  Nowadays, the agricultural data can be generated through various sources,
such as: Internet of Thing (IoT), sensors, satellites, weather stations,
robots, farm equipment, agricultural laboratories, farmers, government agencies
and agribusinesses. The analysis of this big data enables farmers, companies
and agronomists to extract high business and scientific knowledge, improving
their operational processes and product quality. However, before analysing this
data, different data sources need to be normalised, homogenised and integrated
into a unified data representation. In this paper, we propose an agricultural
data integration method using a constellation schema which is designed to be
flexible enough to incorporate other datasets and big data models. We also
apply some methods to extract knowledge with the view to improve crop yield;
these include finding suitable quantities of soil properties, herbicides and
insecticides for both increasing crop yield and protecting the environment.
