This chapter presents a survey of the advances in using machine learning (ML) algorithms for agricultural robotics. The development of ML algorithms in the last decade has been astounding, and there has therefore been a rapid increase in the widespread deployment of ML algorithms in many domains, such as agricultural robotics. However, there are also major challenges to be overcome in ML for agri-robotics, due to the unavoidable complexity and variability of the operating environments and the difficulties in accessing the required quantities of relevant training data. This chapter presents an overview of the usage of ML for agri-robotics and discusses the use of ML for data analysis and decision-making for perception and navigation. It outlines the main trends of the last decade in employed algorithms and available data. We then discuss the challenges the field is facing and ways to overcome these challenges.

• 1 Introduction
• 2 Applications of machine learning in agri-robotics
• 3 Challenges
• 4 Integration and field-testing use-cases
• 5 Conclusion
• 6 Where to look for further information
• 7 References