Water stress detection based on optical multisensor fusion with a least squares support vector machine classifier

被引：51

作者：

Moshou, Dimitrios ^{[1
]}

Pantazi, Xanthoula-Eirini ^{[1
]}

Kateris, Dimitrios ^{[1
]}

Gravalos, Ioannis ^{[2
]}

机构：

[1] Aristotle Univ Thessaloniki, Sch Agr, Agr Engn Lab, Thessaloniki 54124, Greece

[2] Technol Educ Inst Larissa, Sch Agr Technol, Dept Biosyst Engn, Larisa 41110, Greece

来源：

BIOSYSTEMS ENGINEERING | 2014年 / 117卷

关键词：

VEGETATION; MODEL;

D O I：

10.1016/j.biosystemseng.2013.07.008

中图分类号：

S2 [农业工程];

学科分类号：

0828 ;

摘要：

The objective was to optically discriminate between healthy and water stressed wheat canopies. Canopies were grown under greenhouse conditions. The aim was to develop an optical multisensor system that can detect and identify biotic and abiotic stresses. In the current investigation the successful recognition of water stressed and healthy winter wheat plants in the presence of a Septoria tritici infection is presented. The difference in spectral reflectance and fluorescence response between healthy and stressed wheat plants was investigated. Stress type detection algorithms have been developed based on the combination of least squares support vectors machine (LSSVM) with sensor fusion. Through the use of LSSVM, classification performance increased to more than 99%. These results show promise for the development of cost-effective detectors for automated recognition of different biotic and abiotic stresses. (C) 2013 IAgrE. Published by Elsevier Ltd. All rights reserved.

引用

页码：15 / 22

页数：8

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