Crop Knowledge Discovery Based on Agricultural Big Data Integration
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research
scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data
The Cell Ontology in the age of single-cell omics
KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes
Evaluating AI capabilities in detecting conspiracy theories on YouTube
Towards a Spatiotemporal Fusion Approach to Precipitation Nowcasting
토론
이 논문과 연결된 포럼 글이 아직 없습니다.