Proximal Phenotyping and Machine Learning Methods to Identify Septoria Tritici Blotch Disease Symptoms in Wheat

Odilbekov, Firuz; Armoniené, Rita; Henriksson, Tina; Chawade, Aakash

doi:10.3389/fpls.2018.00685

Cited by 44 publications

(19 citation statements)

References 52 publications

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“…Our group have evidence that other optical non-invasive techniques (e.g., laser induced fluorescence and reflectance spectroscopy) are more suited to the stochastic automatic identification of plant genotypes and plant physiological conditions, including in Vitis [36]. However, the JIP curve pertains more valuable information about the function of plants' photochemical apparatus and might prove useful as a complementary diagnostic tool in HTPP, particularly in proximal phenotyping systems [37]. Genetic programming presented a good global classification success rate (75.2%) when compared with a random classifier (14 %).…”

Section: Discussionmentioning

confidence: 99%

Using Rapid Chlorophyll Fluorescence Transients to Classify Vitis Genotypes

Silva

Figueiredo

Cunha³

et al. 2020

Plants

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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.

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Section: Discussionmentioning

confidence: 99%

Using Rapid Chlorophyll Fluorescence Transients to Classify Vitis Genotypes

Silva

Figueiredo

Cunha³

et al. 2020

Plants

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“…Handheld infrared thermometer was used to estimate drought tolerance in maize [40]. Handheld spectroradiometers were used to identify yellow rust, Septoria tritici blotch, nitrogen use efficiency and several morphological and physiological traits in wheat [41][42][43][44]. Handheld chlorophyll meters and chlorophyll fluorescence meter were used for estimating plant health, photosynthesis, plant nitrogen status and yield plus its components in several crops [40,[44][45][46][47][48].…”

Section: Proximal Phenotypingmentioning

confidence: 99%

High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture

Chawade

Ham

Blomquist³

et al. 2019

Agronomy

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161

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High-throughput field phenotyping has garnered major attention in recent years leading to the development of several new protocols for recording various plant traits of interest. Phenotyping of plants for breeding and for precision agriculture have different requirements due to different sizes of the plots and fields, differing purposes and the urgency of the action required after phenotyping. While in plant breeding phenotyping is done on several thousand small plots mainly to evaluate them for various traits, in plant cultivation, phenotyping is done in large fields to detect the occurrence of plant stresses and weeds at an early stage. The aim of this review is to highlight how various high-throughput phenotyping methods are used for plant breeding and farming and the key differences in the applications of such methods. Thus, various techniques for plant phenotyping are presented together with applications of these techniques for breeding and cultivation. Several examples from the literature using these techniques are summarized and the key technical aspects are highlighted.

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“…A set of 84 bi-parental doubled-haploid (DH) lines derived from a cross between winter wheat cultivars Stigg (resistant to STB) and Nimbus (susceptible to STB) were used to evaluate STB resistance in the greenhouse condition. Planting of wheat materials and the experimental design were done as described previously [48]. Briefly, the seeds were germinated on the moist filter paper in the Petri dishes at 4 • C in dark for 4 days followed by germination at room temperature for another two days.…”

Section: Plant Materials and Experimental Designmentioning

confidence: 99%

“…Inoculum preparation and inoculation condition were performed according to Odilbekov, et al [48]. Twenty-one days after the planting, both sides of marked second and third leaf of the seedlings were brushed with the conidial suspension using a flat watercolour paintbrush (bristle length 15 mm).…”

Section: Inoculation and Disease Assessmentmentioning

confidence: 99%

QTL Mapping and Transcriptome Analysis to Identify Differentially Expressed Genes Induced by Septoria Tritici Blotch Disease of Wheat

Odilbekov

Armoniené

et al. 2019

Agronomy

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Resistance to Septoria tritici blotch (STB) is an economically important trait in many wheat-breeding programs across the world. Several quantitative trait loci (QTL) for STB resistance were identified in wheat but due to the dynamic pathogen population it is necessary to continuously identify new resistance genes/QTL and determine the underlying resistance mechanism. In this work, we integrated QTL mapping and transcriptome profiling to identify candidate genes underlying QTL associated with STB resistance in bread wheat at the seedling stage. The results revealed four QTL on chromosomes 1BS, 1BL, 3AS and 3DL for STB resistance. Among these, two QTL on 2BL and 3DL were mapped for chlorosis, necrosis and pycnidia while the other two on 1BS and 3AS were associated with necrosis and pycnidia. Among the four identified QTL, genes were identified in three QTL (1BS, 2BL and 3DL). In total, 238 differentially expressed genes (DEGs) were localized in 1BS, 16 DEGs in 2BL and 80 DEGs in 3DL QTL region respectively. F-box protein, NBS-LRR disease resistance genes and receptor-like protein kinase were the most over-represented. The results emphasize the importance of integrating QTL and transcriptome analysis to accelerate the identification of key genes underlying the traits of interest.

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Proximal Phenotyping and Machine Learning Methods to Identify Septoria Tritici Blotch Disease Symptoms in Wheat

Cited by 44 publications

References 52 publications

Using Rapid Chlorophyll Fluorescence Transients to Classify Vitis Genotypes

Using Rapid Chlorophyll Fluorescence Transients to Classify Vitis Genotypes

High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture

QTL Mapping and Transcriptome Analysis to Identify Differentially Expressed Genes Induced by Septoria Tritici Blotch Disease of Wheat

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