Periodic Reporting for period 1 - Neural Grader (Neural Grader - Digitizing the Wood Industry)
Periodo di rendicontazione: 2019-02-01 al 2019-07-31
user companies with a specialized digital platform for buying products. We have observed how low digitization of
the wood industry poses problems for success & often survival for SMEs. There is an urgent need of an automated
solution for processing lumber. The current methods are too expensive for SMEs and in practice lead to errors,
enormous economic & environmental waste.
Our newest product - Neural Grader is the solution to this problem, having the potential of disrupting the European
and global wood industry. Its innovation lies in the industrial cameras that capture images of each board and, using
state-of-the-art Artificial Intelligence methods, accurately classifies wood quality. Neural Grader also computes the
optimal way for cutting the boards and sends commands to the cutting machine, preventing wood waste.
- Efficiency: it detects defects and grads lumber at high speed, with accuracy that exceeds the one of existing systems;
- Affordability: the price will be 1/4 of the price of our competitors’ products, making it accessible for the SMEs, who
are currently overwhelmed by overly expensive technology and dominated by large companies;
- Full-control of production: companies can manage the production lines and inventory and focus on quality.
We have a large base of existing clients and 10 sawmills in Europe & North America sent us Letters of Support.
Environmental benefits: Studies show that such an automated solution leads to improvements in wood waste reduction
of over 20%. The wood industry plays a key part in climate change and lowering the carbon footprint.
Social benefits: Currently, the wood industry in Europe is more expensive compared to the one in Asian countries, where
labor is significantly cheaper. Neural Grader could tip the scale in favor of the European industry.
Using the technical specifications, we have built a new prototype development plan, adjusting the hardware and software requirements accordingly. Also, we started building a data-gathering hardware system in order to launch the training of new specialized AI algorithms.
The IPR study has resulted in a thorough analysis of existing patents in the industry and has offered us insight regarding the next steps that need to be followed for successfully protecting our research and development work.
From a R&D point of view, our main goal by the end of the project is to have a functioning test-environment prototype for the market segment we have targeted. Early tests show promising results for the 2 quality classes, having already surpassed human accuracy. We are confident that the 90% accuracy threshold can be reached with ease.