From Selfies to Science – Precise 3D Leaf Measurement with iPhone 13 and Its Implications for Plant Development and Transpiration

Abstract

Advanced smartphone technology has unified sophisticated, cost-effective sensors, broadening access to high-precision data acquisition. This study aimed to validate the hypothesis that the iPhone 13-Pro camera, in conjunction with its LiDAR technology, can accurately extract information such as the surface area of maize leaves (Zea mays). 3D point cloud models enabled remote, non-destructive trait data collection, and four methods for 3D canopy area extraction were compared to assess their relation with wole-plant transpiration rates. The experimental findings demonstrated a robust correlation (R^2=0.88, RMSE=62.45) between manually scanned surface area and the iPhone 3-dimentional estimated plant surface area. Furthermore, the study revealed that the ratio of stem to total plant surface area is 11.6% (R^2=0.91, RMSE=30.20). Utilizing this ratio for predicting canopy surface area from total plant surface area resulted in a significant correlation (R^2=0.89) with the measured canopy. The mobile iPhone's surface area measurement tool offers a significant advantage by providing the capability to scan the entire plant surface area. This contrasts with the projected leaf area index measurements taken by most commercial top canopy scanners, which are unable to penetrate the canopy like manual measurements do. An additional advantage of the real size surface measurement of the whole canopy is its high correlation (R^2=0.83) with the whole canopy transpiration rate, as was measured using a gravimetric system on the same scanned plants. This study presents a novel method for analyzing 3D plant traits with a portable, accurate, and affordable tool, enhancing selection processes for plant breeders and advancing agricultural practices.