| 基于超分辨率重建和多模态数据融合的玉米表型性状监测 |
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| 引用本文： | 车荧璞,王庆,李世林,李保国,马韫韬.基于超分辨率重建和多模态数据融合的玉米表型性状监测[J].农业工程学报,2021,37(20):169-178. |
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| 作者姓名： | 车荧璞 王庆 李世林 李保国 马韫韬 |
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| 作者单位： | 中国农业大学土地科学与技术学院，北京 100193 |
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| 基金项目： | 内蒙古科技重大专项和成果转化项目（2019ZD024，2019CG093，2020GG0038） |
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| 摘 要： | 无人机遥感技术已逐渐成为获取作物表型参数的重要工具，如何在不降低测量精度的同时提高空间分辨率和测量通量受到表型研究人员的重视。该研究以玉米为研究对象，获取5个生育期无人机图像序列，结合小波变换与双三次插值对数码影像进行超分辨率重建，提取原始影像和重建影像的冠层结构、光谱等参数。基于单一参数和多模态数据构建地上生物量估算模型。结果表明：重建影像质量较高、失真较小，其峰值信噪比为21.5，结构相似性为0.81。航高60 m的重建影像地面采样距离与30 m的原始影像相近，但每分钟可多获取0.2 hm2地块的图像。多模态数据融合在一定程度上克服冠层饱和问题，相对于单一参数获得更高的生物量估测精度，拟合的决定系数为0.83，单一参数拟合的决定系数为0.095～0.750。在采用更高飞行高度条件下，结合超分辨率重建和多模态数据融合估算生物量的精度没有降低、反而略有提高，满足更高测量通量的需求，为解码基因型与表型关联的策略提供依据。
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| 关 键 词： | 无人机 遥感 地上生物量 玉米 超分辨率重建 多模态数据融合 |
| 收稿时间： | 2021/4/5 0:00:00 |
| 修稿时间： | 2021/6/10 0:00:00 |
Monitoring of maize phenotypic traits using super-resolution reconstruction and multimodal data fusion |
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| Che Yingpu,Wang Qing,Li Shilin,Li Baoguo,Ma Yuntao.Monitoring of maize phenotypic traits using super-resolution reconstruction and multimodal data fusion[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(20):169-178. |
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| Authors: | Che Yingpu Wang Qing Li Shilin Li Baoguo Ma Yuntao |
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| Institution: | College of Land Science and Technology, China Agricultural University, Beijing 100193, China |
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| Abstract: | Abstract: High-throughput phenotyping has posed an urgent challenge on plant genetics, physiology, and breeding at present. Particularly, traditional manual cannot meet the needs of high-throughput phenotyping for breeding, due mainly to time-consuming and labour-intensive work with a limited sample size. Alternatively, the Unmanned Aerial Vehicle (UAV) Remote Sensing can be widely expected to serve as an important tool for crop phenotypic parameters. The main reason can be the high temporal and spatial resolution, fast image acquisition, easy operation and portability, as well as relatively low cost. However, it is also inevitable to balance the flight height and image resolution or accuracy during image acquisition. Efficient techniques are urgently needed to reconstruct the high-resolution images without lossing the measurement accuracy, while improving the spatial resolution and image acquisition. In this study, the maize phenotypic traits were effectively monitored using super-resolution reconstruction and multimodal data fusion. The UAV image sequences of maize were also captured at seedling, 6th leaf, 12th leaf, tasseling, and milk stage. The super-resolution images were then reconstructed combined with the wavelet transform and bicubic interpolation. The reconstructed images presented higher reconstruction quality, less distortion with peak signal-to-noise ratio of 21.5, structure similarity of 0.81, and mean absolute error ratio of 6.4%. A lower error was also achieved for the plant height and biomass estimation with the root mean square error of 0.39 cm and 0.19 kg, respectively. Ground Sampling Distance (GSD) of the reconstructed image at a flight height of 60 m was similar to that of the original image at a flight height of 30 m. Subsequently, the UAV at a flight height of 60 m was utilized to scan 0.2 hm2 larger fields per minute than that at a flight height of 30 m. The plant height, canopy coverage and vegetation index were also extracted from the original and reconstructed images. Leaf area index was calculated by point cloud reconstructed by oblique photography. The original shape of point cloud was remained, while point cloud was compressed for a higher efficiency using 3-D voxel filtering. Specifically, a better correlation was achieved, where the measured LAI was the slope of 0.72 and the root mean square error of 0.14. All canopy structure, spectrum and population structure parameters were then used to construct estimation models of above ground biomass using single characteristic parameter and multimodal data. A higher estimation accuracy of above ground biomass was obtained by multimodal data fusion, compared with a single parameter with the coefficient of determination was 0.83 and root mean square error of 0.19 kg. Therefore, a combination of image super-resolution reconstruction and multimodal data fusion can be widely expected to deal with the canopy saturation for higher spatial resolution and estimation accuracy, indicating fully meeting the demand for higher throughput of data acquisition. Meanwhile, the finding can provide a highly effective and novel solution to the estimation of above ground biomass. More importantly, the correlation between genotype and phenotype can also be extended to cultivate high-quality maize varieties suitable for mechanized production. |
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| Keywords: | UAV remote sensing above ground biomass maize super-resolution reconstruction multimodal data fusion |
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