The widespread adoption of precision agriculture faces many challenges, namely high levels of complexity, poor human-interaction systems, and lack of integration with existing systems. Fraunhofer Portugal AICOS (FhP-AICOS) has the competences needed to turn them into opportunities, closing critical knowledge gaps and contributing to higher adoption rates.


On February 15th, Fraunhofer-Gesellschaft and the FCT (Portuguese Foundation for Science and Technology) renewed their cooperation agreement and announced the creation of a new research centre focusing on the topics of precision agriculture and water management. FhP-AICOS is slated to contribute with its expertise in information technology and emerging communications to this new initiative. Precision agriculture is mainly the combination of agricultural knowledge with technology, however the interaction with humans should not be overlooked, which is why FhP-AICOS’ cooperation and competences on Information and Communication Technologies (ICT) will undoubtedly be an advantage.

The role of precision agriculture for soil management and crop protection is of utmost importance nowadays, as society is facing strong challenges related to climate change, growing world population and scarcity of resources. Despite the unprecedented growth of precision agriculture techniques in the last decade, there are key factors which have discouraged a widespread adoption.

Overall, precision agriculture aims to increase crop production, reduce losses and operational costs, minimize the use of pesticides and preserve valuable resources, such as correctly manage the use of water or keep a sustainable soil. Much of the technology necessary to achieve this is grounded in complex ICT, since it requires the ability to monitor, control and plan the crop production.

The answer lies in ICT composed of connected things, including sensing and actuation mechanisms, with network infrastructures or simple mobile crowdsensing scenarios for extraction of meaningful information as nutrient levels in soil, leaf and canopy moisture or weather and environmental conditions. Decisions once made on empirical level may potentially result in better outcomes if they are made based on larger amount and more quantitative information available instead, which is why the data collection should be fast and precise.

Through intelligent systems, this data can be construed to provide insights in farming operations, such as prediction of crop production or phytosanitary conditions, detection of emergent plagues, explaining its taxonomy to local farmers or the classification of crop maturity using non-destructive embedded vision of comparable performance to some laboratory tests.

Within this particular scientific area, FhP-AICOS has extensive work done on Information Processing Systems, namely systems equipped with context awareness, content retrieval and multimodal information fusion. These abilities – sensing their physical environment, easily searching and retrieving information, and combining and processing data from different sources – can greatly contribute to the effectiveness of Decision Support Systems (DSS).

However, as these tasks cannot be completely done by automated systems, one of the main current challenges is finding a way to interpret all the data collected while taking into account the contribution of the farmers and their agents, i.e., agricultural technicians that advise in the field, farmers’ associations, smallholder farmers, ICT operators, legal authorities or protection agencies. Despite the advances in robotics and sensing technologies, this remains a challenge, even in more controlled environments such as greenhouses or hydroponic farming.

Keeping in mind the strong need of farmers to collect instant information for flexible and quick decision-making, a common goal of research activities is to define DSS for farm management, which can have several layers of complexity and require higher levels of expertise in order to interact and operate it.

The answer might be embracing a human-centred design approach and apply it throughout the development of any system. Designing services around the needs of the rural user is critical to the success of precision agriculture. By fighting technological illiteracy with farmer education for ICT, with participatory design involving the end-users during the development lifecycle, such as employing performing usability and acceptance tests, FhP-AICOS can narrow the gap between the technology and the end-user.


Agriculture is among the priorities of the Deus Ex Machina Project


Deus Ex-Machina (DeM) – Symbiotic technology for societal efficiency gains – is a project led by FhP-AICOS with research lines that address the symbiotic relationship of humans with technology, dealing with challenges that stem from limited access and acceptance of technology, lack of integrated solutions and lack of replication without strong implementation efforts for assistive solutions. The project focuses on several domains, one of them being Agriculture.

’DeMBACCHUS – Boosting Agriculture with Companions for Connecting Healthy Plants, Users and Sensors” is a subproject of DeM focused in the area of precision agriculture, with the aim of prototyping a set of mobile companions co-designed by farmers and stakeholders.

In this project, FhP-AICOS is leveraging the work on hardware prototyping, computer vision and machine learning, with successful results in other business fields, thanks to algorithms dedicated to mobile or low-power data processing that enable scalability and broader coverage. By creating low cost technological blocks, which are easy to install and to operate, we are broadening the range of potential end-users – from low skill workers to high skill producers.

FhP-AICOS’ end goal is to contribute to the progress of the digitalization of agricultural processes and to provide intelligent systems, following recommendations of Portuguese research excellence in agriculture. Thanks to our solid expertise, and contributions from our partners, we can create viable and innovative solutions.