Research on Support Vector Machines Method Modeling for Rice Potassium Nutrition Diagnosis

DOI

10.2991/mmsa-18.2018.12How to use a DOI?

Keywords

rice; potassium nutrition diagnosis; image processing; support vector machines

Abstract

A machine learning method was used to establish a diagnostic model of potassium nutrition for rice obtained from image processing techniques. In this study, super hybrid rice "Liangyoupeijiu" was used as experimental object to set up four kinds of rice cultivation experiments at different potassium fertilization levels, the image data of a total of 1920 groups of the 1st leaves and the 2nd leaves, the 3rd leaves, and their corresponding sheaths were obtained by scanning with a scanner. Nineteen rice characteristic indexes were obtained. Support vector machine was used to establish the diagnostic model of potassium nutrition in nineteen rice characteristic indexes, and to diagnose and identify the potassium nutrition in rice. The experimental results show that the identification method based on image processing and SVM is suitable for the diagnosis of potassium nutrition of the 3rd leaves in rice young panicle differentiation stage with an accuracy of 89%, which provides a reliable and universal method for studying the recognition of potassium nutrition in rice and can meet the needs of agronomic research.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qiong Zhou
AU  - Hongyun Yang
AU  - Jun Yang
AU  - Yuting Sun
AU  - Aizhen Sun
AU  - Wenji Yang
PY  - 2018/03
DA  - 2018/03
TI  - Research on Support Vector Machines Method Modeling for Rice Potassium Nutrition Diagnosis
BT  - Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018)
PB  - Atlantis Press
SP  - 50
EP  - 55
SN  - 1951-6851
UR  - https://doi.org/10.2991/mmsa-18.2018.12
DO  - 10.2991/mmsa-18.2018.12
ID  - Zhou2018/03
ER  -