Leaf area estimation of strawberry plants using commercial low-cost LiDAR

Phenotyping technology still lacks implementation in commercial production of fruit and vegetables so far, possibly due to high investment costs for the sensors. In the present study, two commercial low-cost LiDAR systems (Intel^® RealSense™ LiDAR Camera L515, USA and Nimbus 3D ToF camera, Pieye GmbH, Germany) were applied for estimating the leaf area of strawberry plants. Geometric accuracy of sensors was tested with reference white spheres of known diameter located in a growth chamber at different height and position. Six strawberry plants were measured with both systems on three measuring dates. 3D point clouds obtained with the low-cost sensors were analysed at different grid size (5×5, 10×10, 18×15 cm), where maximum plant height and leaf area were calculated. Results were compared to manually measured plant properties and state-of-the-art LiDAR (SICK LMS 511 Pro, Germany). For plant height analysis, R²=0.74 and R²=0.53 were obtained by RealSense™ and Nimbus, respectively. Both the sensors performed rather poorly in estimating leaf area of strawberry plants with R²<0.17. In comparison, analysis with SICK LMS 511 resulted in R²=0.95 and R²=0.77 for plant height and leaf area estimation, respectively. Consequently, simple plant growth monitoring can be approached with low-cost sensors, whereas phenotyping questions request enhanced geometric data quality.