[USDA-NIFA] TOXIMAP: Computational Framework for Prediction of Geographical and Temporal Incidence of Mycotoxins in US Crop Fields

Amount: $500,000
Duration: 02/2017 – 02/2020
PI: Gabriel Terejanu
Co-PIs: Sourav Banerjee, Anindya Chanda

Abstract

The goal of this proposal is to develop a general predictive modeling framework for calculating
mycotoxin incidence in US crop fields. Prediction and control of the most potent carcinogenic
mycotoxin, aflatoxin, is a fundamental challenge for US grain industry, poultry producers, and
makers of dairy products.

The production of aflatoxin is highly dependent on environmental conditions such as water activity
and temperature and the colonization of crops by fungi has been attributed to plant stress due to
drought conditions. It is projected that environmental perturbations due to climate change will
result in a significant increase in aflatoxin contamination incidents, further aggravating its health
and economic impact.

The current lack of a systematic approach to determine the distribution of aflatoxin occurrence
before harvest adversely affects the grain industry. The proposed platform provides real-time
information on aflatoxin accumulation before harvesting and has the potential to change certain
behaviors in crop management to reduce the aflatoxin hazard. This includes determining the best
time to harvest and efficient isolation of contaminated areas.

Papers

  • K. Abdelfatah, J. Senn, N. Glaeser, and G. Terejanu, “Prediction and Measurement Update of Fungal Toxin Geospatial Uncertainty using a Stacked Gaussian Process,” Agricultural Systems, vol. 176, p. 102662, 2019. doi:https://doi.org/10.1016/j.agsy.2019.102662
    [BibTeX]
    @article{AbdelfatahJ_AS,
    title = {{Prediction and Measurement Update of Fungal Toxin Geospatial Uncertainty using a Stacked Gaussian Process}},
    journal = {{Agricultural Systems}},
    volume = "176",
    pages = "102662",
    year = "2019",
    author = "Kareem Abdelfatah and Jonathan Senn and Noemi Glaeser and Gabriel Terejanu",
    doi = "https://doi.org/10.1016/j.agsy.2019.102662",
    }

  • X. Lin and G. Terejanu, “EnLLVM: Ensemble based Nonlinear Bayesian Filtering using Linear Latent Variable Models,” in International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brighton, UK, 2019.
    [BibTeX]
    @CONFERENCE{LinP_ICASSP_2019,
    author = {Xiao Lin and Gabriel Terejanu},
    title = {{EnLLVM: Ensemble based Nonlinear Bayesian Filtering using Linear Latent Variable Models}},
    booktitle = {{International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brighton, UK}},
    year = {2019},
    month= {May},
    }

  • K. Abdelfatah, J. Bao, and G. Terejanu, “Geospatial uncertainty modeling using Stacked Gaussian Processes,” Environmental Modelling & Software, vol. 109, pp. 293-305, 2018. doi:https://doi.org/10.1016/j.envsoft.2018.08.022
    [BibTeX] [Download PDF]
    @article{AbdelfatahJ_EMS_2018,
    title = {{Geospatial uncertainty modeling using Stacked Gaussian Processes}},
    journal = {{Environmental Modelling \& Software}},
    volume = "109",
    pages = "293 - 305",
    year = "2018",
    issn = "1364-8152",
    doi = "https://doi.org/10.1016/j.envsoft.2018.08.022",
    url = "http://www.sciencedirect.com/science/article/pii/S1364815218304997",
    author = "Kareem Abdelfatah and Junshu Bao and Gabriel Terejanu",
    }

Team Members

Gabriel Terejanu : Associate Professor
Gabriel Terejanu
Associate Professor
Computer Science
UNCC
Sourav Banerjee : Associate Professor
Sourav Banerjee
Associate Professor
Mechanical
Engineering
UofSC
Anindya Chanda : Assistant Professor
Anindya Chanda
Assistant Professor
Environmental
Health Sciences
UofSC

Xiao Lin : PhD 2018
Xiao Lin
PhD 2018
Computer Science
and Engineering
UofSC
Kareem Abdelfatah : PhD 2019
Kareem Abdelfatah
PhD 2019
Computer Science
and Engineering
UofSC