Fruit forecast

Challenges

The meteorological variability generated by climate change causes uncertainty in the evolution of crops that entails added difficulty in planning harvests, and particularly in fruit growing. On the other hand, the increase in the variability of the volume and quality of peach and cherry production means that the investment of resources and dedication of the technical teams to plan the harvests is increasingly higher and not more accurate. The technical teams use different techniques (sampling, ripening controls, gauging, etc.) to determine in advance the aforementioned variables of volume and optimal harvest time, but the reliability of the results that these systems provide has a lot of potential for improvement. The large number of variables that affect both the quality and quantity of production (meteorology, characteristics of the plots, productive areas, etc.) makes it very complex to obtain reliable predictions with traditional approaches, and therefore, That today there is still no successful methodology to be able to know in advance the two main variables that affect harvest planning in peach and cherry cultivation: volume and harvest maturation.

Therefore, improving the reliability of harvest planning becomes a critical factor for the competitiveness of fruit producing companies. Obtaining reliable harvest planning allows you to gain a strategic position when it comes to:

Effectiveness

Make strategic business decisions

Economic profitability

Negotiate sales contracts (prices, delivery dates and volumes) with clients

Efficiency

Reduce production costs thanks to better efficiency in resource management

Optimize

Optimize the cold and logistics capacity of the plants

ACTIVITIES

The project is based on the use of data and Big Data technologies to develop prediction models that anticipate information on the evolution of quality parameters and harvest volumes for the peach and cherry sector. It is understood that these models are scalable to other fruit species later.

Technology linked to Big Data currently makes it possible to integrate multiple sources of information to develop prediction models to reduce uncertainty in harvest planning. The data sources that will be used in this project are divided into 4 main blocks:

Historical data from production companies

a) Ripening controls, using fruit quality parameters such as the degradation of chlorophyll.la in peaches (measured with the DA-meter device) and the sugar content in cherries (measured with a refectrometre). b) History of production volumes by plot (the origin is the central ERP) c) Capacity (the origin is the company records)



Characterization of the plots through:

a) Maps/type of soils
b) Planting details (surface area, age of trees, variety, rootstock, training system...etc)


Project partners

Cluster FEMAC

Coordinator

Cluster FEMAC

Coordinator

VERNIS MOTORS

Beneficiary partner

ALTERITY

Beneficiary partner

EURECAT

Beneficiary partner
With the support of:
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