Crop Knowledge Discovery Based on Agricultural Big Data Integration

• URL: http://arxiv.org/abs/2003.05043v1
• Date: Wed, 11 Mar 2020 00:13:17 GMT
• Title: Crop Knowledge Discovery Based on Agricultural Big Data Integration
• Authors: Vuong M. Ngo and M-Tahar Kechadi
• Abstract summary: 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. 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.
• Score: 2.597676155371155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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