Evaluating two high-throughput phenotyping platforms at early stages of the post-harvest pipeline of sweetpotatoes

Autor: Enrique E. Pena Martinez, Michael Kudenov, Hoang Nguyen, Daniela S. Jones, Cranos Williams

Publikováno v: Smart Agricultural Technology, Vol 8, Iss , Pp 100469- (2024)

Recent advancements in artificial intelligence and big data analytics introduce new tools that can enhance the packing efficiency of sweetpotatoes (Ipomoea batatas) (SPs). In this study, we focused on the quantification of inventory as early in the p

Externí odkaz: https://doaj.org/article/31260e840b604dfca33e88cc2445c09a

Automated Imaging, Tracking, and Analytics Pipeline for Differentiating Environmental Effects on Root Meristematic Cell Division

Autor: Eli Buckner, Imani Madison, Hsuan Chou, Anna Matthiadis, Charles E. Melvin, Rosangela Sozzani, Cranos Williams, Terri A. Long

Publikováno v: Frontiers in Plant Science, Vol 10 (2019)

Exposure of plants to abiotic stresses, whether individually or in combination, triggers dynamic changes to gene regulation. These responses induce distinct changes in phenotypic characteristics, enabling the plant to adapt to changing environments.

Externí odkaz: https://doaj.org/article/27304e38849048a6b677cec22cd66b8f

Clustering and Differential Alignment Algorithm: Identification of Early Stage Regulators in the Arabidopsis thaliana Iron Deficiency Response.

Autor: Alexandr Koryachko, Anna Matthiadis, Durreshahwar Muhammad, Jessica Foret, Siobhan M Brady, Joel J Ducoste, James Tuck, Terri A Long, Cranos Williams

Publikováno v: PLoS ONE, Vol 10, Iss 8, p e0136591 (2015)

Time course transcriptome datasets are commonly used to predict key gene regulators associated with stress responses and to explore gene functionality. Techniques developed to extract causal relationships between genes from high throughput time cours

Externí odkaz: https://doaj.org/article/699113b474f84c6b9cab203ff3ff5d99

Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies

Autor: Sebastiano Busato, Max Gordon, Meenal Chaudhari, Ib Jensen, Turgut Akyol, Stig Andersen, Cranos Williams

Publikováno v: Busato, S, Gordon, M, Chaudhari, M, Thorsgaard Jensen, I, Akyol, T Y, Andersen, S U & Williams, C 2023, ' Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies ', Current Opinion in Plant Biology, vol. 71, 102326 . https://doi.org/10.1016/j.pbi.2022.102326

The plant-associated microbiome is a key component of plant systems, contributing to their health, growth, and productivity. The application of machine learning (ML) in this field promises to help untangle the relationships involved. However, measure

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b4a2b3fb3f21688d23712600eade7cb5
https://pure.au.dk/portal/da/publications/compositionality-sparsity-spurious-heterogeneity-and-other-datadriven-challenges-for-machine-learning-algorithms-within-plant-microbiome-studies(f0ef4b47-5e69-4b58-b4d1-c7802136841b).html

Practical spectral photography II: snapshot spectral imaging using linear retarders and microgrid polarization cameras

Autor: Ali Altaqui, Michael Kudenov, Cranos Williams

Publikováno v: Optics Express. 30:12337

Despite recent advances, customized multispectral cameras can be challenging or costly to deploy in some use cases. Complexities span electronic synchronization, multi-camera calibration, parallax and spatial co-registration, and data acquisition fro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::007caf3ccc57e7e61c6d4bbabacd37f0
https://doi.org/10.1364/oe.453538