Precision agriculture (PA) relies on data collection and analysis to understand spatial variability in agricultural crops, using technologies such as sensors, GNSS, remotely piloted aircraft (RPAs), and management software. This study aimed to evaluate the potential of a LiDAR sensor coupled to an RPA to map damage in coffee plants after frosts and fill scientific gaps. To this end, the experiment was conducted in Santo Antônio do Amparo, Brazil, in May 2023, obtaining data on height and canopy diameter of three coffee plantations, aged 3, 4, and 10 years, affected by frosts in different years (2019 and 2021). Multivariate statistical analyses were performed using non-hierarchical clustering tools to classify the coffee plants into two groups according to frost incidence. Two new classifications were proposed for the two groups, based on k-means non-hierarchical cluster analysis. Thus, the study showed significant differences in the variables, with a p-value of <2.2 × 10⁻¹⁶ for the proposed groupings 1 and 2. Therefore, the groups are statistically different.