Open AccessA review on the methods for big data analysis in agriculture
Author(s) -
Boris Evstatiev,
Katerina Gabrovska-Evstatieva
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1032/1/012053
Subject(s) - data science , big data , variety (cybernetics) , computer science , agriculture , geospatial analysis , cluster analysis , precision agriculture , data analysis , obstacle , data processing , data mining , risk analysis (engineering) , artificial intelligence , business , database , geography , remote sensing , archaeology
The integration of information and communication technologies into agriculture lead to the development of precision agriculture. Nowadays it relies on Internet of Things devices, geospatial data, historical and real-time information, which has the potential to transform farming into smart farming. However the use of Big Data requires significantly different skills and knowledge, compared to what many farmers and agronomists possess, which is an obstacle for their effective use. This study aims to summarize and provide insight into the common methods used for data analysis in a wide variety of agricultural applications. Initially, the basic characteristics and sources of agricultural data are explained. Next, a review of the common data analysis methods (classification, clustering and regressions) is provided, containing information about the data sources used as well as the desired goal of the analysis. At the end of the paper, a summary is given on the applicability of data analysis methods depending on the desired goals.