Water stress detection based on optical multisensor fusion with a least squares support vector machine classifier
Water stress detection based on optical multisensor fusion with a least squares support vector machine classifier
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DOI：
10.1016/j.biosystemseng.2013.07.008
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发表时间：
2014-01-01
影响因子：
5.1
通讯作者：
Gravalos, Ioannis
中科院分区：
文献类型：
--
作者：
Moshou, Dimitrios;Pantazi, Xanthoula-Eirini;Gravalos, Ioannis
目的是光学区分健康和水分胁迫的小麦冠层。冠层在温室条件下生长。目的是开发一种光学多传感器系统，可以检测和识别生物和非生物应力。在目前的调查中，成功地识别水分胁迫和健康的冬小麦植株在存在的壳针孢感染。研究了健康小麦植株和受胁迫小麦植株的光谱反射和荧光响应的差异。将最小二乘支持向量机（LSSVM）与传感器融合相结合，提出了应力类型检测算法。通过使用LSSVM，分类性能提高到99%以上。这些结果表明，开发具有成本效益的检测器，用于自动识别不同的生物和非生物应力的承诺。(C)2013年IAgRE。由爱思唯尔有限公司出版。保留所有权利。
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.