Duration: 01/2016 – 12/2018
PI: Gabriel Terejanu
Co-PIs: Sourav Banerjee, Anindya Chanda, Robin Kloot
The goal of this proposal is to deploy a sensor network to collect environmental data from a cornfield in collaboration with a local farmer. The data will be used to develop and demonstrate a predictive framework for calculating aflatoxin occurrence in South Carolina cornfields prior to harvest.
Aflatoxin is a carcinogenic toxin naturally produced by Aspergillus family of fungi (flavus, parasiticus) occurring in soil and decaying vegetation, which can contaminate corn along with other relevant South Carolina crops like peanuts and cotton before harvest and/or during storage. It is well know that aflatoxin exposure causes one of the deadliest cancers worldwide, namely liver cancer in humans and a variety of animal species. Prediction and control of aflatoxin contaminations before harvest are a fundamental challenge for US grain industry, poultry producers, and makers of dairy products. The predictive model will be used to continuously monitor the aflatoxin incidence in cornfields and provide the farmers with actionable information regarding the best time to harvest and efficient isolation of contaminated areas, as well as testing the role of cover crops and irrigation in plant stress regulation and aflatoxin contamination.