Models for predicting individual leaf area of forage legumes

Resumo

Leaf area is an essential variable in the quantification of other important leaf characteristics in physiological studies of plants, such as photosynthetic rate and phosphorus content. That is one of the reasons why there is a need for fast and accurate methods to estimate leaf area. The objective of this work was to fit linear or non linear regression models to predict the individual leaf area of six forage legume species, based on digital images analyzed with the package LeafArea, software R. In field experiment, 100 leaves were randomly collected from the following species: C. juncea, C. ensiformis, C. cajan, D. lablab, M. cinereum and M. aterrima, in which the central leaflet length and width were measured. Then, digital images of each leaf were processed for leaf area estimation. These estimates were used to fit leaf area prediction models; in fact, seventy leaves were used to fit the models. The rest of them were used for model validation. For the six species, the complete second-degree multiple linear model can be used to predict leaf area as a function of length and width of the central leaflet, presenting R² above 0.98 and percentage absolute mean error below 9%. In these models, the effect of leaf width is generally greater than the length’s. The R package LeafArea was shown to be a very efficient tool in the estimation of leaf area through the execution of the software ImageJ, with high precision and easy calibration.