Lien Smeesters, B-PHOT Brussels Photonics Team is Moderator of the ACTPHAST session

Lien Smeesters

Lien Smeesters from B-PHOT Brussels Photonics Team, Vrije Universiteit Brussels is Moderator of the ACTPHAST session at the Smart Farming Conference 2021.

Lien Smeesters is a post-doctoral researcher within the B-PHOT Brussels Photonics Team at the Vrije Universiteit Brussel (VUB). She focuses on multidisciplinary and applied-oriented research in the field of optical spectroscopy and optical design.

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Earth Observation data benefits in the Agricultural sector

Francesca Piatto

by Francesca Piatto, EARSC

Earth Observation data and information play an essential role in supporting agriculture industry, farmers, technology suppliers, as well as governmental bodies at local, regional, and national level, and giving policymakers access to the best information possible to make informed decisions.

To this end, EARSC, the European Association of Remote Sensing companies, a participating organization of the Group of Earth Observation (GEO), promotes the use of Earth Observation technology and represents the European providers of geo-information services, creating a network between industry, decision makers and users.

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User-centric approach to facilitate a widespread uptake of satellite-based solutions for agri-food sector

Annalisa Donati

by Annalisa Donati, Eurisy

The European agricultural sector is one of the world leading producers of food, guarantor of food security and quality and provider of millions of jobs. Yet it faces many challenges from environmental pressures, mostly related to climate change and loss of biodiversity, to the need for sustainable and efficient management of resources such as water, soil and energy.

Data-based technologies, including satellite positioning systems like GPS, remote sensing, and the Internet have the potential to increase farm efficiency and improve production, and also to contribute to making farming systems more sustainable from an economic, social and environmental point of view.

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Next-level integrated spectral sensors for agrifood applications

Maurangelo Petruzzella

by Maurangelo Petruzzella, Senior Researcher, Eindhoven University of Technology

We will discuss the recent development of integrated spectral sensors capable of identifying and quantifying key properties of fruits, plants, and organic materials along the full food production chain: on the field by farmers, in storage areas, within the distribution process.

This innovative technology is based on arrays of detectors sensitive in the near-infrared, having dimensions similar to current smartphone cameras. They will offer a fast, low-cost, contactless, and non-invasive method to retrieve the chemical composition of materials without the need for sample preparation.

program: https://smartfarmingconference.com/program/

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e-shape – EU CAP Support Pilot: A machine learning enabled system for dynamic phenology estimation and yield prediction using satellite and in-situ observations

Vassilis Sitokonstantinou

by Vassilis Sitokonstantinou, National Observatory of Athens / Beyond Center of Earth Observation Research and Satellite Remote Sensing

Under the framework of e-shape H2020 EU project and the S1P2 EU CAP Support pilot, a number of novel services are developed as an integrated smart farming system; targeting farmers, farmers’ associations, paying agencies of the Common Agricultural Policy (CAP), but also the agri-insurance sector. The services comprise of the near-real time identification of the onset of key phenological stages and the timely prediction of yield.

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Developing tailored services for the agro-insurance sector over northern Greece: The case of Rodopi / cotton crop

Nikolaos S. Bartsotas

by Nikolaos Bartsotas, National Observatory of Athens / Beyond Center of Earth Observation Research and Satellite Remote Sensing

Under the framework of e-shape H2020 EU project and S6P4 ResAgri pilot, a number of novel services are developed as an integrated system for the insurance company sector as well as farmers’ associations. They combine a 40-year long record of meteorological reanalyses (1980-present) to build an adverse selection tool based upon the historical risk from individual weather perils.

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