Data Science problem: Create a short and medium-term prediction model for the quantity of raspberries harvested

Predicting the quantity of fruits that can be harvested weekly is essential to support the daily management of red fruit production in greenhouses. This value is important for planning the allocation of human resources necessary to carry out the harvest, predicting the quantity of fruit delivered to customers. In the case of red fruits, which cannot be stored and have a short shelf life, the accuracy of this prediction is essential.

The Summer Berry Company (TSBCo) group produces small fruits throughout the year. Production is carried out in greenhouse tunnels, which allow stable conditions for plant growth. However, not all environmental factors can be controlled, especially meteorological ones. This may have implications for the speed of fruit development and quantity produced at each time.

In this Hackathon, the challenge was launched to create a short and medium-term prediction model for the quantity of raspberries harvested. The project bases its analysis on the history of data collected by the company on agricultural holdings, which includes, among others, production data, characterization of crop phenological parameters, meteorological and environmental data.