Can machines recognize
stress in plants?
Link: Environmental Chemistry Letters (2003) 1(3): 201–205
Springer
ISSN: 1610-3653 (Paper) 1610-3661 (Online)
DOI: 10.1007/s10311-003-0034-7
For a PDF copy of this paper click here
Ronald Maldonado Rodríguez1, Stancho Vaelkanov Pavlov2,
Alberto Gonzalez Moreno3, Abdullah Okarum1, Reto Strasser1
(1)
Bioenergetics Laboratory, 10 Chemin des Embrouchis, CH-1254 Jussy,
(2) Department of
Mathematics, University Asen Zlatarov,
8010
(3) INIA Carretera de la Coruña,
28040 Madrid, Spain
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 -
Kohonen
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
forestry.
![]()
A Methodological Approach for Pattern Recognition System using
discriminate analysis and artificial neural networks
Link:
Proceedings of the 6th WSEAS Neural Networks, Fuzzy Systems, Evolutionary
Computing Conference held in
WSEAS Transactions
Journal (Accepted for Publication July 2005)
Anna Pérez-Méndez,
Elizabeth Torres-Rivas,
Francklin Rivas-Echeverría, Ronald
Maldonado-Rodríguez
This work is the result of a scientific collaboration between the Escuela de Estadística and the Laboratorio de Sistemas Inteligentes de la Universidad de Los Andes,
Keywords: - Classification, Pattern recognition, Discriminate analysis,
Artificial Neural Networks
Abstract
In this work it is presented a methodology for
the development of a pattern recognition system using classification methods as
discriminate analysis and artificial neural networks. In this methodology, the
information statistical analysis is contemplated, with the purpose of retaining
the observations and the important characteristics that can produce an
appropriate classification, and allows, as well, to detect
outliers’ observations, and multicolinearity between
variables, among other things.
![]()
Pisum sativum classification
based on a methodological approach for pattern recognition using discriminant analysis and neural networks
Link:
Proceedings of the 6th WSEAS Neural Networks, Fuzzy
Systems, Evolutionary Computing held in
WSEAS Transactions
Journal (accepted for
publication 07.05)
Anna Pérez-Méndez, Ronald Maldonado-Rodríguez, Elizabeth Torres-Rivas, Franklin
Rivas-Echeverría.
This work is the result of a scientific collaboration
between the Escuela de Estadística and the Laboratorio de Sistemas Inteligentes de la
Universidad de Los Andes,
Keywords: - Classification, Pattern recognition, Discriminate analysis,
Artificial Neural Networks
@ This paper
obtained the Best
Student Paper Award for Fuzzy Systems at the 6th WSEAS Lisbon 2005 Conference. Read more here.
Abstract
In this work
a statistical analysis-based methodological approach for a pattern recognition system
using discriminate analysis and neural networks is used for the classification
of Pisum sativum (pea) according to
the drought resistance. The statistical techniques used in the exploratory
analysis are a fundamental tool in the creation of variables sets and
observations for the model adjustment in the neural models and in the
discriminate models.
WSEAS
is The World Scientific and
![]()
Chlorophyll a fluorescence patterns of six deciduous forest tree
species exposed to normal and elevated CO2
R. Maldonado Rodríguez, R. Strasser. Submitted
Bioenergetics Laboratory, 10 Chemin des Embrouchis,
CH-1254 Jussy,
Abstract
Artificial Neural Networks (ANN) are able to
discover “patterns” in multivariate data. We show that ANN may offer a
realistic opportunity to automation of physiological patterns identification
and plant stress quantification by using Chlorophyll a Fluorescence (CF)
signals as network information input. Fluorescence is a widely spread
technique used in many photosynthesis and eco-physiology research laboratories,
and it finds numerous applications, from agriculture and forestry, through
marine biology and chronobiology to exobiology research. CF signals are a
direct measure of photosynthetic performance in plants and algae. Fluorescence
signals are a definitive proof for photosynthesis. Measuring fluorescence is
cheap, fast, and non-destructive. In a relative short time, thousands of
fluorescence curves can be collected. The task of identification of CF patterns
that correlate with other physiological parameters becomes a necessity. The
physiological responses of photosynthetic organisms to well-defined stimuli have been observed to be similar, presenting well-defined
patterns and indeed this characteristic suggest the possibility to group
plants, green algae and cyanobacteria into categories or classes according to
their specific fluorescence pattern. Since pattern recognition is the primary
emphasis, an ANN seems to be the most logical method of solving this problem.
Formed by simulated neurons connected together much the same way the brain's
neurons are, ANN are able to associate and generalize without rules. They have
solved problems in pattern recognition, robotics, speech processing, financial
predicting, and signal processing, to name a few. The Self-Organizing Feature
Map (SOM) is a popular ANN. We build a SOM using plant fluorescence signals as
stimulatory input and the resulting Fluorotopic Map has shown to be a valuable
tool for identification of plant classes. Such classes may vary according to
their taxonomy, functional groups, wild type or genetically modified plants,
degree of stress effects, etc. We demonstrate a novel methodology for plant
stress survey using short video sequences of whole leaf fluorescence analyzed
with a Batch-SOM. The new developed technique includes spatio-temporal analysis
of fluorescence kinetics obtained from video processing combined with fast
fluorescence induction measurements. Using this technique, a new method for
recognizing and quantifying plant stress has been developed.
We have tested the applicability of this new technique within the Swiss Canopy
Crane Project framework (Hofstetten,
![]()
Self-Organizing Map (SOM) for monitoring the evolution of Rhizobium
nodulation status in Vigna unguiculata
Ronald Maldonado Rodríguez, Patrick Schmitz, Reto
Strasser. Submitted
Bioenergetics Laboratory, 10 Chemin des Embrouchis,
CH-1254 Jussy,
Abstract
Chlorophyll a fluorescence is
a useful and non-invasive tool to screen for the effects of many biotic and
abiotic parameters on photosynthesis in plants. The Chl a fluorescence emitted by leaves after excitation with red light was measured with a portable fluorometer. The collected data
showing the polyphasic OJIP Chl a fluorescence rise were analysed using
the JIP-test (Strasser and al. 2000) which provides biophysical parameters
indicating Photosystem II properties. Seeds of Vigna unguiculata were sterilized before
germination. Seedlings were planted in Magenta jars (used for hydroponic
cultures) filled with a nitrate-deficient solution (B&D solution). The
roots of some of the plants were inoculated with Rhizobium sp. strain NGR234 (109 bacteria/200 µl) four
days after germination. The other plants were grown on various concentrations
of KNO3 (0, 0.5, 1, 5, 10 and 20 mM). The
plants were followed for 5 weeks. During this time fluorescence measurements were done on the first and
second mature leaves. We have use an Artificial Neural Network (Kohonen's
Self-Organizing Map or SOM) to analyze the raw fluorescence data. The generated
map shows very well defined groups of different concentrations creating a
gradient from low to high nitrate content. The Rhizobium inoculated plants in
the fluorescence SOM map is moving in time according to the hypothetical
nitrogen supply model. This permits us to establish a method for screening the
nodulation evolution as well as nitrogen deficiency in vivo on the level of the leaves.
![]()
Ecophysiological responses to summer drought in Pinus halepensis Mill.
seedlings of five provenances
Rafael Mª Navarro1, David Ariza1, Ronald Maldonado Rodríguez, Francisco Canovas.
In preparation
Laboratory of Bioenergetics and
Microbiology.
Resumen
En este ensayo se estudia la utilidad de las medidas de la cinética de
inducción de fluorescencia de la clorofila de hojas in situ para detectar la respuesta temprana a estrés hídrico
moderado de cinco procedencias Pinus
halepensis Mill.. Las
plantas se sometieron a un ciclo de sequías de 28 días en una cámara de cultivo
a 21ºC. Se han encontrado diferencias significativas de supervivencia entre las
procedencias a los 21 días, pero no al final del ensayo. La variación en la
fluorescencia ha mostrado que la eficiencia potencial del fotosistema II de las
plantas sometidas a un estrés hídrico moderado es menor conforme aumenta el
nivel de estrés (medido en potencial hídrico) y que esta diferencia varía entre
procedencias, por lo que puede representar una medida indirecta del nivel de
estrés en etapas previas a la aparición de perdidas generales de supervivencia.
![]()
Quality
assessment of urban trees: A comparative study of physiological
characterisation, airborne imaging and on site fluorescence monitoring by the
JIP-test
Link: Journal of Plant Physiology.
160 (1): 81–90 (2003)Urban
& Fischer Verlag Publishers
For
a PDF copy of this article click here
Petar
H. Lambrev, Vassilij N. Goltsev, Ronald
Maldonado Rodríguez, Reto J. Strasser
1 Faculty of
Biophysics. University of Sofia “Kliment Ohridskii”; Sofia, Bulgaria
Effects
of Lindane on the Photosynthetic Apparatus of the Cyanobacterium Anabaena:
Fluorescence Induction Studies
and Immunolocalization of
Ferredoxin-NADP+ Reductase.
Environmental
Sciences & Pollution Research 11 (2) 98–106 (2004)
DOI: http://dx.doi.org/10.1065/espr2003.10.175
For a PDF copy of this article click here
3 Bioenergetics
and Microbiology Laboratory, 10 Chemin des Embrouchis, CH-1254 Jussy,
Switzerland
Intention, Goal,
Scope, Background
Software for
calculation and plotting of the membrane energetic fluxes: The pipeline model
R. Maldonado Rodriguez, R. Strasser
Fluorescence Workshop. Institute of Plant Physiology.
University of Leipzig
Oral
presentation Basel March 2000
R. Maldonado Rodriguez, R. Strasser
Swiss Canopy Crane Project Workshop. Institute of
Botany, Basel.
Oral
presentation Basel April 2001
R. Maldonado Rodriguez, R. Strasser
Swiss Canopy Crane Project Workshop. Institute of
Botany, Basel.
Oral
presentation Basel March 2003
R. Maldonado Rodriguez, R. Strasser
Swiss Canopy Crane Project Workshop. Institute of
Botany, Basel.
Oral
presentation Basel February 2004
R. Maldonado Rodriguez, R. Strasser
Swiss Canopy Crane Project Workshop. Institute of
Botany, Basel.
The JIP Test: A
New Tool in Plant Research
R. Maldonado Rodríguez, C. Hermans, R.
Strasser
Biotechnology
Start-up Workshop organized by ETH Zurich
Biosensing methods to assess
environmental stress encountered by sugar beet (Beta vulgaris L.).
Hermans, C. Maldonado-Rodríguez
R., Strasser, R. J.
Proceedings of the 24th IIRB Congress. 26-27 June
2001. Bruges, Belgium. Page 424-428
Coral life as
probed by their fluorescence emission
F. Sinniger, R. Maldonado Rodríguez, R. J. Strasser
Link to abstract in CSIRO PUBLISHING - Science Access
Proceedings of International Photosynthesis Congress.
Brisbane Convention & Exhibition
Proceedings of the 12th International Congress on
Photosynthesis. Brisbane Australia 2001
P. Schmitz, R. Maldonado Rodríguez, R. Strasser
Link to abstract
in CSIRO PUBLISHING - Science Access
Proceedings of International Photosynthesis Congress.
Brisbane
Convention & Exhibition
Proceedings of the 12th International Congress on
Photosynthesis. Brisbane Australia 2001
In vivo
Chlorophyll a Fluorescence imaging of herbicide infiltration in Pisum sativum
R. Maldonado
Rodríguez, R. Strasser
Oral
presentation
Luminescent
control of biotic and abiotic stress effects in plants
Goltsev, V., Zaharieva, I., Lambrev, Maldonado-Rodriguez, R., Strasser, R. J.
European Workshop on Environmental Stress and Sustainable Agriculture, 7-12 September 2002,
Alberto González Moreno1, Ronald Maldonado Rodríguez2
1 INIA Carretera de la Coruña, 28040 Madrid, Spain
Plant Physiology
Workshop
Artificial Neural
Networks for the Characterization of Eight Varieties of Pea (Pisum Sativum)
@ This
poster was awarded with the “BEST STUDENT POSTER”
prize, selected among other 633 posters.
R. Maldonado
Rodríguez1, A. Gonzales2, L. Ayerbe2, J. Sanches2, S. V. Pavlov3,
R. Strasser1
2 INIA Ctra de la Coruña, 28040 Madrid, Spain
3 Department of Mathematics, University Assen
Zlatarov, 8010
How Pea Plants
Respond To Elevated Temperature and Repetitive Saturating Light Pulses
A. Okarum, R. Maldonado Rodríguez, R. J. Strasser
Characterization Of Eight Varieties Of Pea (Pisum Sativum) By the
Jip-Test
A. Gonzales1, F. Reverchon2, R. Maldonado Rodríguez2, R. Strasser2
1 INIA Ctra de la Coruña, 28040 Madrid, Spain
Poster EMEC3
Neural Networks
for Monitoring Environmental Stress Effects on plants
Ronald Maldonado
Rodríguez1, Stancho Vaelkanov
Pavlov2, Reto Strasser1
3rd European Meeting on Environmental Chemistry EMEC3,
December 11 to 17 2002
Poster EMEC3
P. Schmitz1,
R. Maldonado Rodríguez1,
S. Pavlov2, W. Broughton3, R. Strasser1
2 Department of Mathematics, Technological University Assen Zlatarov, 8010
3rd European
Meeting on Environmental Chemistry EMEC3, December 11 to 17 2002
Poster
A fast routine for monitoring plant health status
A. Okarom, R. Maldonado
Rodríguez, S Elmadidi & R. J. Strasser
3rd European
Meeting on Environmental Chemistry EMEC3, December 11 to 17 2002
Oral presentation
Ronald Maldonado Rodríguez1, Stancho Pavlov2, Reto Strasser1
(1) Bioenergetics Laboratory, 10 Chemin des
Embrouchis, CH-1254 Jussy,
(2) Department of Mathematics, University Asen Zlatarov, 8010
Oral presentation
Can machines recognize stress in plants? A neural
Network Approach
Ronald Maldonado Rodríguez1, Stancho Pavlov2, Reto Strasser1
(1) Bioenergetics Laboratory, 10 Chemin des
Embrouchis, CH-1254 Jussy,
(2) Department of Mathematics, University Asen Zlatarov, 8010
Poster presentation
Oral presentation
R. Maldonado
Rodríguez, R. Strasser
Bioenergetics
Laboratory, 10 Chemin des Embrouchis, CH-1254 Jussy,
Swiss Canopy Crane Project Workshop February 2004.
Rafael Mª Navarro Cerrillo, Ronald Maldonado Rodríguez, David Ariza
Bioenergetics Laboratory, 10 Chemin des Embrouchis,
CH-1254 Jussy,
Anna Pérez-Méndez, Elizabeth Torres-Rivas, Francklin
Rivas-Echeverría, Ronald
Maldonado-Rodríguez
WSEAS Neural Networks, Fuzzy Systems, Evolutionary Computing
Conference (Lisbon 2005)
Poster
Presentation.
Substituting the former dinosaurs by trees reverts global
warming and climate changes
Ronald
MALDONADO RODRIGUEZ1, Yuxin YUAN2,
Reto J. STRASSER3
Eighth Swiss Global Change Day.
Plant Biology
5(3):315-323, 2003 May
http://www.blackwell-synergy.com/doi/abs/10.1111/j.1469-8137.2005.01385.x
· The Bioenergetics Laboratory of the
· The Microbiology Laboratory of the
Plant Biology department at the
· Chlorophyll Fluorescence and Fluoromatics web page at Geocities. Link
Here