Computer Vision
We develop solutions based on computer vision and deep learning for any industry. We accompany you throughout the process, designing a tailored solution.
Objective: Developing 4.0 tools to enhance vertical farming
The Vertical Farming Solutions project, “4.0 Technologies for the Vertical Food Factory,” aims to develop a set of Industry 4.0 tools and the necessary agronomic knowledge to promote the vertical production model for food factories.
This project has received public funding under the AEI 2022 call from the Ministry of Industry, Trade, and Tourism, as part of the aid established to support innovative business clusters in order to improve the competitiveness of small and medium-sized enterprises.
The participating partners in Vertical Farming include the Aragón Cluster of Agricultural and Livestock Production Media CAMPAG, Aragón Logistics Cluster ALIA, GROOTS, UNIZAR, ZALUX, TATOMA, and ATRIA.
ATRIA’s role has been to develop a computer vision algorithm to identify and control various parameters that affect the optimal development of plants, such as light, humidity, temperature, and nutrients. The goal of the algorithm is to improve the efficiency of vertical farming systems and increase agricultural production.
Solution: We developed a vision algorithm to detect diseases and other parameters
We developed a vision algorithm that, through continuous imaging, can detect plant diseases, nutritional deficiencies, and adverse humidity conditions.
Our algorithm uses 2D images of plants to analyze and determine the relationship between RGB values and the chlorophyll levels in the leaves. This allows us to assess whether the plants are in good condition for growth and production.
Additionally, the algorithm can also detect pests, foreign objects, and nutritional deficiencies. We identify pests and foreign objects through changes in leaf color, transitioning from green to yellow or brown.
As a result, we have developed an algorithm that can monitor key parameters for plant development, contributing to increased production in a more sustainable manner.
