# Data Warehouse and Decision Support on Integrated Crop Big Data

arXiv: https://arxiv.org/abs/2003.04470 · HTML: https://mlsanity.com/paper/2003.04470/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [Structural Textile Pattern Recognition and Processing Based on Hypergraphs](https://mlsanity.com/paper/2103.11271/) (2021)
- [Transforming Agriculture with Intelligent Data Management and Insights](https://mlsanity.com/paper/2401.13672/) (2023)
- [Building Multi-Agent Copilot towards Autonomous Agricultural Data Management and Analysis](https://mlsanity.com/paper/2411.00188/) (2024)
- [An Enhanced Dual Transformer Contrastive Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2510.23617/) (2025)
- [Applying Time Series Deep Learning Models to Forecast the Growth of Perennial Ryegrass in Ireland](https://mlsanity.com/paper/2511.03749/) (2025)

## Abstract (arXiv)

In recent years, precision agriculture is becoming very popular. The introduction of modern information and communication technologies for collecting and processing Agricultural data revolutionise the agriculture practises. This has started a while ago (early 20th century) and it is driven by the low cost of collecting data about everything; from information on fields such as seed, soil, fertiliser, pest, to weather data, drones and satellites images. Specially, the agricultural data mining today is considered as Big Data application in terms of volume, variety, velocity and veracity. Hence it leads to challenges in processing vast amounts of complex and diverse information to extract useful knowledge for the farmer, agronomist, and other businesses. It is a key foundation to establishing a crop intelligence platform, which will enable efficient resource management and high quality agronomy decision making and recommendations. In this paper, we designed and implemented a continental level agricultural data warehouse (ADW). ADW is characterised by its (1) flexible schema; (2) data integration from real agricultural multi datasets; (3) data science and business intelligent support; (4) high performance; (5) high storage; (6) security; (7) governance and monitoring; (8) consistency, availability and partition tolerant; (9) cloud compatibility. We also evaluate the performance of ADW and present some complex queries to extract and return necessary knowledge about crop management.