'Faculty of Electrical Engineering and Computing, Univ. of Zagreb'
Doi
Abstract
Intelligent agriculture can renovate agricultural production and management, making agricultural production truly scientific and efficient. The existing data mining technology for agricultural information is powerful and professional. But the technology is not well adapted for intelligent agriculture. Therefore, this paper introduces data visualization and big data analysis into the application scenarios of intelligent agriculture. Firstly, an intelligent agriculture data visualization system was established, and the RadViz data visualization method was detailed for intelligent agriculture. Moreover, the intelligent agriculture data were processed using dimensionality reduction through principal component analysis (PCA) and further optimized through k-means clustering (KMC). Finally, the crop yield was predicted using the multiple regression algorithm and the residual principal component regression algorithm. The crop yield prediction model was proved effective through experiments
