One strategy to optimise spraying is to adjust the spray rate according to local vegetative characteristics. Mobile 2D LiDAR proximal sensor data and local measurements of deposition rates from a side-by-side sprayer were made across eleven narrow inter-row fields in three French vineyards at three dates in 2021. Primary canopy attributes (height, width and porosity) and an integrated indicator of canopy geometry (leaf wall area) were calculated from the LiDAR data using a Bayesian Point Cloud Classification algorithm. Multivariate models to predict the deposition distribution, as deciles, using the primary canopy attributes were constructed and calibrated using data from five of the fields and validated against the data from the six other fields. The models are based on log-lin regression for data acquired at three growth stages covering the whole season. The prediction quality and uncertainty of these multivariate statistical models at different growth stages were evaluated by comparison with previously proposed univariate deposition models based on integrated indicators at the same growth stages. The results showed that multivariate models can predict the distribution of deposits from a typical face-to-face sprayer more accurately (0.79 < R² < 0.91), and robustly (11.2% < nRMSEp < 25.3%) than univariate prediction models based on integrated indicators. These predictive multivariate models could enable variable-rate sprayers to adjust the spray rate according to local vegetative characteristics in an automated way. This work is an extension of previous work made on vineyards with wider inter-rows.