Estimation of buckwheat leaf area by leaf dimensions

Abstract

The objective of this work was to model and identify the best models for estimating the leaf area, determined by digital photos, of buckwheat (Fagopyrum esculentum Moench) of the cultivars IPR91-Baili and IPR92-Altar, as a function of length (L), width (W) or length x width product (LW) of the leaf blade. Ten uniformity trials (blank experiments) were carried out, five with IPR91-Baili cultivar and five with IPR92-Altar cultivar. The trials were performed on five sowing dates. In each trial and cultivar, expanded leaves were collected at random from the lower, middle and upper segments of the plants, totaling 1,815 leaves. In these 1,815 leaves, L and W were measured and the LW of the leaf blade was calculated, which were used as independent variables in the model. The leaf area of each leaf was determined using the digital photo method (Y), which was used as a dependent variable of the model. For each sowing date, cultivar and thirds of the plant, 80% of the leaves (1,452 leaves) were randomly separated for the generation of the models and 20% of the leaves (363 leaves) for the validation of the models of leaf area estimation as a function of linear dimensions. For buckwheat, IPR91-Baili and IPR92-Altar cultivars, the quadratic model (Ŷ = 0.5217 + 0.6581LW + 0.0004LW2, R2 = 0.9590), power model (Ŷ = 0.6809LW1.0037, R2 = 0.9587), linear model (Ŷ = 0.0653 + 0.6892LW, R2 = 0.9587) and linear model without intercept (Ŷ = 0.6907LW, R2 = 0.9587) are indicated for the estimation of leaf area determined by digital photos (Y) based on the LW of the leaf blade (x), and, preferably, the linear model without intercept can be used, due to its greater simplicity.

Non-destructive method for estimating leaf area of Ocimum gratissimum L. using leaf dimensions

“…Scatter plots fitted between the analyzed variables showed linear and non-linear relationships, which was observed by other studies (Carvalho et al, 2017;Cargnelutti Filho et al, 2021). The determination coefficients (R²) of the equations were above 0.88, showing that at least 88% of the variations in basil leaf area were explained by the models obtained through linear dimensions.…”

“…The determination coefficients (R²) of the equations were above 0.88, showing that at least 88% of the variations in basil leaf area were explained by the models obtained through linear dimensions. As compared to the equations fitted using L or W, those equations adjusted using the LW showed the best criteria for estimating leaf area (Bezerra et al, 2020;Cargnelutti Filho et al, 2021;Lucena et al, 2021;Toebe et al, 2021), except for the exponential, which showed best indexes when using leaf length (L).…”

Artificial neural network approach for predicting the sesame (Sesamum indicum L.) leaf area: A non-destructive and accurate method

“…An amplitude of 12,495 cm and 6285 cm was observed for the length and width of the leaves and 37,353 cm 2 for the leaf area, where the highest coefficient of variation of the data was recorded (>76%) ( Table 1 ). This wide variability of data is of great importance for studies with modeling for prediction of the leaf area of crops, providing models of greater representativeness to be used in plants with leaves of different shapes and dimensions in distinct phenological stages throughout the plant cycle [ 62 , 63 ]. Thus, the number of samples (11,000 leaves) used in this study is considered appropriate for constructing models for predicting leaf area as a function of the linear dimensions of the leaves.…”

Non-destructive method for estimating chrysanthemum leaf area

“…Wide data variability is critical in regression model studies to estimate plant leaf area (LA) (Gomes et al, 2020), since the high variability of the data allows the estimation of more representative models and more accurate equations that can be applied to leaves of different shapes and sizes (Cargnelutti Filho et al, 2021). Direct collection of LA data necessitates a considerable number of leaf measurements, making it an expensive, time-consuming, destructive, and equipmentintensive endeavor.…”