Raw Data Success cases

Juvé & Camps

With 200 years of history and about 300ha of vine, Juvé & Camps is one of the most recognized wineries at the state level. With an innovative character, the technical team takes a step forward by implementing big data solutions to optimize the planning and management of the harvest.

  • Need

    Obtain a harvest prediction model to know in advance dates of harvest taking into account alcoholic degree and acidity evolution.

  • Proposed solution

    From Raw Data we have helped them in the creation of a harvest prediction model to anticipate the maturation date by plot, the production entry curve and the total volume.

    Thanks to the virtual weather stations, producers can also know in advance the probability of rain, the probability of frost and / or the best times to try.

    They also use the mildiu prediction disease model.

UNIO

Cooperative that summarizes production and commercialization efforts of many cooperatives located in the south of Catalonia.

  • Need

    Obtain a harvest prediction model to know in advance dates of harvest taking into account alcoholic degree and acidity evolution.

  • Proposed solution

    From Raw Data we have helped them in the creation of a harvest prediction model to anticipate the maturation date by plot, the production entry curve and the total volume.

    Thanks to the virtual weather stations, producers can also know in advance the probability of rain, the probability of frost and / or the best times to try.

    They also use the mildiu prediction disease model.

Corporació Unió

Covides

Biggest cooperative of wine producers in Catalonia located at Penedès. Manages a high number of producers and hectares.

  • Need

    Obtain a harvest prediction model to know in advance dates of harvest taking into account alcoholic degree and acidity evolution.

  • Proposed solution

    From Raw Data we have helped them in the creation of a harvest prediction model to anticipate the maturation date by plot, the production entry curve and the total volume.

    Thanks to the virtual weather stations, producers can also know in advance the probability of rain, the probability of frost and / or the best times to try.

    They also use the mildiu prediction disease model.

covides caso éxito raw data

Fruits de Ponent

One of the main spanish cooperatives at the level of peach and nectarine production.

  • Need

    Obtain a harvest prediction model to know in advance dates of harvest by plot and curve of entry into production to improve delivery planning Supermarkets and to coordinate sales with cold capacities.

    Provide a weather alert service to your partners through the virtual weather stations.

     

     

  • Proposed solution

    From Raw Data we have helped them in the creation of a harvest prediction model to anticipate the maturation date by plot, the production entry curve and the total volume.

    Thanks to the virtual weather stations, the Fruits de Ponent producers can also know in advance the probability of rain, the probability of frost and / or the best times to try.

    They also use the monilia prediction disease model.

Coexma

High quality tomato producers located in Mazarron (Murcia, Spain) with more than 40 years of experience.

  • Need

    Provide a weather alert service to your partners through the virtual weather stations.

     

     

  • Proposed solution

    Thanks to the virtual weather stations, Coexma producers can also know in advance the probability of rain, the probability of frost and / or the best times to try.

Coexma y Raw Data

Cerima Cherries

Cherry producers formed by a young and innovative team located in the province of Tarragona, have managed to grow exponentially in the last 5 years..

  • Need

    Obtain a harvest prediction model to know in advance dates of harvest by plot and curve of entry into production to improve delivery planning Supermarkets and to coordinate sales with cold capacities.

    Provide a weather alert service to your partners through the virtual weather stations.

     

     

  • Proposed solution

    From Raw Data we have helped them in the creation of a harvest prediction model to anticipate the maturation date by plot, the production entry curve and the total volume.

    Thanks to the virtual weather stations, the Fruits de Ponent producers can also know in advance the probability of rain, the probability of frost and / or the best times to try.

    They also use the monilia prediction disease model.

Vitalpe

Wine company located at Penedès, leaded by a young team that manages a significant number of producers and hectares.

  • Need

    Obtain a harvest prediction model to know in advance dates of harvest taking into account alcoholic degree and acidity evolution.

  • Proposed solution

    From Raw Data we have helped them in the creation of a harvest prediction model to anticipate the maturation date by plot, the production entry curve and the total volume.

    Thanks to the virtual weather stations, producers can also know in advance the probability of rain, the probability of frost and / or the best times to try.

    They also use the mildiu prediction disease model.

Vitalpe

Wine company located at Penedès, leaded by a young team that manages a significant number of producers and hectares.

  • Need

    Obtain a harvest prediction model to know in advance dates of harvest taking into account alcoholic degree and acidity evolution.

  • Proposed solution

    From Raw Data we have helped them in the creation of a harvest prediction model to anticipate the maturation date by plot, the production entry curve and the total volume.

    Thanks to the virtual weather stations, producers can also know in advance the probability of rain, the probability of frost and / or the best times to try.

    They also use the mildiu prediction disease model.

Yolanda Ferrer

Consultoría técnica especialista en fruticultura asesorando en la zona noreste de España

  • Necesidad

    Consultoría técnica en expansión, necesita ofrecer más valor a sus clientes a través de un asesoramiento escalable.

  • Solución propuesta

    Creación de modelo de predicción de grado por parcela.

  • Resultados

    Con las estaciones meteorológicas virtuales ofrece servicios agronómicos escalables a más clientes y conoce con antelación el riesgo de afectación de monilia de cada uno de sus clientes