The increasing availability of high-quality sensors and computational power on low-cost mobile devices like smartphones and tablets is opening new possibilities for adopting this kind of technology for monitoring biophysical processes of interest for agronomic and environmental studies. A method for leaf area index (LAI) estimates based on gap fraction, derived from the segmentation of images acquired at57° below the canopy, was recently proposed and implemented in the smartphone app PocketLAI®, and successfully tested against commercial devices for paddy rice. In this study, PocketLAI was tested against the AccuPAR ceptometer on canopy structures (maize, row-seeded giant reed and natural grassland) that strongly deviate from the ideal assumption behind the simplified model for light transmittance into thecanopy used in the app (i.e., random distribution of infinitely small leaves). The comparison betweenPocketLAI and AccuPAR showed overall good performances for the app, with root mean square error of 0.41, 0.49 and 0.96 m2m−2 for grassland, maize and giant reed respectively, and R2 of 0.86, 0.92 and 0.88.A saturation effect was observed for PocketLAI for LAI values higher than 5 m2m−2 especially for giant reed, with the LAI values obtained with the app markedly underestimating those provided by AccuPAR. Although further studies are needed to better investigate the need for calibrating the app in case of low-quality devices, these results confirm the possible role of PocketLAI in providing a suitable alternativeto the commercial tools available for indirect LAI estimates in contexts characterized by few economicresources or when a high portability is needed.