Abstract Sampling should be the first step in arthropod pest management, yet it often does not occur. When sampling does happen, it relies on outdated tools that are labor-intensive and imprecise. As a result, many management decisions are made without field-based information. In response, a new generation of sampling technologies has emerged, promising faster, easier, and more automated pest detection. These include camera-enabled smart traps, remote sensing platforms using unmanned aerial vehicles (UAVs), and predictive models based on degree-day accumulation, weather patterns, and regional monitoring networks. Despite their technical sophistication, many of these tools have a limited impact on improving pest management decisions. I argue that the primary limitation is not a lack of innovation in detection hardware, but insufficient development of the knowledge systems needed to interpret and apply the data. Using the previously named examples, I illustrate how gains in data quantity or resolution do not necessarily translate into actionable guidance for growers. In many cases, tools are deployed without validated thresholds, field-level context, or clear links to making profitable decisions. Meaningful advances in sampling will require coordinated development of physical tools and the interpretive frameworks that support their use, including validation with field data, explicit decision thresholds, and integration of ecological and landscape context. Without these complementary investments, new sampling technologies risk offering the appearance of precision while falling short of practical management impact.
Dominic Reisig (2026) studied this question.