Predictions with 95% reliability

Do you know what your next harvest will be like?

Plan the harvest to obtain the best quality and reduce your costs with the support of artificial intelligence. We validate that the models work and ensure their reliability.

Free, no obligation and 2-month money-back guarantee

Features

Harvest prediction model validated in 32,606 fields

Visualize the kg of harvest and plan your marketing

Know in advance the maturation of your harvest

Count of capacity, pests, diseases, control points and much more

Centralize all the information and have a 360º business vision

Volume prediction that drives your sales

Accurately know the harvest volume to better plan the sale. You will be able to know when to send the production and get the best possible price without collapsing the market.

Plan your production and marketing

Get the best selling price

Adjust your staffing and reduce costs

Save by buying the right materials and at the best price

Control Point

Add Your Tooltip Text Here

Ripening prediction 15 days in advance

Lose the fear of “what if”. Know in advance the perfect ripening date for your collection. Degree, pH and Acidity.

Know the best harvest time

Avoid losses and cost overruns

Reduce uncertainty

Reduce personnel costs, production, time and stress of the technical team

Save costs for travel and personnel in maturation controls while improving the quality of your production

Decisions based on reliable and profitable data

When did I come to this field for the last time? Be assured of the applications that all your producers have carried out and that they have used the correct products. Check a specific moment by selecting a date in the history.

Control point

How does it work

We ensure the reliability of the model in your predictions

01.

We recover agrodata

We use historical in any format and we add 447 new factors of stations and satellites

02.

We validate the model

We apply artificial intelligence to show you the reliability of the prediction model

03.

We implement

We give you access to the platform so you can view data and create users

Success stories

Discover customer stories that grow with RawData

Didac Masip
Didac Masip
Technical Director at Cerima Cherries
Read More
“There is always someone on the other side. They never leave you without communication, they attend to anything you raise and allow you to adapt and use the application as you see fit”.
Josep Maria Galimany
Josep Maria Galimany
Technical Director at Covides
Read More
"The tool allows us to ensure that the 20 million kg enter in an orderly manner and as we want."
Teresa Domínguez
Teresa Domínguez
HR Manager at Limones Mónica
Read More
"RawData is the best option to make our human resources more efficient."
Pedro Rodríguez
Pedro Rodríguez
Technical Director at Vitels
Read More
“RawData makes your job easier and gives you confidence for the customer. We have saved the harvest as well as increased profits”.
Ramón Jordán
Ramón Jordán
Technical Director at La Granja de Jordán
Read More
“I saw six more applications. I chose RawData because it is the easiest to use”.
Ramon Llanes
Ramon LlanesManaging Director Agrofarming
Read More
"After more than 10 years using agricultural software, RawData is the most flexible, agile and comprehensive solution we have tested."

Covides already knows what its harvest will be like: 8M bottles of wine in 2022

Share

Covides in figures

1st

1st grade wine cooperative of Catalonia

2.000

ha of vineyard

650

winegrowers

2O M

kg in 2021

Covides, a leading wine cooperative, manages the harvest of more than 20M kg and more than 600 producers thanks to precise volume and maturation predictions, tools such as satellite images and intelligent planning

Obtaining more accurate x3 ripening predictions

When we join vineyards with different quality characteristics, it happens that the singularities and quality of the wine will not correspond to what was planned. Obviously this is not desirable.

Therefore, if we carry out better quality control, a higher quality wine will be obtained with exactly the same production.

The challenge is that using traditional harvest prediction methods leads to large margins of error, easily from 10% to 25%. It’s too much.

In addition, the wine market is becoming increasingly sophisticated, and this represents a significant difficulty in remaining competitive. It is no longer enough to predict a factor, now the important thing is to achieve the desired balance of different parameters (acidity, pH, etc.) to sell a product that is more appreciated by the public.

This is why Covides decided to join RawData to create a prediction model based on artificial intelligence. The result? In the last two campaigns, the margin of error in maturation has been between 5-7% at 15 days ahead, that is, the precision in the maturation forecast has practically tripled.

Improving communication with producers

To produce 8 million bottles of wine and cava a year, significant coordination is required, and the key is harvest planning.

From Covides they comment that their partners have land spread over very different areas. There are many factors of variability, and it is very difficult to follow the evolution of all the fields. It is necessary to use tools that allow individualized or even intra-parcel monitoring.

They manage different crop areas and a large area, so forecasting prior to the use of new tools was a very complicated issue. And the variability went down more when trying to predict a single variety.

Now the cooperative has found a support with which the confidence of its planning is reinforced in the face of its members, and it even has reliable data for a longer time.

As if that were not enough, with RawData it is possible to see a report of the vineyard input updated in real time by being able to connect with ERP or other programs.

When reducing ripening controls pays off

At Covides they are clear that the role of the technicians, both with their visits to the field and with regard to their management, will continue to be necessary.

At the same time, they emphasize that the use of new tools allows more selective decisions to be made. This translates into faster and more timely tackling threats and saving time to make more strategic decisions.

This translates into faster and more timely tackling threats and saving time to make more strategic decisions. They allow them to provide fertilization information to their partners, to detect which areas are suffering greater stress and to carry out more representative maturation controls for each of the fields.

What is the role of agrotechnology in Covides

From the cooperative they have verified the enormous return on investment of agricultural software, together with an ease of implementation that exceeds expectations. Reasons that justify having already started with RawData the implementation of maturation and volume prediction models.

Another great window of opportunity is seen in continuing to improve connectivity to the partner, which is precisely one of the development focuses of our software at the moment. Hyper-personalizing alerts, sharing updated reports on a daily basis or automating part of the agricultural management are going to be one of the new superpowers of technicians.

Finally, another aspect where they see a lot of potential is RawData’s ability to integrate different tools into one with forecasts, satellite images, weather alerts, etc. The time savings and the discoveries that can be made spark a lot of interest.

Share

Josep Maria Galimany

Director of viticulture

Covides

“RawData predictions help us organize our harvest and hit the bullseye at harvest.”

“The tool allows us to ensure that the 20 million kg enter in an orderly manner and as we want. I have to give information to the partners, and now the management becomes easier.”

“Satellite images give us more clues and save unnecessary trips to fields.”

“RawData can be of interest to anyone who is minimally interested in its exploitation. It is a tool that we all need, be it for one thing or another. I wouldn’t rule anyone out.”