A document by Ariyan Zarei. Click on the document to view its contents.

Agricultural water resources are threatened by climatic variability and increased competition for available freshwater resources. In order to mitigate the effect of climate change on cotton production, breeders are increasing their efforts on improving drought tolerance in this essential fiber crop. To achieve this, effective screening of diverse germplasm is needed to identify useful genetic variation that can be utilized for crop improvement. Within the last decade, unmanned aerial vehicles (UAVs) have led to the ability to quickly and reliably image large areas while simultaneously decreasing temporal effects associated with a large time window for data collection. This technology allows researchers to scale their phenotyping efforts, enabling studies that utilize mapping and monitoring efforts such as plant water stress detection. In this study, we used UAV-based thermal imagery to screen a diverse population of over 350 different genotypes of cotton in order to locate varieties that exhibit cooler canopies. This diversity panel was grown under two contrasting levels of irrigation, well-watered and water-limited, with data collection flights occurring weekly for three months during the season. The thermal images were clipped to plot boundaries, soil and plant pixels were segmented, and average temperatures were extracted to identify potential drought tolerant varieties. The objectives of this study were to (i) demonstrate that UAV-based thermal imagery, along with our calibration methods, can be used to render accurate plant canopy temperature values and (ii) identify cotton genotypes that outperform others in a drought-stressed environment.

We present several methods for improving plant reconstruction from multiple 3D observations. Producing 3D data useful for plant phenotyping requires proximal sensing (e.g. line scanner, depth camera) at multiple incident angles (φ) and often with multiple passes. These resulting individual point clouds must then be assembled into a single point cloud for analysis. Our interest in improving the registration of individual plants is focused specifically on observations made within field settings which present additional challenges over laboratory 3D scans, where background, overlap and light conditions can be controlled. To develop these methods, we use several season’s worth of data from the University of Arizona’s Field Scanalyzer located in Maricopa, Arizona. Our approach prioritizes: (1) plant completeness, (2) noise reduction, (3) temporal similarity and (4) computational efficiency. The first priority is accomplished simply by prioritizing individual point clouds that contain the majority of the individual plant. 3D field scanning can result in component point clouds that are from near-identical φ and cover the same portions of the individual plant. This results in both additional noise and uncertainties due to small georeferencing errors and plant movement between scans. Thus, we remove the data that is furthest in time with non-unique φ in order to achieve priorities 2 and 3. Our method results in small scene reconstruction which has low memory and computational demands. In order to improve registration further, we investigate iterative closest point (ICP) registration fitting using weights defined by crop height distributions and semantic segmentation point labeling.

Previous crop yield improvements have been largely due to the implementation of new management strategies, mechanization, and application of emerging technologies. While these approaches have led to stable, linear improvements, increases in crop yields are currently plateauing. The use and improvement of rapid, automated, and accurate phenomic selection methods leveraging high-resolution data collected throughout a growing season could help identify stress-adaptive traits to meet the growing global food demand. As the capacity of phenomics to generate larger and higher dimensional data sets improves, there is an urgent need to develop and implement robust and scalable data processing pipelines for rapid turnaround of processed results. Current phenomics processing pipelines lack modularity and the ability to exploit the distributed computational infrastructure required for machine learning (ML)-based workloads. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines that aim to improve data processing efficiency for plant science research. PO integrates open-source frameworks for distributed task management on local, cloud, or high-performance computing (HPC) systems. Each pipeline component is available as a standalone container which can be independently deployed or linked into a pipeline. Additionally, researchers can swap between available containers or integrate new ones suited to their specific research. PO extracts phenotype trait values such as volume, height, canopy temperature, and maximum quantum efficiency (F v /F m) of photosystem II from data captured in field settings, enabling the study of phenotypic variation for elucidation of the genetic components of quantitative traits.