Low-cost and precise phenotyping using 3D point cloud reconstruction to determine plant architecture and morphology

Abstract. Plant morphological features are important factors that define plant production. Leaf area, leaf number, and plant height are important morphological features that relay critical information about how plants respond to environmental conditions such as lighting. Tracking plant morphology permits growers to better quantify the impact of different environmental factors on plant development to optimize growing conditions and increase yield. However, plant morphology tracking can be redundant and time-consuming, as non-destructive assessment must be manually performed. More precise morphological determinations may be conducted post-harvest, but this does not include real-time data on how plants respond to their growing environment. The objective of this study was to develop an apparatus that determines plant architecture and morphological features with a 3D photogrammetry technique using croton (Codiaeum Variegatum Blume cv. Petra), dumb cane (Dieffenbachia araceae cv. Camille), and kale (Brassica oleceara cv. Winterbor). Actual plant morphological measurements, including leaf area(s), leaf number(s), leaf angle, and plant height were compiled and compared to data obtained with a 3D scanning plant model created from the same plant, using 3D photogrammetry technology. Data collected with the developed apparatus indicate that this method shows potential in allowing plant scientists and growers to better assess how environmental factors influence morphological features during plant development, with the possibility of improving crop production.