Monitoring and detection of sweet corn pests using smart trap technology

Fall armyworm (Spodoptera frugiperda) and corn earworm (Helicoverpa zea) pose significant economic threats to sweet corn and many other crops. Even though traditional monitoring methods such as hartstack and bucket traps are widely adapted and are effective, they are labor-intensive and result in delayed reporting of pest activity data which is critical for timely management decisions. This research will evaluate the efficacy of automated bucket traps that use photodiode sensors and infrared night-vision cameras to detect and record moth captures in real-time. Our study will compare three trap designs across multiple field sites: custom-designed automated bucket traps, commercial CropVue automated delta traps, and CropVue bucket traps. Our designed traps will transmit moth count data through Arduino microcontrollers via cellular networks to a centralized database, providing real time monitoring. Additionally, we will conduct a factorial experimental design examining variables including field position, orientation, and height of the traps to determine optimal deployment parameters for maximizing moth capture accuracy. We hypothesize that the automated traps could achieve improved detection rates compared to traditional trapping methods thereby reducing labor requirements and reporting delays. This technology will allow for precise tracking of moth movement patterns with timestamp data, enabling earlier detection of seasonal emergence, more accurate identification of action thresholds, and better-informed insecticide application timing. The implementation of automated traps across regional monitoring networks has potential to transform integrated pest management practices by supporting more targeted interventions, reducing unnecessary pesticide applications, and providing robust datasets for developing predictive models of pest population dynamics.