Non-Destructive leaf area estimation in fruits: update and state of the art
Urteaga Omar, A. F.1
RESUMEN
The aim of this study was to carry out a systematic review on non-destructive estimation of leaf area in fruit trees. Articles published (N= 66) in scientific journals during the last 20 years (20002020) were analyzed. A standard systematic review was conducted, adjusted to the guidelines of the PRISMA statement. The most outstanding results indicate that the models developed to estimate the leaf area in fruit trees have been carried out mainly in developing countries. In general, these are linear models, based on the measurement of the height and width of the leaf, using a portable meter to measure leaf area. Likewise, most of the estimation models have been validated in the same study, which gives them a strong agreement between predicted and measured data. It is expected that the review presented here will constitute a reference material for technicians, professionals and researchers interested in this topic.
Key words: systematic review, update, leaf area, indirect estimation, fruit crops.
ABSTRACT
Estimación no destructiva del área foliar en frutales: actualización y estado del arte. El objetivo del presente trabajo fue realizar una revisión sistemática de la bibliografía sobre estimación no destructiva del área foliar en frutales. Se analizaron 66 artículos publicados en revistas científicas durante los últimos 20 años (2000-2020). Se realizó una revisión sistemática estándar, ajustada a las pautas de la declaración PRISMA. Los resultados más destacados indican que los modelos se han realizado principalmente en países en desarrollo. En general, se trata de modelos lineales, basados en la medición del alto y ancho de la hoja mediante algún medidor portátil del área foliar. Asimismo, la mayoría de los modelos incluidos en esta revisión han sido validados en el mismo estudio, lo que les confiere un fuerte acuerdo entre los datos medidos y los estimados. Se espera que la revisión que aquí se presenta constituya un material de consulta para técnicos, profesionales e investigadores interesados en esta temática.
Key words: revisión sistemática, actualización, área foliar, estimación indirecta, frutales.
1.- Facultad de Ciencias Agropecuarias. Universidad Nacional de Entre Ríos. Ruta Provincial 11, Km 10.5, (3100) Oro Verde (Entre Ríos). E-mail: florencia.urteaga@fca.uner.edu.ar Manuscrito recibido el 30 de septiembre de 2020 y aceptado para su publicación el 3 de noviembre de 2020.
Urteaga Omar, A.F. Non-destructive leaf area estimation in fruits: update and state of the art. FAVE - Ciencias Agrarias 20 (2): 75-90 -CC BY-NC-SA 4.0
A. F. Urteaga Omar
INTRODUCTION
The leaf is the exchange surface between the plant and the environment. It is the organ where the conversion of sunlight into biochemical energy occurs (Blanco and Folegatti, 2005; Pandey and Singh, 2011). The intensity of these exchanges, as well as the photosynthetic activity, have a direct relationship with the leaf area (Mokhtarpour et al., 2010). For this reason, among the variables that characterize the leaves, the leaf area and the parameters associated with it (leaf area index, net assimilation rate, specific leaf area, specific leaf weight) are the most representative, especially in relation to physiological and environmental factors (Sala et al., 2015).
Leaf area is a valuable index to identify and understand many agronomic and physiological processes, such as photosynthetic efficiency, evaporation, respiration, water balance, transpiration, energy balance, yield potential, response to irrigation and fertilizers (Blanco and Folegatti, 2005, Fotis et al., 2018). Although the precise estimation of the foliar area is very important in all crops, it is crucial in fruit trees (Santesteban et al., 2006) due to its impact on the size and filling potential of the fruit (Demirsoy, 2009, Keramatlou et al., 2015). In this sense, Grantz and Williams (1993) pointed out early that the distribution and density of the foliar area affects, although indirectly, the quality of the fruit and the incidence of diseases. This is so because the amount of solar radiation intercepted by the canopies influences the microclimate (light, temperature and humidity) within them (Jianga et al., 2017). Hence, the measurement of the leaf area is of particular importance both for the study of plant physiology, and for analyzing the vegetative and reproductive responses of plants to climatic conditions and to different agronomic and management practices.
In general terms, the leaf area can be estimated through direct or indirect methods (Weiss et al., 2004). Direct methods, although considered the most accurate, are destructive and require expensive instruments, which gives them limited applicability (Kumar et al., 2017). Through direct methods, the total area is usually obtained by measuring the area of all the excised leaves of the plant (Fallovo et al., 2008). By destroying the leaf, it is not possible to make successive measurements when it is necessary, for example, to verify the evolution of the plant during the growing season (Jonckheere et al., 2004).
Indirect methods allow in situ estimation of the leaf area, not require the leaves to be detached, reduce the variability associated with destructive sampling procedures and allow repeated measurements during the growth period of the plant. They are fast, non-destructive, simple, reliable, inexpensive and susceptible to automation (Keramatlou et al., 2015). In indirect methods, the leaf area is inferred directly from observations of some proxy variables, such as leaf length, leaf width or some combinations of these variables (Fascella et al., 2015). The measured variables constitute inputs for the development of mathematical models to predict the leaf area.
The non-destructive prediction of the leaf area using simple equations has become a common tool in agronomy. In general, leaf area estimation models consist of performing a regression analysis in which the leaf area acts as a dependent variable, and the length and width of the leaf as independent variables (Kumar, 2009). Simple mathematical models allow the measurement of the leaf area in the same plants during the growth period and help reduce variability in experiments (Khan et al., 2016). Different mathematical models can be elaborated for the indirect estimation of the leaf area for several cultivars, species and genotypes, or the same model can be applied for several cultivars and different species.
The review of the scientific literature shows an important number of predictive models of the foliar area for fruit crops. Such profusion requires some systematization for the purpose of offering practical and agile consultation tool. Therefore, the general objective of this study was to carry out a systematic review of the studies on the non-destructive estimation of the leaf area in fruit trees published during the first 20 years of this century (from January 2000 to January 2020).
MATERIALS AND METHOD
For the execution of this study, the guidelines of the standard systematic review were followed, as described in Preferred Reporting Items for Systematic Review and Meta-Analyzes (PRISMA) (Moher et al., 2009). Figure 1 shows the search and selection sequence of the analyzed articles.
Figure 1: Flow diagram of the information through the different phases of the systematic review procedure Figura 1: Diagrama de flujo de la información a través de las diferentes fases del procedimiento de revisión sistemática.
A. F. Urteaga Omar
Phase 1 - Identification and selection of databases
Specific and multidisciplinary databases were consulted, namely: Agricola, Science Direct, Scielo, Google Scholar, Scopus and Web of Sciences. The search equation was constituted from the terms “leaf area” AND “non-destructive estimation” AND “fruit”, NOT “cereal”, NOT “vegetable”, NOT “ornamental”, NOT “medicinal”, taking into account that the latter are not precise descriptors of the object of study addressed here. The search covered research published between January 2000 and January 2020. This decision on temporality was due to the fact that other publications (Demirsoy, 2009, Khan et al., 2016) had already analyzed the previous scientific production. Only articles published in academic journals were considered, disregarding books and chapters, doctoral theses, presentations at conferences, popular science magazines, newspapers and commercial publications.
Phase 2 - Exclusion of duplicate articles and filtering the initial results
The initial results were exported to the EndNote X.7 software package for processing. First, a filter was carried out to identify duplicate results. The resulting articles were examined from the information contained in the fields title, abstract, keywords, type of article, publication date and language. As a result of this analysis, a second filtering process was carried out, excluding brief reports, conference proceedings, letters, essays and works written in languages other than English, Spanish or Portuguese.
Phase 3 - Analysis of preliminary results
The remaining articles were qualitatively analyzed. The inclusion criteria adopted in this instance were established based on three axes: (a) measured variables (leaf length, leaf width, etc.); (b) measurement instruments used (millimetric ruler, leaf area meter, etc.); (c) estimation model proposed. As a result of this analysis, publications were eliminated which, even having the terms “leaf area” and “non-destructive estimation” among their descriptors, titles and abstracts, measured variables such as leaf dry weight; they used sophisticated and unusual measuring instruments, and implemented methodologies to estimate leaf area that were not available since the beginning of this century, such as artificial neural networks.
Phase 4 - Quantitative-qualitative synthesis of articles included
The 66 publications selected through the filtering processes were analyzed in order to achieve the proposed objectives. For this purpose, a database was created with the following categories: (a) common and scientific name of the fruit tree; (b) variety / cultivar; (c) type of proposed model (simple linear, quadratic, cubic, exponential, polynomial); (d) R2 value; (e) bibliographic reference; (f) continent / country where the study was developed; (g) year of publication of the article; (h) variables measured to develop the model (leaf width, leaf height, central nervure length, lateral nervure length, mean primary leaf area per shoot, number of leaves, age of plants, etc.); (i) measuring instrument used to estimate leaf area; (j) validation of the model in the same study.
RESULTS
Next, the general characteristics of scientific production on non-destructive methods of leaf area estimation in fruit trees are presented and analyzed. Table 1 presents the fo
Table 1. Studies comprising the bibliographic portfolio analysis (N = 66)Tabla 1. Estudios que integran el portfolio bibliográfi co analizado (N = 66)
| Common/ scientific name | Variety | Model | R 2 | Reference |
| Almond (Prunus dulcis (Miller) DA Webb.) | General | LA= 0.9397 (L x N) - 2.028 | 0.91 | Spann and Heerema, 2010 |
| Apple (Malus domestica Borkh) | Royal Gala; Fuyi Suprema | LA= 0.6962 (L x W) | 0.99 | Bosco et al., 2012 |
| Apple (Malus domestica Borkh) | General | LA= 1.120301 + 0.615 (L x W) | 0.98 | Kishore et al., 2012 |
| Apple (Malus domestica Borkh) | Generos | LA= 0.9602 (L x W) + 2.3472 | 0.98 | Sala et al., 2015 |
| Apple (Malus domestica Borkh) | Pionier | LA= 0.9652 (L x W) + 1.109 | 0,99 | Sala et al., 2015 |
| Apple (Malus domestica Borkh) | Jonathan | LA= 1.0022 (L x W) + 0.1051 | 0.98 | Sala et al., 2015 |
| Apple (Malus domestica Borkh) | Florina | LA= 1.0066 (L x W) + 0.0229 | 0.98 | Sala et al., 2015 |
| Apple (Malus domestica Borkh) | Delicious de Voinesti | LA= 0.9726 (L x W) + 1.1748 | 0.99 | Sala et al., 2015 |
| Apricot (Prunus armeniaca L.) | General | LA= 1.193 + 0.668 (L x W) | 0.98 | Cirillo et al., 2017 |
| Avocato (Persea americana L.) | General | LA= -4.555 + 0.934 (L x W) | 0.97 | Calderón et al., 2009 |
| Avocato (Persea americana L.) | General | LA= 8.2203 + 0.42241 (L x W) | 0.81 | Chattopadhyay et al., 2011 |
| Banana (Musa paradisiaca L.) | General | LA= L x W x 0.80 x N x 0.662 | 0.99 | Kumar et al., 2002 |
| Banana (Musa paradisiaca L.) | Prata Ana | LA= 0.5187 (L x W x N) + 9603.5 | 0.89 | Zuculoto et al., 2008 |
| Blueberry (Vaccinium myrtillus L.) | General | LA= (-0.5095) + [(0.6636 x W2 )] + [(0.1801 x L2 )] + [0.0033 x C2 )] | 0.97 | Celik et al., 2011 |
| Blueberry (Vaccinium corymbosum L.) | Sharp Blue | LA= 0.0075 (L x W)2 + 0.3321 (L x W) + 2.2104 | 0.94 | Cabezas Gutiérrez and Peña Baracaldo, 2012 |
| Blueberry (Vaccinium corymbosum L.) | General | LA= 0.54 + 0.68 (L x W) | 0.97 | Fallovo et al., 2008 |
| Chestnut (Castanea sativa Mill.) | General | LA= 3.36 + 0.11 L2 – 0.26 L2 /W 2 + 1.1 W2 | 0.99 | Serdar and Demirsoy, 2006 |
| Citrus (Citrus) | General | LA= 0.680 (L x W) – 0.103 | 0.99 | Mazzini et al., 2010 |
| Cocoa (Teobroma cacao L.) | CCN51, EET8, IMC67, TCH565 | LA= ȕ0 + ȕ1 L + ȕ2 W + ȕ3 L 2 + ȕ4 x W + ei | 0.98 | Suárez Salazara et al., 2018 |
A. F. Urteaga Omar
Table 1: Continuation Tabla 1: Continuación.
| Coconut (Cocos nucifera L.) | General | LA= 0.8282 (L x W)1.5662 | 0.95 | Fernandes de Sousa et al., 2005 |
| Coffee (Coffea arabica L.; Coffea canephora P.) | General | LA= 0.6626 (L x W) 1.0116 | 0.99 | Antunes et al., 2008 |
| Coffee (Coffea arabica L.) | Castillo | LA= 0.99927 x [L x (0.14757 + 0.60986 x W)] | 0.99 | Unigarro-Muñoz et al., 2015 |
| Coffee (Coffea canephora P.) | General | LA= 0.6723 + 0.6779 (L x W) | 0.96 | Schmildt et al., 2015 |
| Custard apple (Annona squamosa L.) | General | LA= 0.72 (L x W) | 0.77 | Lima Silva et al., 2004 |
| Durian (Durio zibethinus L.) | CHES-D-I; D-II; D-III; D-IV | LA= 0.888 (L x W) - 4.961 | 0.91 | Kumar et al., 2017 |
| Fig (Ficus carica L.) | General | LA= 0.963 (L x W) - 0.0007 (L x W)2 + 8.5860.10-7 (L x W)3 | 0.99 | Casierra-Posada et al., 2007 |
| Fig (Ficus carica L.) | Roxo de Valinhos | LA= 2,879 x L1.5451 | 0.87 | Souza et al., 2014 |
| Grape (Vitis vinifera L.) | Cencibel | LA= 0.587 (L x W) | 0.99 | Montero et al., 2000 |
| Grape (Vitis vinifera L.) | Niagara | LA= 0.637 (W)1.995 | 0.98 | Williams and Martinson, 2003 |
| Grape (Vitis vinifera L.) | De Chaunac | LA= 0.672 (W)1.963 | 0.96 | Williams and Martinson, 2003 |
| Grape (Vitis vinifera L.) | Aragonez syn Tempranillo | LA= 49,1936 + 0.9958 x MLA | 0.97 | Lopes and Pinto, 2005 |
| Grape (Vitis vinifera L.) | Cabernet-Sauvignon | LA= 18.379 x L - 151.41 | 0.97 | Tsialtas et al., 2008 |
| Grape (Vitis vinifera L.) | Merlot | LA= -0.001 CNL2 + 1.462 CNL - 13.551 | 0.97 | Borghezan et al., 2009 |
| Grape (Vitis vinifera L.) | Sauvignon Blanc | LA= 1.0968 LNL2.1628 | 0.93 | Borghezan et al., 2009 |
| Grape (Vitis vinifera L.) | Cavernet Sauvignon | LA= 1.1265 LNL2.0445 | 0.94 | Borghezan et al., 2009 |
| Grape (Vitis vinifera L.) | General | LA= 0.465 + 0.914 (L x W) | 0.98 | Buttaro et al., 2015 |
| Guava (Acca sellowiana Berg. Burret) | General | LA= 0.868 (L x W) - 0.007 (L x W)2 + 0.0001 (L x W)3 | 0.99 | Casierra-Posada et al., 2007 |
| Guava (Psidium guajava L.) | Paluma | LA= 0.011 (L x W)2 + 1.938 (L x W) + 6.401 | 0.99 | Da Vitória et al., 2018 |
| Hazelnut (Corylus avellana L.) | General | LA= 2.59 + 0.74 (L x W) | 0.98 | Cristofori et al., 2007 |
| Kiwifruit (Actinidia chinensis Planch.) | Bruno; Hayward seedling; Matua; Tomuri; Hayward. | LA= 124.56 - (18.3 x L) - (14.68 x C) + 1.26 (L x W) - [0.011 x (L2 x W x C)] + [0.23 x (L x W x C)] - 0.18 x W2 | 0.97 | Zenginbal et al., 2007 |
| Kiwifruit (Actinidia deliciosa A. Chev.) | Hayward | LA= 0.82 (L x W) - 0.28 | 0.99 | Mendoza-de Gyves et al., 2007 |
| Loquat (Eriobotrya japónica L.) | General | LA= 1.81 + 0.68 (L x W) | 0.98 | Mendoza-de Gyves et al., 2008 |
Table 1: Continuation Tabla 1: Continuación.
| Loquat (Eriobotrya japónica L.) | General | LA= -0.516 + 0.667 (L x W) | 0.98 | Teobaldelli et al., 2019 |
| Macadamia (Macadamia integrifoliaMaiden & Betche) | Haes 344 | LA= 1.6635 + 0.6968 (L x W) | 0.96 | Schmildt et al., 2016 |
| Mango (Mangifera indica L.) | Seedling | LA= 0.2452 (L x W) x N | 0.87 | Ghoreishi et al., 2012 |
| Mango (Mangifera indica L.) | Tommy Atkins | AF = 0,000001(L x W)3 - 0,00059 (L x W)2 + 0,77 (L x W) - 0,462 | 0.97 | De Lima et al., 2012 |
| Mango (Mangifera indica L.) | General | LA= -0.3635 + 0.7961 (L x W) | 0.92 | Calderón et al., 2009 |
| Muskmelon (Cucumis melo L.) | Gold Mine | LA= 0.826 x L1.89 | 0.97 | Nascimento et al., 2002 |
| Muskmelon (Cucumis melo L.) | Chioumith | LA= 0.73 (L x W) | 0.99 | Wu et al., 2010 |
| Muskmelon (Cucumis melo L.) | Zhufen | LA= 0.65 (L x W) | 0.99 | Wu et al., 2010 |
| Muskmelon (Cucumis melo L.) | Tianhun | LA= 0.67 (L x W) | 0.99 | Wu et al., 2010 |
| Blackberry (Rubus fruticosus L.) | General | LA= 0.90 + 0.70 (L x W) | 0.97 | Fallovo et al., 2008 |
| Blackberry (Rubus glaucus L.) | General | LA= 0.637 (L x W) + 0.0001 (L x W)2 - 1.875.10-6 (L x W)3 | 0.99 | Casierra-Posada et al., 2007 |
| Nakai (Pyrus pyrifolia Burm F.) | General | LA= 3.804 (L x N) - 39.80 | 0.93 | Spann and Heerema, 2010 |
| Olive (Olea europea L.) | General | LA= 0.308 + 0.780 (L x W) | 0.94 | Koubouris et al., 2018 |
| Olive (Olea europea L.) | General | LA= 0.427 (L x N) + 6.538 | 0.92 | Spann and Heerema, 2010 |
| Passion fruit (Passiflora edulis L.) | Alata | LA= -3.70 + 0.78 | 0.98 | Morgado et al., 2013 |
| Passion fruit (Passiflora edulis L.) | Coccinea | LA= -0.70 + 0.82 | 0,99 | Morgado et al., 2013 |
| Passion fruit (Passiflora edulis L.) | Giberti | LA= -2.00 + 0.41 | 0.93 | Morgado et al., 2013 |
| Passion fruit (Passiflora edulis L.) | Ligulares | LA= -4.98 + 0.88 | 0.95 | Morgado et al., 2013 |
| Passion fruit (Passiflora edulis L.) | Misera | LA= -1.94 + 0.72 | 0.94 | Morgado et al., 2013 |
| Passion fruit (Passiflora edulis L.) | Micronata | LA= 0.73 + 0.75 | 0.96 | Morgado et al., 2013 |
| Passion fruit (Passiflora edulis L.) | Sebacea | LA= 1.17 + 0.79 | 0.98 | Morgado et al., 2013 |
| Passion fruit (Passiflora edulis L.) | Nitida | LA= -5.79 + 0.64 | 0.98 | Morgado et al., 2013 |
A. F. Urteaga Omar
Table 1: Continuation Tabla 1: Continuación.
| Passion fruit (Passiflora edulis L.) | General | LA= 0.25 + 0.64 (L x W) (lanceolate)LA= 4.82 + 0.649 (L x W) (bilobed)LA= - 0.81 + 0.54 (L x W) (trilobed) | 0.95 | Luna Souto et al., 2017 |
| Pawpaw (Caryca papaya L.) | Formasa; Solo | Log AF= 0.315 + 1.85 log CNC | 0.90 | Campostrini and Yamanishi, 2001 |
| Pawpaw (Caryca papaya L.) | Maradol roja | LA= -303.0742 + 31.2028 CNC | 0.93 | Cardona et al., 2009 |
| Peach (Prunus persica L.) | General | LA= -0.5 + 0.23 x (L / W) + 0.67 (L x W) | 0.99 | Demirsoy et al., 2004 |
| Peach (Prunus persica L.) | Jarillo | LA= 1.572 + 0.651 (L x W) | 0.99 | Quevedo García et al., 2012 |
| Peach (Prunus persica L.) | BRS Kampai | LA= 6.852 + 0.823 (L x W) – 0.691 W2 - 1.614 L/W | 0.97 | Sachet et al., 2015 |
| Peach (Prunus persica L.) | Dorado | LA= 1.381 (L x W) - 0.012 (L x W)2 + 0.000 (L x W)3 - 9.034 | 0.87 | Casierra Posada et al., 2017 |
| Peach (Prunus persica L.) | Rubidoux | LA= 0.611 (L x W) + 0.000 (L x W)2 + 2.013 | 0.96 | Casierra Posada et al., 2017 |
| Peach (Prunus persica L.) | Diamante | LA= 1.186 (L x W) - 0.008 (L x W)2 + 0.000 (L x W)3 - 7.880 | 0.93 | Casierra Posada et al., 2017 |
| Peach (Prunus persica L.) | Rey Negro | LA= 0.582 (L x W) + 0.001 (L x W)2 + 2.356 | 0.91 | Casierra Posada et al., 2017 |
| Pear (Pyrus communis L.) | General | LA= 0.922581 + 0.660898 (L x W) | 0.99 | Kiseleva, 2017 |
| Pecan nut (Carya illinoinensis L.) | General | LA= 1.95 + 0.60 (L x W) | 0.93 | Torri et al., 2009 |
| Pecan nut (Carya illinoinensis L.) | Wichita | LA= 4.898 (L x N) - 127.5 | 0.93 | Spann and Heerema, 2010 |
| Persimmon (Diospyros kaki L.) | Haes 344 | LA= 3.83 + 0.96 (L x W) | 0.98 | Cristofori et al., 2008 |
| Pineaple (Ananas comosus L. Merr) | Vitoria | LA= 19.298 (L x W) - 559.9 | 0.94 | Francisco et al., 2014 |
| Pineaple (Ananas comosus L. Merr) | Perola | LA = -214.727 + (2.938 x L) + (74.329 x W) | 0.97 | Pereira dos Santos et al., 2018 |
| Pistachio (Pistacia vera L.) | Badami | LA= 76.97 (L x W) + 35.985 | 0.98 | Karimi et al., 2009 |
| Pistachio (Pistacia vera L.) | Kerman | LA = 8.814 (L x N) - 85.53 | 0.85 | Spann and Heerema, 2010 |
| Plum (Prunus salicina Lindell) | Gold Fruly | LA= -0.354 (L x W) + 0.008 (L x W)2 + 25.607 | 0.77 | Casierra Posada et al., 2017 |
| Plum (Prunus salicina Lindell) | Ecuatoriano | LA= 0.488 (L x W) + 0.001 (L x W)2 + 4.772 | 0.83 | Casierra Posada et al., 2017 |
| Plum (Prunus salicina Lindell) | Methley | LA= -0.104 (L x W) + 0.010 (L x W)2 + 14.458 | 0.62 | Casierra Posada et al., 2017 |
| Plum (Prunus salicina Lindell) | Horvin | LA= -0.236 (L x W) + 0.050 (L x W)2 - 0.000 (L x W)3 + 5.983 | 0.83 | Casierra Posada et al., 2017 |
Table 1: Continuation Tabla 1: Continuación.
| Pomegranate (Punica granatum L.) | Bhagwa | LA= -0.0477 + 0.0282 x L + 0.0842 x W + 0.965 (L x A) | 1.00 | Meshram et al., 2012 |
| Pomegranate (Punica granatum L.) | General | LA= 0.851 (L x W) | 0.99 | Shabani and Sepaskhah, 2017 |
| Raspberry (Rubus idaens L.) | General | LA= 0.03 + 0.71 (L x W) | 0.97 | Fallovo et al., 2008 |
| Redcurrant (Ribes rubrum L.) | General | LA= 1.72 + 0.69 (L x W) | 0.97 | Fallovo et al., 2008 |
| Strawberry (Fragaria vesca L.) | General | LA= 1.89 + 2.145 x (upper lobe length) x (left lobe width) | 0.99 | Demirsoy et al., 2005 |
| Sweet cherry (Prunus cerasus L.) | Early Burlat; Ziraat; Bing; Van; Lambert; Stella, etc. | LA= -22.45 + 2.59 x W + 4.76 x L + 0.36 x C – 0.23 x L2 + 0.034 (W x L) 2 - 0.002 x C x L2 | 0.96 | Demirsoy and Demirsoy, 2003 |
| Sweet cherry (Prunus avium L.) | Bing; Van; Lapins | LA= 0.6612 (L x W) | 0.99 | Cittadini and Peri, 2006 |
| Sweet cherry (Prunus avium L.) | Regina | LA= 6.84 - 2.36 x L + 0.14 L2 - 0.016 x W x L2 + 0.84 L x W x L | 0.98 | Demirsoy and Lang, 2010 |
| Tomato (Solanum lycorpersicum L.) | Facundo | LA= -8.75 (L x W)2 + 10.43 (L x W) -1.44 | 0.88 | Blanco and Folegatti, 2003 |
| Tomato (Solanum lycorpersicum L.) | Jama | LA= -10.12 + 0.834 (L x W) | 0.88 | Carmassi et al., 2007 |
| Uchuva (Physalis peruviana L.) | General | LA= 0.728 (L x W) - 0.001 (L x W)2 + 8.600.10-5 (L x W)3 | 0.99 | Casierra-Posada et al., 2007 |
| Walnut (Juglans regia L.) | General | LA= 1.11 + 0.69 (L x W) | 0.99 | Keramatlou et al., 2015 |
| Walnut (Juglans regia L.) | General | LA= 7.770 (L x N) - 8.994 | 0.86 | Spann and Heerema, 2010 |
| Watermelon (Citrillus lanatus Thumb.) | General | LA= 2.99 + 0.50 (L x W) | 0.98 | Rouphael et al., 2010 |
| White mulberry (Morus alba L.) | General | LA= 0.973 (L x W) - 0.735 | 1.00 | Pandey and Singh, 2011 |
General: for all cultivars belonging to the specie; LA= leaf area; L= leaf length; W= leaf width; C= cultivar; N= total number of leaves plant; CNL= central nervure length; LNL= lateral nervure lengt h; MLA= mean primary leaf area per shoot (cm2)
A. F. Urteaga Omar
llowing data: scientific and common name of the fruit tree, cultivars analyzed, models developed, regression coefficient obtained, and bibliographic reference. The remaining aspects are discussed qualitatively or quantitatively.
Table 1 shows that non-destructive estimation models of leaf area have been developed in the last 20 years for more than 40 fruit plants and for almost 90 different cultivars. Linear models (62%) predominate over polynomials (25%) and exponentials (13%) models, with high regression rates (varying from 0.77 to 1.00) which indicates the goodness of fit of the proposed models. Regarding the origin of the studies, it is observed that South America is the region in which the most research (51%) have been conducted during the period considered, followed by Asia, Europe and North America, in that order. A detailed analysis shows that Brazil emerges as the country in South America where the greatest number of investigations have been carried out (45%), leaving the remaining 6% distributed among Colombia, Argentina, Cuba and Costa Rica. In Asia, India and Iran stand out as prolific countries (27%) in the production of scientific knowledge on the subject, while Europe and North America together represent 22% of the total.
With respect to the periodicity of the publications it is observed that the research has developed in a markedly irregular way. In this sense, an upward trend has been observed since the beginning of the 21st century, reaching its maximum level in 2012. This boom the publications was followed by a saw-type distribution with peaks corresponding to the years 2015 and 2017. In the last two years, there has been a significant drop in the number of publications on the subject.
When the strictly operational aspects of the articles that make up the portfolio studied are considered, it is noted that regarding the variables measured, the length and width of the leaves (84%) predominate; regarding measuring instruments used, protrude portable leaf area meter, type LICOR, model 3000 or 3100 (41%); in turn, Excel, SAS and SPSS emerge as the main computer programs used for the calculation of the regression equations and the development of the models. Finally, it should be noted that there is a very important percentage of studies (77.50%) that have validated the model, either with a new sample of the same fruit trees or with different cultivars to those used to develop the model.
DISCUSSION
The general objective of the study was to provide a systematic review of articles on non-destructive estimation of leaf area in fruits, published between January 2000 and January 2020. For this purpose, a bibliographic portfolio, composed of 66 scientific articles that responded to the defined search profile, was examined. The detailed analysis of the selected articles offers an overview of the most recent scientific evidence on this problem, being able to become a reference material for technicians, professionals and researchers interested in this subject. Because it is a descriptive-retrospective work, the study carried out includes the main trends that have characterized the published research on non-destructive estimation of leaf area in fruits during the last 20 years, which can be summarized as follows:
- a.
- most of the studies have been carried out in emerging or developing countries. Possibly because they are relatively inexpensive studies, which do not require sophisticated instruments or facilities equipped with the latest technology,
- b.
- models have been developed for almost all fruit crops, from the most popular to the least traditional, such as durian (Kumar et al., 2017). What gives horticultural researchers a considerable competitive advantage to the equip them with simple and reliable methods to measure leaf area in a non-destructive way,
- c.
- most models developed employing as input variables proxy such as the length and/or width of leaves (Rouphael et al., 2010). In all cases, these are simple and non-destructive methodologies that preserve the canopy, allowing the reuse of the same leaves. This also makes it easier to measure the leaf area of the same plants during the growth process (Kalacska et al., 2005),
- d.
- in general terms, the proposed models are based on simple equations, which can be calculated with statistical resources available to all users. This transforms them into ductile tools, easy to handle and reliable when making decisions,
- e.
- finally, it is noteworthy that almost all the models analyzed in this review have been validated within the same study. This gives them a strong consistency between the observed and estimated leaf area, increasing prediction accuracy.
CONCLUSSION
Leaf area is a key parameter in various agronomic processes and physiological studies (Fanourakis et al., 2016). Among other aspects, it plays a key role on the size and filling of the fruit (Keramatlou et al., 2015). For this reason, having simple, accurate and inexpensive methodologies to estimate the foliar area is key in the physiology of fruit trees. This study presents a review of 66 models, developed around the world during the last 20 years, to measure the foliar area in fruit crops in situ and in a non-invasive or non-destructive way. It is expected to constitute a useful contribution to technicians, academics and researchers interested in this topic.
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