Project type: Bridge Project
Today, the manual visual inspection of grain is still one of the most important quality assurance procedures throughout the value chain of bringing cereals from the field to the table. In order to improve performance, robustness and consistency of this inspection, there is a need for automated imaging-based solutions to replace subjective manual inspection. In order to meet this need FOSS has developed a multispectral imaging system called EyeFoss™. With this system user independent multispectral images of +10.000 individual kernels can easily be collected within minutes real time on site. The EyeFoss™ applications currently cover wheat and barley grading.
To derive maximum value from the data there is a need to develop methods of training data algorithms to automatically be able to provide industry with the best possible feedback on the quality of incoming materials. The purpose is to develop a framework which replaces the current feature-based models with deep learning methods. By using these methods, the potential is significantly to reduce the labor needed to expand the application of EyeFoss™ into new applications; e.g. maize, coffee, while at the same time increase the performance of the algorithms in accurately and reliably describing the quality of cereals.
This project aims at developing and validating, with industrial partners, a method of using deep learning neural networks to monitor quality of seeds and grains using multispectral image data. The method has the potential of providing the grain industry with a disruptive new tool for ensuring quality and optimising the value of agricultural commodities. The ambition of the project is to end up with an operationally implemented deep learning framework for deploying EyeFoss™ to new applications in the industry. In order to the achieve this, the project will team up with DTU Compute as a strong competence centre on deep learning as well as a major player within the European grain industry (to be selected).
The project involves the research themes of AI (WS2) and CyPhys (WS6) of DIREC.
October 1, 2020 – September 31, 2024 – 3.5 years
Total budget DKK 3,91 million / DIREC investment DKK 1,90 million
University of Copenhagen
Department of Computer Science
Technical University of Denmark
DTU Compute
The aim of DIREC is to expand the capacity within research, innovation and education in digital technologies in Denmark. In addition, DIREC shall contribute to the competitiveness of Denmark through collaboration with Danish businesses and the public sector on developing new innovative products and services based on the newest digital technologies.
DIREC is partially funded by Innovation Fund Denmark.
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