Non-Destructive Estimation of the Leaf Weight and Leaf Area in Common Bean

Abstract

Regression models to predict leaf area and leaf weight in common bean (Phaseolus vulgaris) were fitted using the three leaflets of the leaves. A total of 1504 leaves from 40 genotypes were collected, covering a large range of leaf sizes. Width, length, area, and weight were measured for each leaflet. The total leaf area and weight was obtained by the sum of left, central, and right leaflets. The dataset was randomly divided into training and validation sets. The training set was used for model fitting and selection, and the validation dataset was used to obtain statistics for model prediction ability. The leaf area and leaf weight were modeled using different linear regression models based on the length and width of the leaflet. Polynomial regressions involving both length and width of the leaflet provided very good models to estimate the expected area (R2 = 0.978) and weight (R2 = 0.820) of leaves.