论文标题
基于农业大数据整合的作物知识发现
Crop Knowledge Discovery Based on Agricultural Big Data Integration
论文作者
论文摘要
如今，可以通过各种来源生成农业数据，例如：物联网（IoT），传感器，卫星，气象站，机器人，农用设备，农业实验室，农民，农民，政府机构和农业综合企业。对这些大数据的分析使农民，公司和农艺师能够提取高业务和科学知识，从而提高其运营过程和产品质量。但是，在分析这些数据之前，需要将不同的数据源进行标准化，匀浆并集成到统一的数据表示中。在本文中，我们使用星座架构提出了一种农业数据集成方法，该方法旨在灵活地将其他数据集和大数据模型合并。我们还采用一些方法来提取知识以提高作物产量；其中包括找到合适的土壤特性，除草剂和杀虫剂，以增加农作物产量和保护环境。
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.
