Optical spectroscopy and machine learning enhancing food sorting

Anna Pakuła

a presentation by Anna Pakuła, Warsaw University of Technology, at the 2021 edition of the Smart Farming Conference

Anna Pakuła received the MSc. Eng. degree at Warsaw University of Technology (Poland) in 2004 in Marketing and Management and in 2006 in Photonics Engineering. In 2011 she finished the postgraduate course on Intellectual Property Management (University of Warsaw, Poland) and in 2016 she received PhD at Warsaw University of Technology. The main aim of her doctoral dissertation was to apply novel illumination techniques in optical profilometry.

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Water quality monitoring using RGB cameras

indy magnus

Indy Magnus: In this presentation, we will explain how a ‘Water-Quality Early Alert System’, enabling the remote, continuous and real-time monitoring of water using RGB cameras is being developed. Generally, the idea is to correlate the RGB camera images to a variety of physicochemical parameters and biological parameters without direct contact or the need of water samples. By doing a spectroscopic study of water with different parameters, using both a spectroradiometer and hyperspectral camera, the RGB camera outlook (filters, lenses) can be optimized. Machine learning techniques are employed to process the high-dimensional data and push the sensing performance even further.

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Deep Tech and Water quality Early Alert

Pablo Perez

Pablo Perez: Deep Tech: A game changing solution aiming the intersection of three disciplines: software combined with hardware and spectroscopy. A Water-quality Early Alert System (WEAS) based on water pictures analysis to remotely (in-situ) and real time evaluate water parameters (Color, Turbidity, pH, Conductivity, DO, COD, BOD, SS, Amonia) at any sampling point using water images, in a easy to use and cost effective way.

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