Blueberry is a profitable fruit exported from South American countries like Peru, Argentina, and Chile to different continents worldwide. Understanding the crop to have the capacity to estimate or predict how many tons or kilograms of blueberry the farm will produce is critical for the business to attract customers and meet the deadlines. Wrong estimations could lead to important economic losses and impact the prestige of the farm to close future contracts.

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4420288%2Fd0c103228269358b8a9b440b6a549737%2Fproduction-over-time.png?generation=1743670364258287&alt=media)
Figure 1. Weekly yield per plant for two blueberry cultivars. Figure taken from M. E. Cortes, P. A. Mesa, C. M. Grijalba, and M. M. Perez. (2016). Yield and Fruit Quality of the Blueberry Cultivars Biloxi and Sharpblue in Guasca, Colombia.

Agronomists usually take into consideration many sources of variables to determine the yield per week (Fig. 1). Sources such as irrigation, plant nutrition, soil quality, NDVI, and weather, among others, are some of the most common. However, as the crop fields increase in size, the burden of calculating manually the yield per sector becomes harder, so calculations tend to be biased or mistaken. 

In order to make this process easier, we require a tool to assist the agronomist in making better decisions and have a whole picture of the production of his fields. With Artificial Intelligence we can predict the class of the productions as **low, medium and high**.