This paper examines the pedagogical impact of developing an autonomous mobile robot designed for precision agriculture in community greenhouses. Developed through an interdisciplinary collaboration between mechanical and software engineering students, the project follows a Software-First ap-proach, where mechanical design is dictated by AI-driven perception re-quirements. The system integrates a Raspberry Pi 5 with a Hailo-8 AI accel-erator to perform real-time YOLOv8-based detection of crop diseases at 20-30 FPS. By bridging the gap between a 2.5-credit IoT course and a capstone software project, the initiative provides a robust Project-Based Learning (PBL) framework. We discuss how challenges in Edge AI computing, cloud-based CAD collaboration (Onshape), and stakeholder engagement foster es-sential systems-engineering competencies and CDIO skills. The project demonstrates high educational value by connecting advanced engineering education with environmental sustainability and community-driven needs.