Estimation of leaf area in coffee leaves (Coffea arabica L.) of the Castillo® variety

Unigarro, Carlos Andrés; Hernández-Arredondo, Juan David; Montoya-Restrepo, Esther Cecilia; Medina-Rivera, Rubén; Ibarra-Ruales, Lizardo Norbey; Carmona-González, Claudia Yoana; Flórez-Ramos, Claudia Patricia

doi:10.1590/1678-4499.0026

Cited by 19 publications

(10 citation statements)

References 18 publications

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“…Leaf rust severity was calculated using a diagrammatic scale (Jefuka et al, 2010). Leaf area (LA) was calculated using formula LA = 0.99927*(L*(-0.14757 + 0.60986*W)), of which L and W are leaf length and leaf width, respectively (Unigarro- Muñoz et al, 2015). The data was collected in November 2015 and 2016, and December 2017.…”

Section: Data Collectionmentioning

confidence: 99%

Resistance Variability of Arabica Coffee Genotypes (Coffea arabica L.) to Leaf Rust Disease (Hemileia vastatrix)

Malau

2019

J. tanam. industri dan penyegar

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Coffee leaf rust disease (Hemileia vastatrix) causes large damage to Arabica coffee plantation in Asia, Africa, and America. In Indonesia, particularly in North Sumatra, the resistance level of Arabica coffee genotypes is still unknown. The objective of this research was to determine the resistance variability of Arabica coffee genotypes to leaf rust disease and its relation to leaf morphology. A total of 84 genotypes grown in North Sumatra were selected in November 2015 and 2016, and December 2017 using a nested design. Data were analyzed using nested design, correlation, stepwise regression, and cluster hierarchy analysis. The result showed that the G56 genotype performed the most resistant to leaf rust disease, with a severity of 5.21%. The severity of leaf rust disease has high genotypic variation, low heritability, and high genetic advance. Leaf morphological ratios showed moderate to high genotypic variation and heritability. The severity of leaf rust (y) significantly correlated with the ratio of leaf length to leaf area (x1) and the ratio of leaf length to leaf width (x2) with the equation y = 2.04 + 62.48x1 - 3.95x2, and multiple correlation coefficients R = 0.470 **. By using the leaf rust severity and the two ratios in the cluster analysis, these 84 genotypes were grouped into five clusters. The result showed that several Arabica coffee genotypes with a high level of resistance to leaf rust disease are potential to be further developed.

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Section: Data Collectionmentioning

confidence: 99%

Resistance Variability of Arabica Coffee Genotypes (Coffea arabica L.) to Leaf Rust Disease (Hemileia vastatrix)

Malau

2019

J. tanam. industri dan penyegar

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“…In this respect, there is an increasing interest in using computer-assisted imaging systems [8] for producing reliable biometric measurements [9] and analyzing phenotypic traits related to plant architecture and leaf characteristics [10]. For instance, data on leaf characteristics can be incorporated into databases [11,12] and employed to validate time-series quantification of leaf morphology (e.g., [13,14]) and to determine the performance of computer-assisted imaging systems and machine learning algorithms used to classify/recognize phenotypic traits of specific genotypes [15].Leaf area is generally measured with destructive or non-destructive methods [16], the latter often preferred as they are faster, cheaper, and non-invasive (i.e., no excision of leaves is required), therefore, permitting repeated and simultaneous measurements of LA and other physiological parameters (e.g., leaf gas exchange or fluorescence) on the same leaves.Collected information, such as leaf blade length (L) and width (W) [17][18][19][20][21][22][23][24][25] or the shape ratio of the leaf (L:W) [26], can be useful for characterizing leaf functions and structure, based only on proxy variables. In particular, the leaf shape ratio is of particular importance in horticultural sciences as it is regulated by several genetic factors and mutations [27], whose diversity can be analyzed in functional [28] and evolutionary terms [29].Thus far, numerous models have been proposed and applied with respect to both leaf (e.g., [20,30,31]) and shoot level [31-41] morphology of several fruit, vegetable, ornamental, medicinal, and aromatic crops [42].…”

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confidence: 99%

“…Collected information, such as leaf blade length (L) and width (W) [17][18][19][20][21][22][23][24][25] or the shape ratio of the leaf (L:W) [26], can be useful for characterizing leaf functions and structure, based only on proxy variables. In particular, the leaf shape ratio is of particular importance in horticultural sciences as it is regulated by several genetic factors and mutations [27], whose diversity can be analyzed in functional [28] and evolutionary terms [29].…”

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confidence: 99%

Analysis of Cultivar-Specific Variability in Size-Related Leaf Traits and Modeling of Single Leaf Area in Three Medicinal and Aromatic Plants: Ocimum basilicum L., Mentha Spp., and Salvia Spp.

Teobaldelli

Basile

Giuffrida

et al. 2019

Plants

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In this study, five allometric models were used to estimate the single leaf area of three well-known medicinal and aromatic plants (MAPs) species, namely basil (Ocimum basilicum L.), mint (Mentha spp.), and sage (Salvia spp.). MAPs world production is expected to rise up to 5 trillion US$ by 2050 and, therefore, there is a high interest in developing research related to this horticultural sector. Calibration of the models was obtained separately for three selected species by analyzing (a) the cultivar variability-i.e., 5 cultivars of basil (1094 leaves), 4 of mint (901 leaves), and 5 of sage (1103 leaves)-in the main two traits related to leaf size (leaf length, L, and leaf width, W) and (b) the relationship between these traits and single leaf area (LA). Validation of the chosen models was obtained for each species using an independent dataset, i.e., 487, 441, and 418 leaves, respectively, for basil (cv. 'Lettuce Leaf'), mint (cv. 'Comune'), and sage (cv. 'Comune'). Model calibration based on fast-track methodologies, such as those using one measured parameter (one-regressor models: L, W, L 2 , and W 2 ) or on more accurate two-regressors models (L × W), allowed to achieve different levels of accuracy. This approach highlighted the importance of considering intra-specific variability before applying any models to a certain cultivar to predict single LA. Eventually, during the validation phase, although modeling of single LA based on W 2 showed a good fitting (R 2 basil = 0.948; R 2 mint = 0.963; R 2 sage = 0.925), the distribution of the residuals was always unsatisfactory. On the other hand, two-regressor models (based on the product L × W) provided the best fitting and accuracy for basil (R 2 = 0.992; RMSE = 0.327 cm 2 ), mint (R 2 = 0.998; RMSE = 0.222 cm 2 ), and sage (R 2 = 0.998; RMSE = 0.426 cm 2 ).Plants 2020, 9, 13 2 of 21 contribute towards food security and social stability. Within this scope, applied research on innovative horticultural practices can make effective use of dynamic crop growth models [4] under conditions optimal for plant growth and for eliciting plant response to abiotic stresses [5], therefore, allowing a more rational use of resources, such as water and nutrients [4].Several fundamental physiological processes such as photosynthesis, transpiration, and cooling are facilitated by leaves [6] and they are, therefore, strongly influenced by leaf morphology (size, shape, symmetry, venation, organization, and petiole characteristics) [7]. The characterization of leaf morphology and quantification of leaf area (LA) and/or leaf area index (LAI) is consequently of paramount importance to horticultural crop science. In this respect, there is an increasing interest in using computer-assisted imaging systems [8] for producing reliable biometric measurements [9] and analyzing phenotypic traits related to plant architecture and leaf characteristics [10]. For instance, data on leaf characteristics can be incorporated into databases [11,12] and employed to validate time-series quantification of lea...

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“…For this reason, many studies still highlight the importance of studying LA of several fruit crops, so that it would be possible to characterise leaf functions and structures, based only on those two important proxy variables [13,24,40,44,59,60,61,62].…”

Section: Resultsmentioning

confidence: 99%

Developing an Accurate and Fast Non-Destructive Single Leaf Area Model for Loquat (Eriobotrya japonica Lindl) Cultivars

Teobaldelli

Rouphael

Fascella

et al. 2019

Plants

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In this research, seven different models to predict leaf area (LA) of loquat (Eriobotrya japonica Lindl) were tested and evaluated. This species was chosen due to the relevant importance of its fruit as an appreciated early summer product and of its leaves and flower as a source of additional income within the nutraceutical and functional food markets. The analysis (calibration and validation) was made using a large dataset (2190) of leaf width (W), leaf length (L), and single LA collected in ten common loquat cultivars. During the analysis, the results obtained using one- and two-regressor models were also evaluated to assess the need for fast measurements against different levels of accuracy achieved during the final estimate. The analysis permitted to finally select two different models: 1) a model based on a single measurement and quadratic relationship between the single LA and W (R2 = 0.894; root mean squared error [RMSE] = 12.98) and another model 2) based, instead, on two measurements (L and W), and on the linear relationship between single LA and the product of L × W (R2 = 0.980; RMSE = 5.61). Both models were finally validated with an independent dataset (cultivar ‘Tanaka’) confirming the quality of fitting and accuracy already observed during the calibration phase. The analysis permitted to select two different models to be used according to the aims and accuracy required by the analysis. One, based on a single-regressor quadratic model and W (rather than L) as a proxy variable, is capable of obtaining a good quality of fitting of the single LA of loquat cultivars (R2 = 0.894; RMSE = 12.98), whereas, the other, a linear two-regressor (i.e., W and L) model, permitted to achieve the highest prediction (R2 = 0.980; RMSE = 5.61) of the observed variable, but double the time required for leaf measurement.

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Estimation of leaf area in coffee leaves (Coffea arabica L.) of the Castillo® variety

Cited by 19 publications

References 18 publications

Resistance Variability of Arabica Coffee Genotypes (Coffea arabica L.) to Leaf Rust Disease (Hemileia vastatrix)

Resistance Variability of Arabica Coffee Genotypes (Coffea arabica L.) to Leaf Rust Disease (Hemileia vastatrix)

Analysis of Cultivar-Specific Variability in Size-Related Leaf Traits and Modeling of Single Leaf Area in Three Medicinal and Aromatic Plants: Ocimum basilicum L., Mentha Spp., and Salvia Spp.

Developing an Accurate and Fast Non-Destructive Single Leaf Area Model for Loquat (Eriobotrya japonica Lindl) Cultivars

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