Can machines recognise stress in plants?

被引：18

作者：

Maldonado-Rodriguez, Ronald ^{[1
]}

Pavlov, Stancho ^{[2
]}

Gonzalez, Alberto ^{[3
]}

Oukarroum, Abdallah ^{[1
]}

Strasser, Reto J. ^{[1
]}

机构：

[1] Bioenerget Lab, CH-1254 Jussey, Switzerland

[2] Univ Asen Zlatarov, Dept Math, Burgas 8010, Bulgaria

[3] INIA Ctra Coruna, Madrid 28040, Spain

来源：

ENVIRONMENTAL CHEMISTRY LETTERS | 2003年 / 1卷 / 03期

关键词：

Artificial neural networks; Chlorophyll a fluorescence; Drought stress; JIP-test; O-J-I-P fluorescence rise; Pea; Pisum sativum; Plants; Self-organizing map (SOM);

D O I：

10.1007/s10311-003-0034-7

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

In this paper we show that chlorophyll a fluorescence signals analysed with the self-organizing map (SOM) can be used as a routine tool for the monitoring and classification of pea varieties (Pisum sativum) according to their degree of resistance against drought stress. Fluorescence kinetics measurements were obtained from non-stressed plants. The aim of this study is to evaluate the applicability of artificial intelligence techniques in eco-physiological research. Our goal is to provide a fast tool that will contribute to the knowledge needed to develop strategies that would help to decrease the impact of environmental stress in agriculture and forestry.

引用

页码：201 / 205

页数：5

共 7 条

[1] SELF-ORGANIZED FORMATION OF TOPOLOGICALLY CORRECT FEATURE MAPS [J].