Adaptive Robotic Chassis (ARC)

The ARC is a width adjusting agricultural robot and accommodates auxiliary functions for supporting crop production and maintenance. Easily interchangeable payloads and components provide a modular solution to perform focused crop surveying functions with the potential for herbicide distribution, weeding, and harvesting while driving through varying crop rows. The potential auxiliary functions will be implemented by future teams with this year\u27s effort being put toward finishing the physical chassis. The final product was successfully designed to weigh approximately 600 pounds targeting rolling speeds of0.90 fps to 2.30 fps with proof of concept shown in testing consisting of chain drive attached to wheels to show speeds are attainable as well as bench tests to show differential control capabilities

Influence of soil granulometry on average body size in soil ant assemblages: Implications for bioindication

© 2017 Associação Brasileira de Ciência Ecológica e Conservação. Soil granulometric composition can impose constraints on ant species living in ground habitats, being an important factor in defining the habitat templet, which describes how certain animal life histories, including the trait of body size, can be selected. The ant fauna plays a central role in soil formation, and a vast literature describes such influence, but not the converse. Along with termites, worms and other invertebrates, these organisms promote the formation of channels, pores, and aggregates that influence gases and water moving through the soil profile. On the other hand, it is important to understand whether soil traits constrain insect colonization, so we here ask how soil traits can influence niche specificities, which seems to be a neglected ecological issue. A literature search using the key words 'ants or Formicidae' and 'soil structure or pedogenesis' revealed numerous references dealing with the influence of ants on soil, but not conversely. We here present a novel geomorphologic approach to habitat templets for two distinct riparian Neotropical ecosystems, based on the amalgamation of soil/sediment analysis with ecological processes and ant species biology. We found that predominance of fine grains favoured the preponderance of small ant species at a threshold of < 5. mm in body length. Based on this, we propose the use of a quantitative, theoretically sound, statistical approach to bioindication

The politics of state compliance with international \u201csoft law\u201d in finance

Why do jurisdictions comply (or not) with international soft law in finance? This research systematically links international and domestic explanations of compliance by highlighting the \u201cdisjuncture\u201d between the international standard-setting process and the process of domestic compliance. Two causal mechanisms that affect compliance are identified. In the uploading stage, elected officials delegate the making of international soft law to domestic regulators; large, internationally active financial institutions mobilize extensively and, to a large extent, successfully. In the downloading stage, domestic interest groups team up with elected officials in order to resist compliance with international soft law that has negative distributional implications for domestic constituencies. These arguments are illustrated through a structured, focused comparison, and process tracing of the mixed record of compliance of the two main jurisdictions worldwide\u2014the United States and the European Union\u2014with the main international banking standards, the Basel Accords

PhytoOracle: Scalable, modular phenomics data processing pipelines

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to (i) improve data processing efficiency; (ii) provide an extensible, reproducible computing framework; and (iii) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area). Copyright © 2023 Gonzalez, Zarei, Hendler, Simmons, Zarei, Demieville, Strand, Rozzi, Calleja, Ellingson, Cosi, Davey, Lavelle, Truco, Swetnam, Merchant, Michelmore, Lyons and Pauli.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

PhytoOracle: Scalable, modular phenomics data processing pipelines

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to (i) improve data processing efficiency; (ii) provide an extensible, reproducible computing framework; and (iii) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area)