv 3.24 variant contained five point mutations, and intriguingly none were in the tau binding site. The increased affinity was hypothesized to occur due …

Using Quantitative Imaging For Personalized Medicine In Pancreatic Cancer: A Review Of Radiomics And Deep Learning Applications, Kiersten Preuss, Nate Thach, Xiaoying Liang, Michael Baine, Justin Chen, Chi Zhang, Huijing Du, Hongfeng Yu, Chi Lin, Michael A. Hollingsworth, Dandan Zheng

School of Biological Sciences: Faculty Publications

As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by …

Two Heads Are Better Than One: Current Landscape Of Integrating Qsp And Machine Learning, Tongli Zhang, Ioannis P. Androulakis, Peter Bonate, Limei Cheng, Tomáš Helikar, Jaimit Parikh, Christopher Rackauckas, Kalyanasundaram Subramanian, Carolyn R. Cho

Department of Biochemistry: Faculty Publications

Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer ‘omics’ data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about …

A Probabilistic Framework For Behavioral Identification From Animal-Borne Accelerometers, Jane Dentinger, Luca Börger, Mark D. Holton, Ruholla Jafari-Marandi, Durham A. Norman, Brian K. Smith, Seth F. Oppenheimer, Bronson K. Strickland, Rory P. Wilson, Garrett M. Street

College of Forest Resources Publications and Scholarship

Many studies of animal distributions use habitat and climactic variables to explain patterns of observed space use. However, without behavioral information, we can only speculate as to why and how these characteristics are important to species persistence.

Animal-borne accelerometer and magnetometer data loggers can be used to detect behaviors and when coupled with telemetry improve our understanding of animal space use and habitat requirements. However, these loggers collect tremendous quantities of data requiring automated machine learning techniques to identify patterns in the data. Supervised machine learning requires a set of training signals with known behaviors to train the model to …

Fine-Scale Mapping Of Natural Ecological Communities Using Machine Learning Approaches, Parth Bhatt, Ann Maclean, Yvette Dickinson, Chandan Kumar

Michigan Tech Publications

Remote sensing technology has been used widely in mapping forest and wetland communities, primarily with moderate spatial resolution imagery and traditional classification techniques. The success of these mapping efforts varies widely. The natural communities of the Laurentian Mixed Forest are an important component of Upper Great Lakes ecosystems. Mapping and monitoring these communities using high spatial resolution imagery benefits resource management, conservation and restoration efforts. This study developed a robust classification approach to delineate natural habitat communities utilizing multispectral high-resolution (60 cm) National Agriculture Imagery Program (NAIP) imagery data. For accurate training set delineation, NAIP imagery, soils data and spectral …

Democratizing Bioinformatics Through Easily Accessible Software Platforms For Non-Experts In The Field, Konstantinos Krampis

Publications and Research

No abstract provided.

Corn Nitrogen Nutrition Index Prediction Improved By Integrating Genetic, Environmental, And Management Factors With Active Canopy Sensing Using Machine Learning, Dan Li, Yuxin Miao, Curtis J. Ransom, Gregory Mac Bean, Newell R. Kitchen, Fabián G. Fernández, John E. Sawyer, James J. Camberato, Paul R. Carter, Richard B. Ferguson, David W. Franzen, Carrie A. M. Laboski, Emerson D. Nafziger, John F. Shanahan

Department of Agronomy and Horticulture: Faculty Publications

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen …

Identifying Conifer Tree Vs. Deciduous Shrub And Tree Regeneration Trajectories In A Space-For-Time Boreal Peatland Fire Chronosequence Using Multispectral Lidar, Humaira Enayetullah, Laura Chasmer, Christopher Hopkinson, Dan Thompson, Danielle Cobbaert

Aspen Bibliography

Wildland fires and anthropogenic disturbances can cause changes in vegetation species composition and structure in boreal peatlands. These could potentially alter regeneration trajectories following severe fire or through cumulative impacts of climate-mediated drying, fire, and/or anthropogenic disturbance. We used lidar-derived point cloud metrics, and site-specific locational attributes to assess trajectories of post-disturbance vegetation regeneration in boreal peatlands south of Fort McMurray, Alberta, Canada using a space-for-time-chronosequence. The objectives were to (a) develop methods to identify conifer trees vs. deciduous shrubs and trees using multi-spectral lidar data, (b) quantify the proportional coverage of shrubs and trees to determine environmental conditions driving …

Corn Nitrogen Nutrition Index Prediction Improved By Integrating Genetic, Environmental, And Management Factors With Active Canopy Sensing Using Machine Learning, Dan Li, Yuxin Miao, Curtis J. Ransom, Gregory Mac Bean, Newell R. Kitchen, Fabián G. Fernández, John E. Sawyer, James J. Camberato, Paul R. Carter, Richard B. Ferguson, David W. Franzen, Carrie A.M. Laboski, Emerson D. Nafziger, John F. Shanahan

United States Department of Agriculture-Agricultural Research Service / University of Nebraska-Lincoln: Faculty Publications

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen …

Evaluation Of Uav-Derived Multimodal Remote Sensing Data For Biomass Prediction And Drought Tolerance Assessment In Bioenergy Sorghum, Jiating Li, Daniel P. Schachtman, Cody F. Creech, Lin Wang, Yufeng Ge, Yeyin Shi

Department of Agronomy and Horticulture: Faculty Publications

Screening for drought tolerance is critical to ensure high biomass production of bioenergy sorghum in arid or semi-arid environments. The bottleneck in drought tolerance selection is the challenge of accurately predicting biomass for a large number of genotypes. Although biomass prediction by low-altitude remote sensing has been widely investigated on various crops, the performance of the predictions are not consistent, especially when applied in a breeding context with hundreds of genotypes. In some cases, biomass prediction of a large group of genotypes benefited from multimodal remote sensing data; while in other cases, the benefits were not obvious. In this study, …

Whooping Crane Stay Length In Relation To Stopover Site Characteristics, Andrew J. Caven, Aaron T. Pearse, David A. Brandt, Mary J. Harner, Greg D. Wright, David M. Baasch, Emma M. Brinley Buckley, Kristine L. Metzger, Matthew R. Rabbe,, Anne E. Lacy

Proceedings of the North American Crane Workshop

Whooping crane (Grus americana) migratory stopovers can vary in length from hours to more than a month. Stopover sites provide food resources and safety essential for the completion of migration. Factors such as weather, climate, demographics of migrating groups, and physiological condition of migrants influence migratory movements of cranes (Gruidae) to varying degrees. However, little research has examined the relationship between habitat characteristics and stopover stay length in cranes. Site quality may relate to stay length with longer stays that allow individuals to improve body condition, or with shorter stays because of increased foraging efficiency. We examined this …

Cbp60-Db: An Alphafold-Predicted Plant Kingdom-Wide Database Of The Calmodulin-Binding Protein 60 (Cbp60) Protein Family With A Novel Structural Clustering Algorithm, Keaun Amani, Vanessa Shivnauth, Christian Castroverde

Biology Faculty Publications

Molecular genetic analyses in the model species Arabidopsis thaliana have demonstrated the major roles of different CAM-BINDING PROTEIN 60 (CBP60) proteins in growth, stress signaling, and immune responses. Prominently, CBP60g and SARD1 are paralogous CBP60 transcription factors that regulate numerous components of the immune system, such as cell surface and intracellular immune receptors, MAP kinases, WRKY transcription factors, and biosynthetic enzymes for immunity-activating metabolites salicylic acid (SA) and N-hydroxypipecolic acid (NHP). However, their function, regulation and diversification in most species remain unclear. Here we have created CBP60-DB, a structural and bioinformatic database that comprehensively characterized 1052 CBP60 gene homologs …