Aim: The objective of this short communication is to study the opportunity of using a smartphone application for leaf area index (LAI) observations within vineyards in southern France in a farmsourcing context, i.e. when several operators make parallel acquisitions over the same area. In this context, several sources of variability are likely to affect measurement quality, such as the smartphone model or the operator. Understanding these sources of variability will enhance the ability to properly interpret LAI observations to produce relevant information for decision-making. Methods and material: A study was specifically designed to evaluate the ability of a smartphone application to differentiate sites with different LAI and to determine the origin and the relative importance of different sources of variation in a context of farmsourcing data collection. This focused on the VitiCanopy application, which has been developed specifically for viticulture LAI measurements. Measurements were performed by 18 operators with 11 different smartphone models, on three different vines presenting controlled canopy size to evaluate the ability of the smartphone application to differentiate sites under varying acquisition conditions. Controlled repetitions over seven vines by seven operators with seven smartphone models were performed to further determine the sources of variation and their relative importance. Results: LAI estimations made with VitiCanopy were consistent with the different levels of controlled vine size in the experiment. The operator and the smartphone model had a significant effect on the variance of the estimated LAI. The variance caused by the observation protocol was relatively low compared to the variability between plants within the observation site (seven vines). Conclusions: This study showed that the VitiCanopy application was relevant for ordering or classifying vines according to LAI. In an operational context, the results of this study support the use of this smartphone application for relative measurements. However, the best results were achieved when smartphone model differences were minimised or avoided and with homogeneous acquisition conditions between operators. This last condition will require the organisation of group training sessions to minimise an observed operator effect on measurement variability. Significance and impact of the study: This short communication demonstrated the potential of LAI observations collected with smartphones by several operators for decision-making in a context of farmsourcing. The results showed that this new source of observations, which is inexpensive to collect, made it possible to characterise vine size (LAI) differences in vineyards of southern France. This shows the potential of this app for large production areas such as cooperatives. Further investigations are needed to understand how different training systems may affect the measurement. This source of observations could be complementary to other information sources that are more precise or more accurate, but also more expensive (i.e. destructive methods).