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

Naimin Xu, Guoxiang Sun (), Yuhao Bai, Xinzhu Zhou, Jiaqi Cai and Yinfeng Huang
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Naimin Xu: College of Engineering, Nanjing Agricultural University, Nanjing 210095, China
Guoxiang Sun: College of Engineering, Nanjing Agricultural University, Nanjing 210095, China
Yuhao Bai: College of Engineering, Nanjing Agricultural University, Nanjing 210095, China
Xinzhu Zhou: College of Engineering, Nanjing Agricultural University, Nanjing 210095, China
Jiaqi Cai: College of Engineering, Nanjing Agricultural University, Nanjing 210095, China
Yinfeng Huang: College of Engineering, Nanjing Agricultural University, Nanjing 210095, China

Agriculture, 2023, vol. 13, issue 2, 1-15

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 R 2 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.

Keywords: Kinect; crop phenotypic; point cloud processing; three-dimensional reconstruction; singular value decomposition (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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