Article (Published version) (226 Kb) - Limited access to UNIGE
Other version: http://link.springer.com/10.1007/s10311-003-0034-7
Can machines recognise stress in plants?
|Published in||Environmental Chemistry Letters. 2003, vol. 1, no. 3, p. 201-205|
|Abstract||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 fores|
|Keywords||Artificial neural networks — Chlorophyll a fluorescence — Drought stress — JIP-test — O-J-I-P fluorescence rise — Pea — Pisum sativum — Plants — Self-organizing map (SOM)|
|MALDONADO RODRIGUEZ, Ronald et al. Can machines recognise stress in plants?. In: Environmental Chemistry Letters, 2003, vol. 1, n° 3, p. 201-205. https://archive-ouverte.unige.ch/unige:82876|