Automated detection of field-evolved lepidopteran resistance to Bt-maize using computer vision

Resistance evolution of lepidopteran pests to Bacillus thuringiensis (Bt) toxins expressed in maize and cotton is a significant issue worldwide. Effective toxin stewardship requires reliable detection of field-evolved resistance to enable the implementation of mitigation strategies. Currently, visual estimates of maize injury are used to document changing susceptibility. In this study, we evaluated an existing maize injury monitoring protocol used to estimate Bt resistance levels in Helicoverpa zea (Lepidoptera: Noctuidae). We detected high inter-observer variability across multiple injury metrics, suggesting that the precision and accuracy of maize injury detection could be improved. To do this, we developed a computer vision-based algorithm to measure H. zea injury. Algorithm estimates were more accurate and precise than a sample of human observers. Moreover, observer estimates tended to overpredict H. zea injury, which may increase the false positive rate, leading to prophylactic insecticide applications and unnecessary regulatory actions. Automated detection and tracking of lepidopteran resistance evolution to Bt toxins are critical for genetically engineered crop stewardship to prevent the use of additional insecticides to combat resistant pests. Advantages of this computerized screening are 1) standardized Bt injury metrics in space and time, 2) preservation of digital data for cross-referencing when thresholds are reached, and 3) the ability to increase sample sizes significantly. This technological solution represents a significant step toward improving confidence in resistance monitoring efforts among researchers, regulators, and the agricultural biotechnology industry.