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Meeting MS&T24: Materials Science & Technology
Symposium Materials Informatics for Images and Multi-dimensional Datasets
Presentation Title Advancing Sustainable Agriculture through Multiscale Spatiotemporal Data Integration and High-Performance Computing
Author(s) Olatunde David Akanbi, Vibha Mandayam, Ethan Tobey, Adaezeogo Ezeogo-Enwo, HyangMok Baek, Atharva Gupta, Laura S Bruckman, Yinghui Wu, Erika I Barcelos, Jeffrey M Yarus, Roger H French
On-Site Speaker (Planned) Olatunde David Akanbi
Abstract Scope This research presents a transformative approach to sustainable agriculture by leveraging multiscale spatiotemporal data integration and high-performance computing. Utilizing a comprehensive framework called CRADLE (Common Research Analytics and Data Lifecycle Environment), we efficiently integrate and process diverse geospatial datasets, including satellite imagery, soil surveys, and environmental variables. Our work encompasses analyses from daily crop growth monitoring using Normalized Difference Vegetation Index time series from the MODIS Aqua satellite to hydrological assessments, precipitation monitoring using NASA's Integrated Multi-satellitE Retrievals for GPM (IMERG) dataset, and soil nutrient modeling. Advanced spatiotemporal analytical techniques uncover complex spatiotemporal patterns and relationships within agricultural systems. Novel datasets, including Sentinel-5P methane observations and locations of Concentrated Animal Feeding Operations and Wastewater Treatment Plants, enable the identification of emission hotspots and development of targeted mitigation strategies. Our holistic, data-driven approach empowers stakeholders to make informed decisions, optimize resource management, and promote sustainable agricultural practices.


Advancing AI-Driven Analysis of Synchrotron Data via FAIR Practices, Ontology and Knowledge Graphs
Advancing Sustainable Agriculture through Multiscale Spatiotemporal Data Integration and High-Performance Computing
Aligning Grains in Time-Series Laboratory Diffraction Contrast Tomography (LabDCT) Data for Machine Learning of Microstructure Evolution
Autonomous approaches for determining structure-processing-property relationships in materials
Categorization of Fracture Surfaces using Deep Learning-enabled 2D Image Analysis
Deep Learning Accelerated Lab-Scale X-Ray Computed Tomography of Low-Melting-Point Solder Alloys Used in Heterogeneously Integrated Semiconductor Packages
Enhancing Rietveld Refinement Analyses with Machine Learning Techniques
Extraction of Local Scalar 3D Microstructural Properties of SOFC Electrodes from 2D Micrographs using Convolutional Neural Networks.
Feature Extraction from SEM Images of Fatigue Fracture Surfaces
Foundation models for multimodal data mining with applications in materials science.
Hierarchical Bayesian Models for Automating Structural Materials Characterization
Machine Learning Enhanced Data Analytics for Transmission Electron Microscopy
Synthetic 3D Microstructure Generation of Solid Oxide Cell Electrodes using Denoising Diffusion Models

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