Unlocking Insights into Crop Growth and Nutrient Distribution: A Geospatial Analysis Approach Using Satellite Imagery and Soil Data

Accurate monitoring of crop growth and nutrient distribution is crucial for optimizing agricultural practices, promoting a sustainable environment, and ensuring long-term food production. In this study, we propose a novel and comprehensive approach to monitor crop growth and nutrient distribution in large-scale agricultural landscapes. Our methodology combines advanced geospatial and temporal analysis techniques, providing valuable insights into the intricate relationships between crop health, soil nutrients, and other essential soil properties. To monitor vegetation dynamics, we obtained data from the IBM EIS (Environment Intelligence Suite) and processed it using our HPC (High-Performance Computing) infrastructure. This is ingested into our CRADLE (Common Research Analytics and Data Lifecycle Environment). The IBM EIS consists of vast amounts of geospatial data curated from diverse sources, readily available for analysis. Leveraging the Normalized Difference Vegetation Index (NDVI) algorithm and MODIS Aqua satellite imagery, we classified vegetation on a daily basis, yielding a detailed assessment of land use and growth. Additionally, by integrating MODIS Aqua data with USDA Historical Crop planting data, we can identify the dominant crops in each region and monitor their growth and health across Texas and Ohio during 2019. To investigate soil properties and their influence on crop health, we utilize prominent soil databases from IBM EIS such as The Soil Survey Geographic Database (SSURGO) and the World Soil Information Service (WoSIS). These databases provide essential information on key soil properties, including pH, texture, water holding capacity, and organic carbon. By correlating these properties with soil nitrogen content, we can assess their interdependencies and infer their impacts on crop health. Furthermore, we analyze the correlation between crop health and nitrogen content, gaining valuable insights into the effects of soil nitrogen on crop well-being. By integrating remote sensing technology, soil science, and data science, this interdisciplinary study contributes to the development of sustainable agricultural management strategies. The findings of this research enhance food production capabilities and provide valuable information for policy decision-making, ultimately promoting environmental conservation within large-scale agricultural systems

Extreme Value Statistics Analysis of Process Defects in Additive Manufacturing Materials

Fatigue and fracture studies focused on process defects that occur in Additive Manufacturing (AM) materials have shown that defect populations possess features which are better measured with extreme value statistics (EVS). In AM alloys, defect occurrences increase with material volume. This situation facilitates the need to model process defects in the path of fatigue crack growth with suitable statistical tools, such as EVS, which is more cost-effective when compared to destructive experiments. The application of EVS on defect space features helps determine the difference in defects present on fracture surfaces. As the fatigue quality of any material depends on its extreme value flaws, we use the Block Maxima and Peak over Threshold methodologies to study the distribution of the features of the defects in AM Ti-6Al-4V and make recommendations for the distributions of best fit based on different scenarios with different ranges of complexity of different defect types

Integrating Multiscale Geospatial Analysis for Monitoring Crop Growth, Nutrient Distribution, and Hydrological Dynamics in Large-Scale Agricultural Systems

These are GIFs generated from Geospatial analysis for Ohio, Florida and Texas for 2019 corresponding to Land use/ Vegetation Classification (NDVI), Crops Growth/Healthiness, and Correlation

CWRU SDLE Research Center

Open Data and Open Codes shared from CWRU SDLE Research Cente