Cane density is a key management factor in raspberry production, directly affecting yield formation and canopy structure. However, most previous studies have focused on floricane cultivars and relied on conventional field measurements, while the response of primocane raspberries and their canopy level dynamics remain less explored. The objective of this study was to evaluate how cane density influences yield components, cane growth, and canopy structure in primocane raspberry cultivars, and to assess whether these effects can be captured using UAV-based multispectral imaging. Field experiments were conducted over two growing seasons using two primocane cultivars grown under different cane density treatments. Yield components and cane growth parameters were measured, and repeated drone multispectral surveys were performed during the production period to quantify the spatial and temporal variability of vegetation indices. Increasing cane density led to higher total yield per unit area in both cultivars, mainly through an increase in fruit number rather than fruit weight, indicating a compensatory yield response. Cane density significantly modified canopy architecture, with responses varying between cultivars and seasons. Multispectral vegetation indices revealed predominantly consistent density-dependent gradients, characterized by higher mean values and reduced spatial and temporal variability at higher cane densities. Denser cane configurations were associated with lower total temporal amplitude and smoother seasonal trajectories, indicating a stabilization of canopy reflectance dynamics. Although this overall pattern was preserved across indices, the magnitude and regularity of temporal responses were index-specific and cultivar-dependent. The results demonstrate that cane density management in primocane raspberries affects both yield formation and canopy structure, and that these effects can be effectively monitored using UAV-based multispectral imaging. Integrating remote sensing with field measurements offers a valuable approach for supporting data-driven optimization of raspberry production systems.