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Related Experiment Video

Updated: Oct 26, 2025

Using Flight Mills to Measure Flight Propensity and Performance of Western Corn Rootworm, Diabrotica virgifera virgifera LeConte

Using Flight Mills to Measure Flight Propensity and Performance of Western Corn Rootworm, Diabrotica virgifera virgifera LeConte

Published on: October 29, 2019

Computer vision for detecting field-evolved lepidopteran resistance to Bt maize.

Seth J Dorman¹, Michael W Kudenov², Amanda J Lytle¹

¹Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC, USA.

Pest Management Science

|July 26, 2021

Summary

A new computer vision algorithm accurately measures insect damage to crops, improving detection of pest resistance to Bacillus thuringiensis (Bt) toxins. This technology enhances crop stewardship and reduces unnecessary insecticide use.

Keywords:

Bt crops Helicoverpa zea corn earworm engineering application resistance detection

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Last Updated: Oct 26, 2025

Using Flight Mills to Measure Flight Propensity and Performance of Western Corn Rootworm, Diabrotica virgifera virgifera LeConte

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Published on: October 29, 2019

Bioassays for Monitoring Insecticide Resistance

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Published on: December 30, 2010

Protocols for Robust Herbicide Resistance Testing in Different Weed Species

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Published on: July 2, 2015

Area of Science:

Agricultural Entomology
Biotechnology
Computer Vision

Background:

Lepidopteran pest resistance to Bacillus thuringiensis (Bt) toxins in maize and cotton is a global challenge.
Accurate monitoring of evolved resistance is crucial for effective Bt toxin stewardship and mitigation strategies.
Current methods rely on visual estimates of crop injury, which can be imprecise.

Purpose of the Study:

To evaluate an existing maize injury monitoring protocol for estimating Bt resistance in Helicoverpa zea.
To develop and validate a computer vision-based algorithm for more accurate measurement of insect injury.

Main Methods:

Evaluation of a visual maize injury monitoring protocol.
Development of a computer vision algorithm to quantify Helicoverpa zea injury.
Comparison of algorithm-based measurements with human observer estimates.

Main Results:

High interobserver variability was detected in current visual injury assessment methods.
The developed computer vision algorithm provided more accurate and precise measurements of insect injury compared to human observers.
Human observers tended to overpredict insect injury, potentially leading to unnecessary insecticide applications.

Conclusions:

Automated detection of lepidopteran resistance to Bt toxins is vital for genetically engineered crop stewardship.
Computerized screening offers standardized metrics, preserves digital data, and allows for increased sample sizes.
This technology improves confidence in resistance monitoring for researchers, regulators, and the agricultural biotechnology industry.