Shunfu Xiao

China Agricultural University

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i-10 index

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Yuntao Ma

China Agricultural University

Kaiyi Bi

Aerospace Information Research Institute

Shuaipeng Fei

Chinese Academy of Agricultural Sciences

Haochong Chen

Huawei Technologies (China)

Zheng Niu

State Key Laboratory of Remote Sensing Science

Ke Shao

Biotechnology Research Institute

Qing Wang

China Agricultural University

Long Yan

Academy of Agricultural and Forestry Sciences

Yun Xu

China Agricultural University

Shuai Gao

Aerospace Information Research Institute

Cooperative Institutions

China Agricultural University

Chinese Academy of Sciences

Chinese Academy of Agricultural Sciences

University of Chinese Academy of Sciences

Ministry of Agriculture and Rural Affairs

State Key Laboratory of Remote Sensing Science

Aerospace Information Research Institute

Ministry of Natural Resources

Beijing Normal University

Northwest A&F University

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Non-Destructive Monitoring of Maize Nitrogen Concentration Using a Hyperspectral LiDAR: An Evaluation from Leaf-Level to Plant-Level

Remote Sensing (2021)

Kaiyi Bi Zheng Niu Shunfu Xiao Jie Bai Gang Sun

Advanced remote sensing techniques for estimating crop nitrogen (N) are crucial for optimizing N fertilizer management. Hyperspectral LiDAR (HSL) data, with both spectral and spatial information of the targets, can extract more plant properties than traditional LiDAR and hyperspectral imaging systems. In this study, we tested the ability of HSL in terms of estimating maize N concentration at the leaf-level by using spectral indices and partial least squares regression (PLSR) methods. Subsequently, the N estimation was scaled up to the plant-level based on HSL point clouds. Biomass, extracted with structural proxies, was utilized to exhibit its supplemental effect on N concentration. The results show that HSL has the ability to extract N concentrations at both the leaf-level and the canopy-level, and PLSR showed better performance (R2 > 0.6) than the single spectral index (R2 > 0.4). In comparison to the stem height and maximum canopy width, the plant height had the strongest ability (R2 = 0.88) to estimate biomass. Future research should utilize larger datasets to test the viability of using HSL to monitor the N concentration of crops, which is beneficial for precision agriculture.

Precision Agriculture

Plant canopy

10.3390/rs13245025

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Citations (4)

Bridging the gap between hyperspectral imaging and crop breeding: soybean yield prediction and lodging classification with prototype contrastive learning

Computers and Electronics in Agriculture (2025)

Guangyao Sun Zhang Yong Lei Wang Longyu Zhou Shuaipeng Fei

10.1016/j.compag.2024.109859

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Quantitative analysis and planting optimization of multi-genotype sugar beet plant types based on 3D plant architecture

Computers and Electronics in Agriculture (2024)

Haochong Chen M. Zhang Shunfu Xiao Qing Wang Zhibo Cai

10.1016/j.compag.2024.109231

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Citations (1)

Bridging the Gap between Hyperspectral Imaging and Crop Breeding: Soybean Yield Prediction and Lodging Classification with Prototype Contrastive Learning

Guangyao Sun Zhang Yong Lei Wang Longyu Zhou Shuaipeng Fei

10.2139/ssrn.5016935

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Estimating Vertical Chlorophyll Concentrations in Maize in Different Health States Using Hyperspectral LiDAR

IEEE Transactions on Geoscience and Remote Sensing (2020)

Kaiyi Bi Shunfu Xiao Shuai Gao Changsai Zhang Ni Huang

The detection of vertical heterogeneity in vegetation has attracted an increasing attention as it has a great significance for precise agriculture. The hyperspectral light detection and ranging (LiDAR) (HSL) can obtain the spectral and spatial information simultaneously. However, its ability to monitor the vertical distribution of biochemical parameters in plants has not been fully explored. In this article, the applicability of empirical ratio and normalized spectral indices for HSL channels in chlorophyll (Chl) detection was investigated using three data sets: the PROSPECT-5 synthetic data set, the ANGERS public data set, and an HSL-measured data set. A linear regression model of the best performing index against measured Chl values was constructed so as to build 3-D Chl point clouds of maize. The performance of HSL in Chl detection at the upper and lower layers was also tested based on the selected spectral index. The result showed that the CIred edge index was most compatible with the HSL channels. The estimated Chl concentrations of the upper and lower layers showed the close relationships with HSL measurements (R ² = 0.73 and 0.91, respectively). The vertical Chl profiles in maize were also presented, indicating that the HSL system has a strong ability to monitor the vertical distribution of maize Chl concentrations. This article provides a basis for the vertical detection of vegetation biochemical parameters directly from HSL measurements.

Data set

Red edge

10.1109/tgrs.2020.2987436

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Citations (30)

Simultaneous Extraction of Plant 3-D Biochemical and Structural Parameters Using Hyperspectral LiDAR

IEEE Geoscience and Remote Sensing Letters (2020)

Kaiyi Bi Zheng Niu Shuai Gao Shunfu Xiao Jie Pei

Hyperspectral light detection and ranging (LiDAR) (HSL) can be used to acquire backscattered full-waveform data with abundant spectral information, providing a new technique for the remote sensing of plant 3-D properties monitoring. HSL 3-D point cloud of two Kniphofia uvaria plants was acquired by HSL scanning and data processing. Then, chlorophyll concentration could be retrieved at any 3-D position using a constructed partial least squares regression (PLSR) model. At the same time, the vegetation canopy structural parameters (canopy height, maximum canopy width, and projected canopy area) were retrieved based on the geometric information of the HSL data. The results of this study showed that the HSL system could be used to realize the simultaneous extraction of both the 3-D biochemical and structural parameters, which has great potential in the field of quantitative remote sensing.

Ranging

Laser Scanning

10.1109/lgrs.2020.3025321

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Citations (10)

Enhancing 3d Spectral Mapping of Trees Using Corrected Intensity of Hyperspectral Lidar

Kaiyi Bi Shunfu Xiao Zheng Niu Jianying Liu Yishuo Hao

Intensity

10.2139/ssrn.4910142

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Improved random patches and model transfer for deriving leaf mass per area across multispecies from spectral reflectance

Computers and Electronics in Agriculture (2024)

Shuaipeng Fei Shunfu Xiao Demin Xu Meiyan Shu Hong Sun

10.1016/j.compag.2024.108745

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Segmentation of Individual Leaves of Field Grown Sugar Beet Plant Based on 3D Point Cloud

Agronomy (2022)

Yunling Liu Guoli Zhang Ke Shao Shunfu Xiao Qing Wang

Accurate segmentation of individual leaves of sugar beet plants is of great significance for obtaining the leaf-related phenotypic data. This paper developed a method to segment the point clouds of sugar beet plants to obtain high-quality segmentation results of individual leaves. Firstly, we used the SFM algorithm to reconstruct the 3D point clouds from multi-view 2D images and obtained the sugar beet plant point clouds after preprocessing. We then segmented them using the multiscale tensor voting method (MSTVM)-based region-growing algorithm, resulting in independent leaves and overlapping leaves. Finally, we used the surface boundary filter (SBF) method to segment overlapping leaves and obtained all leaves of the whole plant. Segmentation results of plants with different complexities of leaf arrangement were evaluated using the manually segmented leaf point clouds as benchmarks. Our results suggested that the proposed method can effectively segment the 3D point cloud of individual leaves for field grown sugar beet plants. The leaf length and leaf area of the segmented leaf point clouds were calculated and compared with observations. The calculated leaf length and leaf area were highly correlated with the observations with R2 (0.80–0.82). It was concluded that the MSTVM-based region-growing algorithm combined with SBF can be used as a basic segmentation step for high-throughput plant phenotypic data extraction of field sugar beet plants.

10.3390/agronomy12040893

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Citations (4)

Investigating the 3D distribution of Cercospora leaf spot disease in sugar beet through fusion methods

Computers and Electronics in Agriculture (2024)

Shunfu Xiao Haochong Chen Yaguang Hou Ke Shao Kaiyi Bi

Cercospora

10.1016/j.compag.2024.109107

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Citations (1)