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

Abstract

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.

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.

“…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]. Currently, LA models for aromatic and medicinal plants comprise several species such as basil, winter red Bergenia, or purple bergenia, calamint, coffee, cherry laurel, bush-willows, jimson weed, wild cucumber, horse-eye bean, lemon balm, peppermint, oleander, mountain mint, opium poppy, ground-cherry, or winter cherry, picrorhiza or kutka, saffron, sugar leaf, snowbell, summer snowflake, tea, common nettle, orange mullein [42], valeriana [43], and pepper plants [44].The world production of medicinal and aromatic plants (MAPs) is expected to rise up to 5 trillion US$ by 2050 [45].…”

“…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]. Currently, LA models for aromatic and medicinal plants comprise several species such as basil, winter red Bergenia, or purple bergenia, calamint, coffee, cherry laurel, bush-willows, jimson weed, wild cucumber, horse-eye bean, lemon balm, peppermint, oleander, mountain mint, opium poppy, ground-cherry, or winter cherry, picrorhiza or kutka, saffron, sugar leaf, snowbell, summer snowflake, tea, common nettle, orange mullein [42], valeriana [43], and pepper plants [44].…”

Lesser-known ethnic leafy vegetables Talinum paniculatum grown at tropical ecosystem: Morphological traits and non-destructive estimation of total leaf area per branch

“…Misgana et al (2018) preferred to use two traits for higher accuracy although it required more time on L and W measurements. Teobaldelli et al (2019) moderately suggested choosing the model according to its main purpose, i.e., for quick comparison or use as an estimation model.…”

Area estimation of soybean leaves of different shapes with artificial neural networks

“…Measuring leaf area is fundamental for studying the photosynthetic efficiency of plants, determining biotic and abiotic damage to crops, analyzing growth, and estimating crop yield (Hosseini, McNairn, Merzouki, & Pacheco, 2015). The length and width of the leaf blade have been used to estimate the leaf area in fruit trees (Teobaldelli et al, 2019), vegetable crops (Padrón et al, 2016;Toebe et al, 2019), and ornamental crops (Fascella, Maggiore, Zizzo, Colla, & Rouphael, 2009;Giuffrida et al, 2011), among others.…”