2026 ASEE Annual Conference & Exposition

Emerging technologies, such as artificial intelligence, the Internet of Things, and sensing systems, are transforming modern agriculture. Preparing students and professionals to apply these tools effectively requires experiential learning frameworks that integrate engineering design, data science, and practical agricultural applications. Prior research in engineering education highlights that authentic, problem-centered experiences enhance technical competency, collaboration skills, and readiness for professional practice. However, there remains a need for coordinated models that connect universities and industry to strengthen technology-driven agricultural training.
The Digital Agriculture and Plant Phenotyping Technologies (DAPPT) project addresses this need through a collaboration between Florida A&M University, the University of Nebraska-Lincoln, and Purdue University. The program offers structured research and training experiences that connect learners and professionals with digital agriculture systems and real-world problem-solving. Through a cohort-based model, participants engage in AI-assisted image analysis, sensor calibration, canopy and soil monitoring, and data visualization using field and laboratory systems. In addition to academic participants, industry partners receive hands-on training in cutting-edge sensing technologies and data analytics to enhance their capacity for implementing precision agriculture solutions.
Preliminary outcomes indicate improvement in participants’ technical literacy, confidence, and understanding of digital agriculture applications. Students report greater motivation to pursue research and professional careers in precision agriculture, while industry trainees gain practical experience with modern sensing platforms and decision-support tools. The multi-institutional and industry collaboration promotes applied learning, curriculum innovation, and workforce development for data-driven agricultural systems.
This project is supported by the U.S. National Science Foundation Experiential Learning for Emerging and Novel Technologies (ExLENT) Program (award no. 2322535).