Artificial Intelligence (AI)

In short

At NEUROPUBLIC, we leverage AI techniques to develop the company’s services, primarily in the agri-food sector through smart farming systems, as well as in other areas. The power of this technology lies in its very data: the more we use Artificial Intelligence (AI), the more we feed its models with data, improve them and achieve the results we need, continuously enhancing the services we offer to our customers with highly accurate outcomes.

NEUROPUBLIC’s key advantage in this emerging sector is that the platforms it provides to its customers are powered by diverse data sources. These sources help the algorithm run even faster, significantly reducing the likelihood of errors.

Highlights

gaiaTRAP

NEUROPUBLIC uses artificial intelligence techniques to develop the electronic olive fruit fly trap, an innovative olive fruit fly monitoring system. This trap automatically takes pictures of the insects and then utilizes computer vision models and neural networks to identify and count the olive fruit flies.

The use of these techniques allows for more accurate estimation of the olive fruit fly population, helping to make targeted decisions to protect olive crops, reducing the environmental impact of unnecessary pesticide use.

Fraud Detection System for OPEKEPE

As part of the new information system being developed for OPEKEPE (the Greek Payment Authority of Common Agricultural Policy Aid Schemes), NEUROPUBLIC leverages artificial intelligence techniques to detect fraud cases in producer declarations. The system employs data analysis methods and embeddings to compare applications and identify potential inconsistencies or patterns linked to fraudulent activity.

This ensures the reliability of the payment system and safeguards resources allocated to legitimate beneficiaries.

Machine Learning Models for Plant Diseases (gaiasense)

NEUROPUBLIC develops machine learning models for monitoring and predicting plant diseases. These models analyze data from telemetric stations, such as climatic conditions and other parameters, to calculate risk indices for specific diseases. With these predictions, farmers receive timely alerts and can take the necessary protective measures, ensuring both the quality and quantity of their production.