Our forecasting models

Our previsional models are the beating heart of Vinai.Farm system, allowing us to give you the right indications to protect your vineyards.

What is a forecasting model?

 

Predictive models are mathematical models, which when certain climatic conditions occur, reveal whether a phytopathology is being developed.
In this way, the winegrower can intervene in a timely manner to stop the birth or recurrence of the diseases that are the subject of prediction.
Vinai.Farm system is a Decision Support System (DSS), a tool that helps you make the best decisions for your crop at the best possible time so that you can better plan your business, reduce waste and optimize your resources.

Downy mildew model

The system provides alerts to the probability of the disease occurring in the vineyard.

In addition to an optimization in the programming of treatments, depending on the number of nodes installed, the application will allow evaluating any areas to be excluded from the treatment programmed or suggested by the application of Vinai.Farm.

Oidium model

The system provides alerts to the likelihood of the disease occurring in the vineyard.

The application will provide a percentage relative to the risk of oidium occurring as a result of meteorological conditions favourable to the onset of the disease. Intervening in time, without abusing the treatments, will no longer be a problem.

 

botrite o muffa grigia

Botrytis cinerea model

The system provides alerts to the likelihood of the disease occurring in the vineyard.

tignoletta uva

Lobesia botrana model

The system provides alerts related to the flight phase of the various generations of the lepidopteran.

This system can be combined with pheromone traps.

Our Artificial Intelligence based algorithm

Our artificial intelligence algorithm allows us to perfect the prediction of the onset of diseases such as Downy Mildew and Powdery Mildew. As soon as the data collection stations are installed, they begin to process the collected data and provide information on the risk of contraction of these pathogens.

Thanks also to the indications that will periodically be provided by the winegrower through the app, the algorithm will be perfected and will be able to provide more and more precise indications with the passage of time.

We collaborate with a team of researchers who study new application methods for artificial intelligence. We believe in this technology and its greater effectiveness compared to traditional agronomic models for the prediction of phytopathologies.

 

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