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Article

Using Rapid Chlorophyll Fluorescence Transients to Classify Vitis Genotypes

1
Biosystems and Integrative Sciences Institute (BioISI), Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
2
National Station of Viticulture and Enology, 2565-191 Dois Portos, Portugal
3
LASIGE, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
4
NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal
*
Author to whom correspondence should be addressed.
Plants 2020, 9(2), 174; https://doi.org/10.3390/plants9020174
Received: 16 December 2019 / Revised: 15 January 2020 / Accepted: 16 January 2020 / Published: 1 February 2020
(This article belongs to the Special Issue 2019 Feature Papers by Plants’ Editorial Board Members)
When a dark-adapted leaf is illuminated with saturating light, a fast polyphasic rise of fluorescence emission (Kautsky effect) is observed. The shape of the curve is dependent on the molecular organization of the photochemical apparatus, which in turn is a function of the interaction between genotype and environment. In this paper, we evaluate the potential of rapid fluorescence transients, aided by machine learning techniques, to classify plant genotypes. We present results of the application of several machine learning algorithms (k-nearest neighbors, decision trees, artificial neural networks, genetic programming) to rapid induction curves recorded in different species and cultivars of vine grown in the same environmental conditions. The phylogenetic relations between the selected Vitis species and Vitis vinifera cultivars were established with molecular markers. Both neural networks (71.8%) and genetic programming (75.3%) presented much higher global classification success rates than k-nearest neighbors (58.5%) or decision trees (51.6%), genetic programming performing slightly better than neural networks. However, compared with a random classifier (success rate = 14%), even the less successful algorithms were good at the task of classifying. The use of rapid fluorescence transients, handled by genetic programming, for rapid preliminary classification of Vitis genotypes is foreseen as feasible. View Full-Text
Keywords: Kautsky effect; k-nearest neighbors; decision trees; artificial neural networks; genetic programming; molecular markers; Vitis; chlorophyll a fluorescence; photosynthesis Kautsky effect; k-nearest neighbors; decision trees; artificial neural networks; genetic programming; molecular markers; Vitis; chlorophyll a fluorescence; photosynthesis
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MDPI and ACS Style

Marques da Silva, J.; Figueiredo, A.; Cunha, J.; Eiras-Dias, J.E.; Silva, S.; Vanneschi, L.; Mariano, P. Using Rapid Chlorophyll Fluorescence Transients to Classify Vitis Genotypes. Plants 2020, 9, 174. https://doi.org/10.3390/plants9020174

AMA Style

Marques da Silva J, Figueiredo A, Cunha J, Eiras-Dias JE, Silva S, Vanneschi L, Mariano P. Using Rapid Chlorophyll Fluorescence Transients to Classify Vitis Genotypes. Plants. 2020; 9(2):174. https://doi.org/10.3390/plants9020174

Chicago/Turabian Style

Marques da Silva, Jorge, Andreia Figueiredo, Jorge Cunha, José E. Eiras-Dias, Sara Silva, Leonardo Vanneschi, and Pedro Mariano. 2020. "Using Rapid Chlorophyll Fluorescence Transients to Classify Vitis Genotypes" Plants 9, no. 2: 174. https://doi.org/10.3390/plants9020174

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