Researchers at Texas A&M AgriLife have developed an artificial intelligence system capable of predicting destructive crop pest outbreaks with high accuracy, offering farmers a crucial early warning before damage occurs. Published in Ecological Informatics, the study demonstrates that machine learning models outperform traditional forecasting methods for managing western flower thrips, a significant pest that damages vegetable and commodity crops while spreading viruses. Led by Kiran Gadhave, an entomologist at the Texas A&M AgriLife High Plains Research and Extension Center in Canyon, the team integrated data from nearly 1,700 yellow sticky traps deployed in open fields and high tunnel systems for tomatoes and peppers. These traps were sampled weekly, generating millions of data points that were combined with up to 16 environmental variables. These variables included temperature, humidity, wind speed and direction, rainfall, and the size of the pest population recorded 14 days prior. Traditional pest forecasting often relies on limited parameters such as temperature and humidity, which frequently fail to accurately assess threat potential. In contrast, the AI models analyzed complex patterns across multiple biological and environmental factors simultaneously. The results showed that the models predicted thrips populations with nearly 88% accuracy in open fields and about 85% accuracy in high tunnels. Gadhave emphasized the transformative potential of detecting risks even a week earlier. This capability shifts pest management strategies from reacting to visible damage to proactively preventing outbreaks. The study identified two critical drivers for these predictions: the size of the parent population from two weeks prior, which significantly increased the risk of severe outbreaks, and temperature, followed by wind and humidity which influence population spread. A notable finding was the sharp drop in accuracy when models attempted to apply parameters across both open field and high tunnel systems at the same location. This highlighted the importance of microclimates, showing that even neighboring fields with slightly different conditions function as distinct ecosystems affecting pest dynamics. The research underscores that accurate localized forecasting requires tailored models for specific environments rather than generalized data. Postdoctoral researcher Arinder Arora and plant pathologist Nolan Anderson also contributed to the study, reinforcing the interdisciplinary nature of the work. The success of this AI application suggests a future where similar tools can be adapted for various crops, pests, and regional microclimates. Gadhave stated that this technology proves AI-enabled agricultural tools are not futuristic concepts but are already available to improve productivity. By leveraging these advanced forecasting methods, producers can better monitor and protect their crops, reducing yield losses and optimizing resource use. The research positions Texas A&M AgriLife as a leader in developing and deploying these AI solutions to benefit the agricultural sector directly in the field.