Ke Shao

Biotechnology Research Institute

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Yuntao Ma

China Agricultural University

Qing Wang

China Agricultural University

Shunfu Xiao

China Agricultural University

Ruili Wang

Hainan Medical College Hospital

Baoguo Li

China Agricultural University

Yan Guo

Zhengzhou University

Haochong Chen

Huawei Technologies (China)

Lei Meng

Western Michigan University

Ruili Wang

Southwest University

Yingpu Che

Institute of Crop Sciences

Cooperative Institutions

China Agricultural University

Chinese Academy of Sciences

Zhejiang University

Ministry of Agriculture and Rural Affairs

Tianjin University

University of Chinese Academy of Sciences

Collaborative Innovation Center of Chemical Science and Engineering Tianjin

Chinese Academy of Agricultural Sciences

Biotechnology Research Institute

Northwest A&F University

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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)

A comparison of several types of carbon in ‘de novo’ dioxins formation: effects of time, doping with copper, water vapour, and suppression by sulphur dioxide

Alfons Buekens Ke Shao Xiaodong Li Shengyong Lu Jianhua Yan

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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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Estimating economic benefit of sugar beet based on three-dimensional computer vision: a case study in Inner Mongolia, China

European Journal of Agronomy (2021)

Shunfu Xiao Honghong Chai Qing Wang Ke Shao Lei Meng

Taproot

10.1016/j.eja.2021.126378

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Citations (15)

Influence of Parallel Impeller Inlet Trimming on Mixed Flow Pump as Turbine

Deleted Journal (2020)

Sun-Sheng Yang Ke Shao Tao Dai

Trimming

Specific speed

10.16183/j.cnki.jsjtu.2019.081

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Citations (1)

Toward underlying reasons for rice starches having low viscosity and high amylose: physiochemical and structural characteristics

Journal of the Science of Food and Agriculture (2012)

Changquan Zhang Lijia Zhu Ke Shao Minghong Gu Qiaoquan Liu

Abstract BACKGROUND To understand the reasons for low starch viscosity in rice variety Q11 (Qing‐lu‐zhan 11), the physiochemical and structural characteristics of flours and starches were investigated and compared with another rice cultivar with similar high amylose but normal viscosity. RESULTS Our results showed that residual α‐amylase activity and proteins were not the major causes of low starch viscosity in Q11 rice. Homogeneous small granule size and lower swelling power of high‐amylose Q11 rice starches was one reason for the low swelling volume and thus the low pasting property. Q11 starch paste contained some partially swollen granules, which could increase the fluidity and thus cause the low paste viscosity. The small gelatinization enthalpy might be due to the lower crystallinity in Q11 starches. Moreover, Q11 starches consisted of more amylose with short chains, but also amylopectin with fewer short chains ( DP 11–21) and more long chains ( DP 22–54), which might be other important factors contributing to the low viscosity of Q11 starches. Conclusion These data can add to our understanding of the relationships between low viscosity and physiochemical properties, and will be helpful in elucidating the underlying mechanism of formation of low starch viscosity, as well as applications for low‐viscosity rice starches. © 2012 Society of Chemical Industry

10.1002/jsfa.5987

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Citations (80)

Estimation of sugar content in sugar beet root based on UAV multi-sensor data

Computers and Electronics in Agriculture (2022)

Qing Wang Yingpu Che Ke Shao Zhu Jinyu Ruili Wang

RGB color model

10.1016/j.compag.2022.107433

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Citations (22)

High-throughput calculation of organ-scale traits with reconstructed accurate 3D canopy structures using a UAV RGB camera with an advanced cross-circling oblique route

ISPRS Journal of Photogrammetry and Remote Sensing (2023)

Shunfu Xiao Yulu Ye Shuaipeng Fei Haochong Chen Bingyu Zhang

10.1016/j.isprsjprs.2023.05.016

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Citations (32)

Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field

Remote Sensing (2020)

Shunfu Xiao Honghong Chai Ke Shao Mengyuan Shen Qing Wang

Sugar beet is one of the main crops for sugar production in the world. With the increasing demand for sugar, more desirable sugar beet genotypes need to be cultivated through plant breeding programs. Precise plant phenotyping in the field still remains challenge. In this study, structure from motion (SFM) approach was used to reconstruct a three-dimensional (3D) model for sugar beets from 20 genotypes at three growth stages in the field. An automatic data processing pipeline was developed to process point clouds of sugar beet including preprocessing, coordinates correction, filtering and segmentation of point cloud of individual plant. Phenotypic traits were also automatically extracted regarding plant height, maximum canopy area, convex hull volume, total leaf area and individual leaf length. Total leaf area and convex hull volume were adopted to explore the relationship with biomass. The results showed that high correlations between measured and estimated values with R2 > 0.8. Statistical analyses between biomass and extracted traits proved that both convex hull volume and total leaf area can predict biomass well. The proposed pipeline can estimate sugar beet traits precisely in the field and provide a basis for sugar beet breeding.

10.3390/rs12020269

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Citations (38)