Title

Smartgrain: Using Digital Analysis to Quantify Fusarium Damaged Kernels

School Name

South Carolina Governor's School for Science & Mathematics

Grade Level

12th Grade

Presentation Topic

Microbiology

Presentation Type

Mentored

Abstract

Every year farmers lose millions of dollars in revenue from diseased crops. In wheat, these losses are caused by Fusarium Head Blight (FHB) infections from the pathogen Fusarium graminearum. Infected kernels are called Fusarium Damaged Kernels (FDK). In its worst years, FHB yield losses exceed 45% crop loss because of the vomitoxin deoxynivalenol. Identifying FDK is traditionally accomplished inefficiently by hand. The goal of this experiment is to use a digital analysis software called SmartGrain to measure seven kernel parameters to correctly quantify FDK percentage in a sample. Hand-separation of FDK kernels was used to create 21 seed standards with increasing concentrations of FDK. These standards served as a comparison against samples of unknown FDK rates from samples of Uniform Southern soft red winter wheat (USW) and Uniform Southern winter wheat Scab Nursery (USSN) wheat varieties. Following visual comparison, the 21 standards, 68 USW samples, and 81 USSN samples were imaged and analyzed using SmartGrain. The standards' data were used to create an equation that could predict FDK based on the correlation of each measurement to its FDK value. This standards equation had an r2 value of 0.987. When the formula was applied to the USW and USSN kernels, both samples produced a r-squared value of <0.01. Through comparison of SmartGrain predictions to samples identified in the traditional methods, we observed that SmartGrain was not an effective tool for measuring FDK. Additional tools will be used to increase time efficiency sorting grains and increase the accuracy of FDK identification.

Location

Furman Hall 126

Start Date

3-28-2020 11:45 AM

Presentation Format

Oral Only

Group Project

No

COinS
 
Mar 28th, 11:45 AM

Smartgrain: Using Digital Analysis to Quantify Fusarium Damaged Kernels

Furman Hall 126

Every year farmers lose millions of dollars in revenue from diseased crops. In wheat, these losses are caused by Fusarium Head Blight (FHB) infections from the pathogen Fusarium graminearum. Infected kernels are called Fusarium Damaged Kernels (FDK). In its worst years, FHB yield losses exceed 45% crop loss because of the vomitoxin deoxynivalenol. Identifying FDK is traditionally accomplished inefficiently by hand. The goal of this experiment is to use a digital analysis software called SmartGrain to measure seven kernel parameters to correctly quantify FDK percentage in a sample. Hand-separation of FDK kernels was used to create 21 seed standards with increasing concentrations of FDK. These standards served as a comparison against samples of unknown FDK rates from samples of Uniform Southern soft red winter wheat (USW) and Uniform Southern winter wheat Scab Nursery (USSN) wheat varieties. Following visual comparison, the 21 standards, 68 USW samples, and 81 USSN samples were imaged and analyzed using SmartGrain. The standards' data were used to create an equation that could predict FDK based on the correlation of each measurement to its FDK value. This standards equation had an r2 value of 0.987. When the formula was applied to the USW and USSN kernels, both samples produced a r-squared value of <0.01. Through comparison of SmartGrain predictions to samples identified in the traditional methods, we observed that SmartGrain was not an effective tool for measuring FDK. Additional tools will be used to increase time efficiency sorting grains and increase the accuracy of FDK identification.