doi: 10.4304/jsw.9.4.841-846
A Novel K-Nearest Neighbor Algorithm Based on I-Divergence with Application to Soil Moisture Estimation in Maize Field
- 1 College of Water Conservancy and Construction, Northeast Agricultural University, Harbin, China
- 2 College of Science, Northeast Agricultural University, Harbin, China
Abstract
This paper proposes a novel k-nearest neighbor algorithm to predict soil moisture in maize field. In order to estimate soil moisture in maize field accurately without any destruction to root and soil, this paper uses biological characteristics of maize to estimate soil moisture, including plant height, leaf area, stem diameter, dry weight and fresh weight, all the values of which are non-negative. So a novel k-nearest neighbor based on I-divergence (ID_KNN) is proposed. ID_KNN uses I-divergence as the distance metric instead of Euclidean distance, which is more effective when the data is positive. The proposed method is tested on datasets in six growth stages of maize, and the experimental results show that ID_KNN is more effective in accuracy and macro F<sub>1</sub> measure than traditional k-nearest neighbor algorithm.
Keywords
- soil moisture
- k-nearest neighbor
- dista
How to Cite
Xiangyan Meng, Zhongxue Zhang, and Xinying Xu, "A Novel K-Nearest Neighbor Algorithm Based on I-Divergence with Application to Soil Moisture Estimation in Maize Field," Journal of Software, vol. 9, no. 4, pp. 841-846, 2014. https://doi.org/ 10.4304/jsw.9.4.841-846
Copyright & License
Copyright © 2014 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).