Undergraduate preparation in precision agriculture often struggles to move beyond concepts because students rarely work with sensors, equipment, data and field decision workflows. To directly address this gap, a three-day, field-based Precision Agronomy Teaching Hub (PATH) workshop was implemented at a North Agronomy Farm-KSU (university research farm) to build practical precision agronomy skills and confidence in data-driven decision making. The program engaged 22 undergraduate students, supported by 8 educators, 5 industry partners, 8 graduate student volunteers, and 2 producers. Instruction followed a deliberate learning sequence, beginning with spatial variability, management zones, and agronomic data layers, and progressing into hands-on field rotations. Students worked directly with soil electrical conductivity mapping systems, canopy sensing tools, variable-rate technologies, cloud-based farm management platforms, and unoccupied aerial systems for crop monitoring. Industry specialists and producers led applied demonstrations and panel discussions to connect technology performance with real farm adoption challenges. Program impact was evaluated using a pre and post survey focused on technical understanding, confidence, engagement, and interest in precision agriculture. All major learning indicators improved after the workshop. The largest gain was observed in sensor technology understanding, with average scores increasing by more than one full point on a five-point Likert scale, shifting from neutral to strong agreement in students’ ability to interpret and use sensor data. Confidence in collecting and analyzing field data also increased substantially. Hands-on activities, particularly drone operations, sensor stations, and industry-led demonstrations, received the highest effectiveness ratings, with overall satisfaction and instructional support averaging between 1.2 and 1.4 (1 = strongly agree). Nearly all participants reported increased interest in precision agriculture and clear awareness of digital agriculture career pathways. Therefore, a structured, field-centered instructional model can produce measurable gains in digital agronomy competency and workforce readiness.