Crop Knowledge Discovery Based on Agricultural Big Data Integration
This work addresses data integration challenges for farmers and agronomists, but it appears incremental as it builds on existing data integration and knowledge extraction methods.
The paper tackles the problem of integrating diverse agricultural data sources by proposing a flexible constellation schema method, and applies knowledge extraction techniques to improve crop yield by optimizing soil properties and chemical inputs.
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.