IPCISS: an RGB Image and Point Cloud Integrated Scanning System for Crop Phenotyping

Precise acquisition of crop phenotypes requires integrating three-dimensional (3D) point clouds with two-dimensional (2D) imagery. However, current systems that capture both point clouds and images simultaneously are typically expensive, limiting phenotypic analysis. To address this challenge, we developed and open-sourced a low-cost RGB image and point cloud integrated scanning system (IPCISS). IPCISS provides a hardware platform for spatio-temporal sensor registration, streamlining the fusion of RGB images and point clouds. Furthermore, an indirect cross-modal extrinsic calibration method based on laser spots was proposed to enable precise spatial registration between an RGB camera and a single-point laser range finder (LRF). The calibrated system enabled accurate 3D scanning and phenotypic analysis of wheat. The 3D scanning performance of IPCISS was evaluated in an indoor environment using a color‑textured calibration box with known geometry. Field experiments were conducted to compare the performance of IPCISS and a terrestrial laser scanner (FARO) in estimating wheat canopy height and leaf area index. Results showed that IPCISS achieved high measurement accuracy and stability. At one-tenth the cost of FARO, IPCISS provided comparable geometric performance, with a ranging accuracy of 0.007 m and a precision of 0.009 m. With the proposed integrated scanning, fusion, and analysis workflow for crop phenotyping, IPCISS enabled the simultaneous estimation of wheat structural and physiological traits. IPCISS achieved centimeter-level accuracy in wheat canopy height estimation (R² = 0.972), comparable to that of FARO (R² = 0.992). By combining structural features with vegetation indices, both IPCISS (R² = 0.922) and FARO (R² = 0.918) achieved improved accuracy in LAI estimation. The low-cost system enables stable 3D scanning and reliable crop phenotyping. This open-source solution provides a reliable and practical tool for building 3D crop datasets, sharing data, and performing integrated phenotypic analysis.