Global Reconstruction Method of Maize Population at Seedling Stage Based on Kinect Sensor

Abstract

Automatic plant phenotype measurement technology based on the rapid and accurate reconstruction of maize structures at the seedling stage is essential for the early variety selection, cultivation, and scientific management of maize. Manual measurement is time-consuming, laborious, and error-prone. The lack of mobility of large equipment in the field make the high-throughput detection of maize plant phenotypes challenging. Therefore, a global 3D reconstruction algorithm was proposed for the high-throughput detection of maize phenotypic traits. First, a self-propelled mobile platform was used to automatically collect three-dimensional point clouds of maize seedling populations from multiple measurement points and perspectives. Second, the Harris corner detection algorithm and singular value decomposition (SVD) were used for the pre-calibration single measurement point multi-view alignment matrix. Finally, the multi-view registration algorithm and iterative nearest point algorithm (ICP) were used for the global 3D reconstruction of the maize seedling population. The results showed that the R2 of the plant height and maximum width measured by the global 3D reconstruction of the seedling maize population were 0.98 and 0.99 with RMSE of 1.39 cm and 1.45 cm and mean absolute percentage errors (MAPEs) of 1.92% and 2.29%, respectively. For the standard sphere, the percentage of the Hausdorff distance set of reconstruction point clouds less than 0.5 cm was 55.26%, and the percentage was 76.88% for those less than 0.8 cm. The method proposed in this study provides a reference for the global reconstruction and phenotypic measurement of crop populations at the seedling stage, which aids in the early management of maize with precision and intelligence.

Application of Vision Technology and Artificial Intelligence in Smart Farming

“…Sun et al [12] used multi-view stereoscopic technology (MVS) to reconstruct the entire growth period (13 stages) of five different soybean varieties in three dimensions, constructed a 3D dataset named Soybean-MVS with the labels of the entire soybean growth period, and used RandLA-Net and BAAF-Net two point cloud semantic segmentation models to verify its usability, which can provide usable basic data support for the 3D crop model segmentation models. Xu et al [13] proposed a reconstruction algorithm based on 3D information for the detection of maize phenotypic traits, utilizing a multiview registration algorithm and iterative closest point (ICP) algorithm for the global 3D reconstruction of maize seedling populations, which contributes to precise and intelligent early management of maize.…”