Research Article | Open Access

Volume 2024 |Article ID 0265 | https://doi.org/10.34133/plantphenomics.0265

Phenotyping of Panicle Number and Shape in Rice Breeding Materials Based on Unmanned Aerial Vehicle Imagery

Xuqi Lu,^1,2 Yutao Shen,^1,2 Jiayang Xie,^1,2 Lu et al. 2024 | https://doi.org/10.34133/plantphenomics.0265 1RESEARCH ARTICLEPhenotyping of Panicle Number and Shape inRice Breeding Materials Based on UnmannedAerial Vehicle ImageryXuqi Lu1,2, Yutao Shen 1,2, Jiayang Xie 1,2,,^1,2 Xin Yang,^1,2 Qingyao Shu,^3,4 Song Chen,⁵ Zhihui Shen,⁶ Haiyan Cen ^1,2

¹College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
²Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058,China
³The Advanced Seed Institute, Zhejiang University, Hangzhou 310058, China
⁴National KeyLaboratory of Rice Biology and Breeding, Zhejiang University, Hangzhou 310058, China
⁵ChinaNational Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310006,China
⁶Zhejiang Dahua Technology Co. Ltd., Hangzhou 310056, China

Abstract

The number of panicles per unit area (PNpA) is one of the key factors contributing to the grain yield of rice crops. Accurate PNpA quantification is vital for breeding high-yield rice cultivars. Previous studies were based on proximal sensing with fixed observation platforms or unmanned aerial vehicles (UAVs). The near-canopy images produced in these studies suffer from inefficiency and complex image processing pipelines that require manual image cropping and annotation. This study aims to develop an automated, high-throughput UAV imagery-based approach for field plot segmentation and panicle number quantification, along with a novel classification method for different panicle types, enhancing PNpA quantification at the plot level. RGB images of the rice canopy were efficiently captured at an altitude of 15 m, followed by image stitching and plot boundary recognition via a mask region-based convolutional neural network (Mask R-CNN). The images were then segmented into plot-scale subgraphs, which were categorized into 3 growth stages. The panicle vision transformer (Panicle-ViT), which integrates a multipath vision transformer and replaces the Mask R-CNN backbone, accurately detects panicles. Additionally, the Res2Net50 architecture classified panicle types with 4 angles of 0°, 15°, 45°, and 90°. The results confirm that the performance of Plot-Seg is comparable to that of manual segmentation. Panicle-ViT outperforms the traditional Mask R-CNN across all the datasets, with the average precision at 50% intersection over union (AP50) improved by 3.5% to 20.5%. The PNpA quantification for the full dataset achieved superior performance, with a coefficient of determination (R2) of 0.73 and a root mean square error (RMSE) of 28.3, and the overall panicle classification accuracy reached 94.8%. The proposed approach enhances operational efficiency and automates the process from plot cropping to PNpA prediction, which is promising for accelerating the selection of desired traits in rice breeding.