Big Data Knowledge Acquisition Platform for Smart Farming

Nowadays, big data enables to discover many aspects in agriculture sector such as finding unknown crop patterns or predicting the price of products. However, these massive data are often complex and heterogeneous which includes both structured (e.g., farm information) and unstructured data (e.g., image data, sensor data). It is required new techniques and tools to extract and represent valuable information in the form of human understanding to improve decision making for enhancing farm management. In this paper, we propose a big data knowledge acquisition platform which consists of efficient knowledge acquisition techniques integrated with an intuitive visualization tool supporting decision making applications. Firstly, we deploy open source big data frameworks (e.g., Flume, Hive, HBase) to support developing of multiple methods for collecting and storing data. Secondly, we implement distributed machine learning techniques on Hadoop and Spark to acquire knowledge from big data sources. Finally, we provide a visualization tool on web interface which can display extracted knowledge in multiple views (e.g., charts, tables) to support decision making applications. Experiments with real datasets show that the proposed platform is efficient and effective to answer important questions in smart farming.