Volume 9 Number 4 (Apr. 2014)
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JSW 2014 Vol.9(4): 841-846 ISSN: 1796-217X
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

Xiangyan Meng1, 2, Zhongxue Zhang1, Xinying Xu2
1College of Water Conservancy and Construction, Northeast Agricultural University, Harbin, China
2College 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 F1 measure than traditional k-nearest neighbor algorithm.

Index Terms—soil moisture, k-nearest neighbor, distance metric, I-divergence

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Cite: Xiangyan Meng, Zhongxue Zhang, 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.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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