This research aims to investigate the integration of distributed learning paradigms with robotics, IoT, and edge computing in the context of digital agriculture. It will explore the potential benefits of using distributed learning approaches, such as federated learning, continual learning, and reinforcement learning to improve crop yields and reduce costs and environmental impact by enhancing the overall efficiency of agricultural operations, also including active sensing, autonomous navigation, and decision-making. The research will explore IoT and edge computing to support the collection, processing, and analysis of data from distributed sensors in the field. The study will investigate the technical and economic factors that influence the adoption and implementation of these technologies and frameworks in agriculture, including issues related to scalability, resource constraints, and interoperability. Finally, the research will also explore the potential of combining these technologies and frameworks to create new opportunities for innovation and collaboration in digital agriculture.