USPLeaf: Automatic leaf area determination using a computer vision system

Abstract

Computer vision systems based on digital image processing have been proposed as alternative tools to traditional
methods to estimate leaf area, replacing the most time-consuming steps and laboring manual measurements. However, many
of the available applications are still based on manual determination of leaf dimensions or require excessive and laborious
user interventions before providing results. USPLeaf was designed to process images containing single or multiple leaves, and
automatically determine the leaf area without user intervention. The accuracy for leaf area measurements of the software was
compared to the results obtained by the standard method, an electronic planimeter (LI-3100). The vegetal species, Mavuno
grass (MAV, Urochloa hybrid) and Macrotyloma axillare (MAC), were chosen because they are characterized by different leaf
shapes. A smartphone camera was used as image capture device. When using a standard black paper square of 9 cm², both LI3100 and USPLeaf provided accurate and precise results, with an estimated average area of 8.90 and 9.00 cm² and a standard
deviation of 0.17% and 0.00%, respectively. The relative error rate for the vegetal species varied from -6.37 to 2.25%. The
regression analysis indicated that the software was a precise tool to estimate leaf area (R²=0.983 for MAV and 0.977 for MAC),
but it also revealed that samples inferior to 25 cm² for grasses and 15 cm² for legume species should be avoided. The software
can be used as an automated tool in image processing aiming to determine leaf area from digital images.