In the Mediterranean area, the potato is a crucial crop and can be cultivated throughout the year. However, environmental and operational issues related to warming, such as mechanical planting settings may negatively effect on potato production and tuber quality, endangering the Mediterranean region's productivity. The research aim was to evaluate how various combinations of planting parameters affected potato yield in the Mediterranean region, integrate UAV-based RGB imaging with ground-level sensors, identify optimal planting combinations, and compare the effectiveness of UAVs versus ground sensors. This study evaluated 24 potato crop plots (Solanum tuberosum L.), comparing 8 different treatment combinations of planting parameters: interplant spacing (cm)*Interrow spacing (cm)*Planting depth (cm). The specific combinations were as follows: 1 (28*90*10), 2 (35*90*10), 3 (28*90*20), 4 (35*90*20), 5 (28*100*10), 6 (35*100*10), 7 (28*100*20), 8 (35*100*20), 9 (control: 32*80*15). Agricultural drone (UAV) provided with image sensor and field cameras were used to measure canopy vegetation and leaf area indexes. The ground and UAV RGB vegetation indices indicated a strong correlative among yield and vegetative indexes. Yield variation from the proximal, aerial, and combined datasets was explained by multivariate regression models in 69.4, 87.9, and 88.4% accordingly. The close resemblance among the proximal and aerial results in this study underscored the output advantages of RGB UAV HTPPs and demonstrates how HTPPs can be used to evaluate the effects of various planting features on optimal planting conditions. Future research should focus on validating these findings across different regions, incorporating advanced sensors, conducting long-term monitoring, performing economic analyses, and applying the methodology to other crops to enhance agricultural productivity and food security.