Projects

Harnessing Robotics, XR/AR, and 5G for a New Era of Safe, Sustainable, and Smart Agriculture

(AgRibot) Horizon Europe (2024-2028) 

https://agribot-project.eu/

AgRibot is a groundbreaking project aimed at advancing robotics as well as augmented and extended reality (AR/ XR) technologies within the agricultural sector. The project’s overarching goal is to achieve a range of critical impacts including: improving working conditions, reducing environmental footprint, addressing workforce shortages, and enhancing overall sustainability and competitiveness. In alignment with strategic initiatives such as the Farm-to-Fork Strategy, the Common Agricultural Policy post-2022, and the broader ambition of embracing the Digital Age, AgRibot is poised to drive a transformative shift toward a fair, safer, and resilient agriculture sector.

Sustainable physical management system and automated detection of juvenile aphrophoridae vectors of Xylella fastidiosa

PRIN 2022 (2023-2026) 

The project is aimed at developing a sustainable control system for vector insects to counter the spread of Xylella fastidiosa. It consists of an innovative mechanical aeraulic device capable of generating an air flow of suitable shape, fast enough and with sufficient mass to cause damage to target organisms and possibly kill them. The machine will be positioned on board an agricultural robotic platform for automated deployment in the field. A system to automatically identify the foam produced by the vector insects in juvenile stages will be developed based on artificial vision technologies using a camera positioned on board the robot. The objective is to detect the foam before the treatment to guide the action of the aeraulic machine, as well as after the treatment to evaluate its effectiveness.

Project funded by the European Union-NextGenerationEU under the research program “PNRR MUR Missione 4, Componente 2, Investimento 1.1 NextGenerationEU – PRIN 2022” (Grant N. 20227F7J5W).

E-CROPS - Tecnologie per l'agricoltura Digitale Sostenibile

PON 2014-2020 (2020-2023) 

https://www.e-crops.it/

E-crops aims at developing and integrating new technologies with the ambition of promoting the transition of precision agriculture to Agriculture 4.0 in close connection with Industry 4.0. E-crops intends to help the development and diffusion of Agriculture 4.0 in Southern Italy: i) developing innovative technologies and methodologies to manage crops and the risks to which they are exposed; ii) inserting the new technologies in the supply chain, through a series of pilot applications able to monitor and manage the processes according to company objectives. Decision support tools will be developed, through the close interaction between business needs and monitoring and analysis methodologies, which allow to manage the spatial variability of the field both to increase the quality of the final product (on high value-added supply chains), and to management optimization in terms of sustainability (on industrial supply chains).

Agricultural Interoperability and Analysis System

(ATLAS), HORIZON 2020 (2019-2023) 

https://www.atlas-h2020.eu/

The goal of ATLAS is to achieve a new level of interoperability of agricultural machines, sensors and data services. ATLAS enables farmers to have full control over their data: farmers decide which data is shared with whom in which place. ATLAS will build an open, distributed and extensible data platform based on a service-oriented architecture which offers a high level of scalability from a single farm to a global community. The technology developed in ATLAS will be tested and evaluated within pilot studies on a multitude of real agricultural operations across Europe along several use cases, e.g.: precision agriculture tasks, sensor-driven irrigation management, data-based soil management and behavioural analysis of livestock. ATLAS will involve all actors along the food chain, simplifying and improving the processes from farm to fork. Through the support of innovative start-ups, SMEs and farmers, ATLAS will enable new business models for and with the farmers and establish sustainable business ecosystems based on innovative data-driven services.

Multimodal sensing for individual plANT phenOtyping iN agrIculture robOtics

(ANTONIO), ERA-NET ICT-AGRIFOOD (2020-2023) 

The project ANTONIO is aimed at developing a unifying framework to combine different sensor modalities, methods for creating accurate maps to facilitate operations on a narrow scale with a smaller environment footprint, artificial intelligence algorithms for data processing and decision support, and applications to make relevant information easily visible to the farmer. 

Simultaneous Safety and Surveying for Collaborative Agricultural Vehicles 

(S3-CAV), ERA-NET ICT-AGRI2 (2016-2018)

Precision farming relies on the ability to accurately locate the crops or leaves with problems and to accurately apply a local remedy without wasting resources or contaminating the environment. This project develops a unifying framework allowing incorporation of many different types of sensor data, methods for creating 3D maps and maximising map accuracy to facilitate operations on a narrow scale with a smaller environment footprint, methods for combining this data to make relevant information easily visible to the farmer, and methods for incorporating real-time sensor data into historical data both to increase precision during applications and to provide fast automated safety responses.

Ambient Awareness for Autonomous Agricultural Vehicles 

(QUAD-AV) , ERA-NET ICT-AGRI (2011-2013)

Autonomous vehicles are being increasingly adopted in agriculture to improve productivity and efficiency. For an autonomous agricultural vehicle to operate safely, environment perception and interpretation capabilities are fundamental requirements. The present project will focus on the development of sensors and sensor processing methods to provide an autonomous agricultural vehicle with such ambient awareness. The “obstacle detection” problem will be specifically addressed.

The obstacles that might be encountered in the field can be separated into four overall categories that should be detected and handled in different ways: positive obstacles, negative obstacles, moving people/animals/obstacles, and difficult terrain. Further, obstacles may vary greatly from situation to situation, depending on type of crop, fruit, vegetable or plant grown, curvature of landscape as well as other factors. Owing to the variety of situations and problems that may be encountered, no sensor exists that can guarantee reliable results in every case. Any candidate sensor has its strengths and drawbacks. Therefore, a complementary sensor suite should be used to gain the best performance.

The idea of this project is that of using different sensor modalities and multi-algorithm approaches to detect the various kinds of obstacles and to build an obstacle database that can be used for vehicle control. For instance, bearing and distance to the nearest collision can be estimated and used by the path planner to change route or to lower the speed if an obstacle is in close proximity to the vehicle’s planned path.