The global population is expected to reach 9 billion by 2050. Feeding this growing population more sustainably is a huge challenge facing agriculture. Developing agricultural robotics is seen as one potential solution to tackling this challenge. Advances in agri-food robotics reviews the utilisation of agricultural robots to deal with increasing labour shortages in agriculture whilst bringing greater precision and efficiency into farming operations. The book addresses recent advances in agricultural robotic technologies and how these can be optimised to monitor and manage crop production more effectively, from phenotyping for improved varieties to harvesting the finished product. In its comprehensive exploration of the technologies available, the book provides farmers with the means necessary to invest - and trust - in agricultural robotics to improve the productivity and profitability of their farm. With its distinguished editors and expert team of authors, Advances in agri-food robotics will be a standard reference for academic researchers in crop and livestock science, agricultural engineers, data scientists, as well as government and private sector agencies supporting sustainable agriculture and the development of agricultural technology.

This collection features four peer-reviewed reviews on Artificial Intelligence (AI) applications in agriculture. The first chapter reviews developments in the use of AI techniques to improve the functionality of decision support systems in agriculture. It reviews the use of techniques such as data mining, artificial neural networks, Bayesian networks, support vector machines and association rule mining. The second chapter examines how robotic and AI can be used to improve precision irrigation in vineyards. The chapter pays particular attention to robot-assisted precision irrigation delivery (RAPID), a novel system currently being developed and tested at the University of California in the United States. The third chapter reviews the current state of mechanized collection technology, such as the development of harvest-assist platforms, as well as the possibilities of these machines to incorporate artificial vision systems to perform an in-field pre-grading of the product. The final chapter explores the emergence of the automated assessment of plant diseases and traits through new sensor systems, AI and robotics. The chapter then considers the application of these digital technologies in plant breeding, focussing on smart farming and plant phenotyping.