Using Rapid Chlorophyll Fluorescence Transients to Classify Vitis Genotypes.

Abstract

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.

Plant phenotyping relevance

Rapid chlorophyll fluorescenceを用いたブドウ遺伝型の分類手法と機械学習性能を評価しており、植物フェノタイピング手法が研究の中心である。

Code and data availability

The article describes Kautsky curve measurements on seven Vitis genotypes classified with KNN, decision trees, neural networks, and genetic programming, but contains no data availability statement, no public deposit of the fluorescence transient dataset, and no authors' code repository. URLs mentioned (MEGA, OIV report

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