PhenAI-Bot: precision 3D crop phenotyping of pepper (Capsicum annuum L.) varieties in a greenhouse

Abstract

Manual crop phenotyping is labor-intensive and time-consuming. The lack of affordable, open-access 3D phenotyping tools has hindered the study of dynamic 3D growth in crops across all growth stages. Plant height and leaf growth rates at different stages are critical indicators of plant health and overall yield. This study introduces an autonomous robot, PhenAI-Bot, designed to assess five phenotypic traits: plant height, canopy major and minor diameters, canopy area, and leaf count of four pepper varieties (Black Hungarian, Hungarian Hot Wax, Poblano, and Thai Hot; Capsicum annuum L.) grown in a greenhouse. The PhenAI-Bot uses an Intel RealSense D435i RGB-D camera mounted on an aluminum extrusion to capture top-down RGB, Depth, and 3D point cloud images of plant canopies. A secondary RGB camera scans QR codes on plant pots to autonomously retrieve variety and plant data. Six plants per variety, totaling 24 plants, were analyzed in this study. The collected data was compared with ground truth measurements, with Pearson’s correlation coefficient (R²) ranging from 0.015 for Hungarian Hot Wax to 0.91 for Poblano, and mean squared error (MSE) ranging from 0.004 for Hungarian Hot Wax to 0.097 for Thai Hot. The results show that PhenAI-Bot can accurately measure phenotypic traits at a moving speed of up to 0.2 m/s under standard daytime conditions. Future work will focus on incorporating AI algorithms to improve measurement accuracy and expanding the robot’s application to other crops, including wheat, soybean, corn, and sunflower.