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IntroductionAdvancements in machine learning (ML) algorithms that make predictions from data without being explicitly programmed and the increased computational speeds of graphics processing units (GPUs) over the last decade have led to remarkable progress in the capabilities of ML. In many fields, including agriculture, this progress has outpaced the availability of sufficiently diverse and high-quality datasets, which now serve as a limiting factor. While many agricultural use cases appear feasible with current compute resources and ML algorithms, the lack of reusable hardware and software components, referred to as cyberinfrastructure (CI), for collecting, transmitting, cleaning, labeling, and training datasets is a major hindrance toward developing solutions to address agricultural use cases. This study focuses on addressing these challenges by exploring the collection, processing, and training of ML models using a multimodal dataset and providing a vision for agriculture-focused CI to accelerate innovation in the field. MethodsData were collected during the 2023 growing season from three agricultural research locations across Ohio. The dataset includes 1 terabyte (TB) of multimodal data, comprising Unmanned Aerial System (UAS) imagery (RGB and multispectral), as well as soil and weather sensor data. The two primary crops studied were corn and soybean, which are the state's most widely cultivated crops. The data collected and processed from this study were used to train ML models to make predictions of crop growth stage, soil moisture, and final yield. ResultsThe exercise of processing this dataset resulted in four CI components that can be used to provide higher accuracy predictions in the agricultural domain. These components included (1) a UAS imagery pipeline that reduced processing time and improved image quality over standard methods, (2) a tabular data pipeline that aggregated data from multiple sources and temporal resolutions and aligned it with a common temporal resolution, (3) an approach to adapting the model architecture for a vision transformer (ViT) that incorporates agricultural domain expertise, and (4) a data visualization prototype that was used to identify outliers and improve trust in the data. DiscussionFurther work will be aimed at maturing the CI components and implementing them on high performance computing (HPC). There are open questions as to how CI components like these can best be leveraged to serve the needs of the agricultural community to accelerate the development of ML applications in agriculture.
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This content will become publicly available on April 1, 2026
Utility of UAS Photogrammetry and Thermal Sensors for Identifying Locations and Understanding Functions of Puebloan Gravel Mulch Fields in Northern New Mexico
ABSTRACT This article presents results from an unmanned aircraft system (UAS) aerial remote sensing study to improve understanding of Pueblo agricultural features in the Northern Rio Grande area of New Mexico that were in use by the 13th centuryad. It builds on previous archaeological research that has focused on recording precontact and historic Pueblo agricultural practices, pollen analyses and paleoclimatic reconstruction. Evidence suggests that Pueblo people were successfully growing crops including maize, cotton and wheat, in areas where, based on environmental conditions, they could not necessarily grow. This study seeks to better understand the environmental modifications employed by Pueblo peoples to enable growth of these crops. Cobble‐bordered gravel mulch field systems, thought to retain heat and moisture, are located throughout the study area. This article discusses the utility of airborne photogrammetry to locate and map gravel mulch fields on the landscape. Geographic information system (GIS) analysis of the UAS‐derived digital surface model includes slope, aspect and water flow direction and sink to shed light on gravel mulch field function. The article also discusses the potential of handheld and airborne infrared imaging for assessing the thermoregulation of these fields. Final consideration of how the survey results align with the priorities of the Tewa people for future arid‐land farming demonstrates additional utility of the approach.
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- Award ID(s):
- 2309808
- PAR ID:
- 10634353
- Publisher / Repository:
- WIley
- Date Published:
- Journal Name:
- Archaeological Prospection
- Volume:
- 32
- Issue:
- 2
- ISSN:
- 1075-2196
- Page Range / eLocation ID:
- 313 to 327
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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