Estimation of total leaf area in perennial plants using image analysis

Abstract

One feature of most horticultural crop plants that is biologically relevant to their yield and productivity is total leaf area. However, direct methods of estimation of the leaf area cause damage to the plants, whereas indirect methods such as based on light measurement, demand accuracy in the setup of the measurement procedure, which is specific to each crop. Coffee is one of the most important perennial plants related to worldwide trade, and this demands some ability to estimate the productivity of the crop, as well as all the perennial plants involved in production of agricultural products. This study aims to build a model based on indirect measures to estimate the leaf area in coffee plants using image analysis. Two models were evaluated, one based on the height and width of the canopies, and other based on the area of the digital image of a tree. The results of the models have been compared with the real area of the leaves using the destructive approach with measurement of area of all the leaves using a digital scanner. Comparisons between the models and the real values indicated values of adjusted R2 of about 0.82 with a model using the height and the width values, and about 0.91 in the second model which used the area projection. The robustness of the model using the height and the width values were tested using data presented in the literature to other cultivars and achieved R2 = 0.54 with an outlier point and 0.91 without it.