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
摘要：
如今，农业数据可以通过各种来源生成，例如：物联网（IoT），传感器，卫星，气象站，机器人，农业设备，农业实验室，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、公司和农学家能够提取高水平的商业和科学知识，改善他们的运营流程和产品质量。然而，在分析这些数据之前，需要将不同的数据源标准化，同质化并集成到统一的数据表示中。在本文中，我们提出了一种农业数据集成方法，使用星座模式，设计成足够灵活，以纳入其他数据集和大数据模型。我们还应用一些方法来提取知识，以提高作物产量;这些方法包括找到合适数量的土壤特性，除草剂和杀虫剂，以提高作物产量和保护环境。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
