[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, S. Banerjee, A. Chanda, and G. Terejanu, “Geographical Prediction Framework of Aflatoxin with Quantified Uncertainties,” to be submitted to Scientific Reports, 2017.
    [BibTeX]
    @article{working_Kareem,
    author = {Kareem Abdelfatah and Jonathan Senn and Noemi Glaeser and Sourav Banerjee and Anindya Chanda and Gabriel Terejanu},
    title = {{Geographical Prediction Framework of Aflatoxin with Quantified Uncertainties}},
    journal = {{to be submitted to Scientific Reports}},
    year = {2017},
    }

  • K. Abdelfatah, J. Bao, and G. Terejanu, “Geospatial Uncertainty Modeling using Stacked Gaussian Processes,” under review Environmental Modelling & Software, 2017.
    [BibTeX] [Download PDF]
    @article{Abdelfatah_EMS,
    author = {Kareem Abdelfatah and Junshu Bao and Gabriel Terejanu},
    title = {{Geospatial Uncertainty Modeling using Stacked Gaussian Processes}},
    journal = {{under review Environmental Modelling \& Software}},
    url = {https://arxiv.org/abs/1612.02897},
    year = {2017},
    }

Team Members

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

Xiao Lin : PhD 2018
Xiao Lin
PhD 2018
Computer Science
and Engineering
Kareem Abdelfatah : PhD Student
Kareem Abdelfatah
PhD Student
Computer Science
and Engineering