AI-Driven Collaborative Robotics in Agriculture

摘要

Emerging trends in precision agriculture are the consequence of the convergence of AI with collaborative robotics, delivering smart and dynamic soil and crop management systems. This chapter covers the theoretical background and application of AI-driven robots in farm environments, with a specific focus on the analytical optimization of soil health and the assessment of crop yield. It emphasizes data-driven AI techniques, including machine learning, deep learning, and sensor data modeling, to simulate interventions by collaborative robots, such as soil spatial mapping, nutrient deficiency detection, predictive analytics for yield estimation, and decision-making support for farmers. The chapter serves as a prelude to forthcoming AI-robot applications and real-world use case scenarios. By bridging human expertise and AI, this chapter moves toward creating resilient, efficient, and sustainable agricultural ecosystems. Furthermore, this approach aligns with global sustainability goals by enhancing resource efficiency and empowering data-driven agricultural policymaking.