Ricerc@Sapienza

It is well known that agriculture plays a significant role both in the economic and nutrition sectors, while the challenges are the environmental sustainability, the safety of operations and the relief of heavy duties for the workers, especially in a world where the population is increasing tremendously together with the demand for food. Traditional methods, used by farmers, are not sufficient enough to fulfill these requirements. Indeed, the automation process in agriculture started early last century, but today the technology evolution in the field of ICT is driving a deep transformation thanks to the injection of intelligence through IoTs, Big Data analytics, robotics and Artificial Intelligence techniques. In fact, digitization of data collection processes in the crop fields will be at the heart of the next agricultural revolution. A stronger convergence between mechatronics and Artificial Intelligence is expected, mostly in the field of designing autonomous robots with both sensing and acting capabilities, such as ground sampling, fertilizing injection, seeding, etc. The present project proposes the design and development of a middleware software architecture equipped with several Artificial Intelligence algorithms capable of acquiring data related to the field, through suitable sensor devices, and processing them to improve a wide range of agriculture-related tasks, within the field of autonomous precision agriculture. The software architecture is in charge of processing low-level information coming from smart sensors on-board to an agri-robot (e.g. cameras, lidar, suitable soil sensors) solving some advanced recognition tasks through algorithms and techniques mediated by the computer vision fields such as state-of-art deep learning architectures. The main design principles adopted in this context are the control over models' computational complexity and the explainability of both models and autonomous decisions taken by the proposed system.