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

IF 7.6 1区 农林科学 Q1 AGRONOMY
Plant Phenomics Pub Date : 2024-10-24 eCollection Date: 2024-01-01 DOI:10.34133/plantphenomics.0265
Xuqi Lu, Yutao Shen, Jiayang Xie, Xin Yang, Qingyao Shu, Song Chen, Zhihui Shen, Haiyan Cen
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引用次数: 0

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 (R 2) 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.

基于无人飞行器图像的水稻育种材料圆锥花序数量和形状表型分析
单位面积上的圆锥花序数(PNpA)是影响水稻产量的关键因素之一。准确量化 PNpA 对培育高产水稻品种至关重要。以往的研究基于固定观测平台或无人机(UAV)的近距离传感。这些研究中生成的近冠层图像效率低下,图像处理流程复杂,需要人工进行图像裁剪和标注。本研究旨在开发一种基于无人机图像的自动化、高通量的田块分割和圆锥花序数量定量方法,以及一种针对不同圆锥花序类型的新型分类方法,从而提高地块层面的 PNpA 定量。水稻冠层的 RGB 图像是在 15 米高空有效捕获的,然后通过基于掩膜区域的卷积神经网络(掩膜 R-CNN)进行图像拼接和地块边界识别。然后将图像分割成地块尺度的子图,并将其分为 3 个生长阶段。圆锥花序视觉变换器(Panicle-ViT)集成了多路径视觉变换器,取代了掩膜 R-CNN 骨干网络,可准确检测圆锥花序。此外,Res2Net50 架构还对 0°、15°、45° 和 90° 四种角度的圆锥花序类型进行了分类。结果证实,Plot-Seg 的性能可与人工分割相媲美。在所有数据集上,Panicle-ViT 的表现都优于传统的 Mask R-CNN,50% 交集大于联合(AP50)时的平均精度提高了 3.5% 至 20.5%。全数据集的 PNpA 量化取得了优异的性能,决定系数(R 2)为 0.73,均方根误差(RMSE)为 28.3,整体圆锥花序分类准确率达到 94.8%。所提出的方法提高了操作效率,实现了从小区种植到 PNpA 预测过程的自动化,有望加速水稻育种中理想性状的选择。
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来源期刊
Plant Phenomics
Plant Phenomics Multiple-
CiteScore
8.60
自引率
9.20%
发文量
26
审稿时长
14 weeks
期刊介绍: Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics. The scope of the journal covers the latest technologies in plant phenotyping for data acquisition, data management, data interpretation, modeling, and their practical applications for crop cultivation, plant breeding, forestry, horticulture, ecology, and other plant-related domains.
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