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  1. Article ; Online: What can aerial phenotyping do and bring to us (breeders)?

    Yang, Wanneng / Zhai, Ruifang

    The New phytologist

    2022  Volume 236, Issue 4, Page(s) 1229–1231

    MeSH term(s) Plant Breeding ; Crops, Agricultural
    Language English
    Publishing date 2022-08-13
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Comment
    ZDB-ID 208885-x
    ISSN 1469-8137 ; 0028-646X
    ISSN (online) 1469-8137
    ISSN 0028-646X
    DOI 10.1111/nph.18413
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: An Overview of High-Throughput Crop Phenotyping: Platform, Image Analysis, Data Mining, and Data Management.

    Yang, Wanneng / Feng, Hui / Hu, Xiao / Song, Jingyan / Guo, Jing / Lu, Bingjie

    Methods in molecular biology (Clifton, N.J.)

    2024  Volume 2787, Page(s) 3–38

    Abstract: In this chapter, we explore the application of high-throughput crop phenotyping facilities for phenotype data acquisition and the extraction of significant information from the collected data through image processing and data mining methods. Additionally, ...

    Abstract In this chapter, we explore the application of high-throughput crop phenotyping facilities for phenotype data acquisition and the extraction of significant information from the collected data through image processing and data mining methods. Additionally, the construction and outlook of crop phenotype databases are introduced and the need for global cooperation and data sharing is emphasized. High-throughput crop phenotyping significantly improves accuracy and efficiency compared to traditional measurements, making significant contributions to overcoming bottlenecks in the phenotyping field and advancing crop genetics.
    MeSH term(s) Crops, Agricultural/genetics ; Crops, Agricultural/growth & development ; Data Mining/methods ; Phenotype ; Image Processing, Computer-Assisted/methods ; Data Management/methods ; High-Throughput Screening Assays/methods
    Language English
    Publishing date 2024-04-24
    Publishing country United States
    Document type Journal Article ; Review
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-0716-3778-4_1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Editorial: Convolutional neural networks and deep learning for crop improvement and production.

    Yang, Wanneng / Egea, Gregorio / Ghamkhar, Kioumars

    Frontiers in plant science

    2022  Volume 13, Page(s) 1079148

    Language English
    Publishing date 2022-11-18
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2613694-6
    ISSN 1664-462X
    ISSN 1664-462X
    DOI 10.3389/fpls.2022.1079148
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Research Status and Prospect on Height Estimation of Field Crop Using Near-Field Remote Sensing Technology

    ZHANG Jian / XIE Tianjin / YANG Wanneng / ZHOU Guangsheng

    智慧农业, Vol 3, Iss 1, Pp 1-

    2021  Volume 15

    Abstract: Plant height is a key indicator to dynamically measure crop health and overall growth status, which is widely used to estimate the biological yield and final grain yield of crops. The traditional manual measurement method is subjective, inefficient, and ... ...

    Abstract Plant height is a key indicator to dynamically measure crop health and overall growth status, which is widely used to estimate the biological yield and final grain yield of crops. The traditional manual measurement method is subjective, inefficient, and time-consuming. And the plant height obtained by sampling cannot evaluate the height of the whole field. In the last decade, remote sensing technology has developed rapidly in agriculture, which makes it possible to collect crop height information with high accuracy, high frequency, and high efficiency. This paper firstly reviewed the literature on obtaining plant height by using remote sensing technology for understanding the research progress of height estimation in the field. Unmanned aerial vehicle (UAV) platform with visible-light camera and light detection and ranging (LiDAR) were the most frequently used methods. And main research crops included wheat, corn, rice, and other staple food crops. Moreover, crop height measurement was mainly based on near-field remote sensing platforms such as ground, UAV, and airborne. Secondly, the basic principles, advantages, and limitations of different platforms and sensors for obtaining plant height were analyzed. The altimetry process and the key techniques of LiDAR and visible-light camera were discussed emphatically, which included extraction of crop canopy and soil elevation information, and feature matching of the imaging method. Then, the applications using plant height data, including the inversion of biomass, lodging identification, yield prediction, and breeding of crops were summarized. However, the commonly used empirical model has some problems such large measured data, unclear physical significance, and poor universality. Finally, the problems and challenges of near-field remote sensing technology in plant height acquisition were proposed. Selecting appropriate data to meet the needs of cost and accuracy, improving the measurement accuracy, and matching the plant height estimation of remote sensing with the ...
    Keywords plant height ; near-field remote sensing ; crop ; unmanned aerial vehicle ; visible-light camera ; lidar ; Agriculture (General) ; S1-972 ; Technology (General) ; T1-995
    Subject code 333
    Language English
    Publishing date 2021-03-01T00:00:00Z
    Publisher Editorial Office of Smart Agriculture
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article: Editorial: State-of-the-art technology and applications in crop phenomics, volume II.

    Yang, Wanneng / Doonan, John H / Guo, Xinyu / Yuan, Xiaohui / Ling, Feng

    Frontiers in plant science

    2023  Volume 14, Page(s) 1195377

    Language English
    Publishing date 2023-05-10
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2613694-6
    ISSN 1664-462X
    ISSN 1664-462X
    DOI 10.3389/fpls.2023.1195377
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: Dynamic detection of three-dimensional crop phenotypes based on a consumer-grade RGB-D camera.

    Song, Peng / Li, Zhengda / Yang, Meng / Shao, Yang / Pu, Zhen / Yang, Wanneng / Zhai, Ruifang

    Frontiers in plant science

    2023  Volume 14, Page(s) 1097725

    Abstract: Introduction: Nondestructive detection of crop phenotypic traits in the field is very important for crop breeding. Ground-based mobile platforms equipped with sensors can efficiently and accurately obtain crop phenotypic traits. In this study, we ... ...

    Abstract Introduction: Nondestructive detection of crop phenotypic traits in the field is very important for crop breeding. Ground-based mobile platforms equipped with sensors can efficiently and accurately obtain crop phenotypic traits. In this study, we propose a dynamic 3D data acquisition method in the field suitable for various crops by using a consumer-grade RGB-D camera installed on a ground-based movable platform, which can collect RGB images as well as depth images of crop canopy sequences dynamically.
    Methods: A scale-invariant feature transform (SIFT) operator was used to detect adjacent date frames acquired by the RGB-D camera to calculate the point cloud alignment coarse matching matrix and the displacement distance of adjacent images. The data frames used for point cloud matching were selected according to the calculated displacement distance. Then, the colored ICP (iterative closest point) algorithm was used to determine the fine matching matrix and generate point clouds of the crop row. The clustering method was applied to segment the point cloud of each plant from the crop row point cloud, and 3D phenotypic traits, including plant height, leaf area and projected area of individual plants, were measured.
    Results and discussion: We compared the effects of LIDAR and image-based 3D reconstruction methods, and experiments were carried out on corn, tobacco, cottons and Bletilla striata in the seedling stage. The results show that the measurements of the plant height (R²= 0.9~0.96, RSME = 0.015~0.023 m), leaf area (R²= 0.8~0.86, RSME = 0.0011~0.0041
    Language English
    Publishing date 2023-01-27
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2613694-6
    ISSN 1664-462X
    ISSN 1664-462X
    DOI 10.3389/fpls.2023.1097725
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: A Novel Method for Filled/Unfilled Grain Classification Based on Structured Light Imaging and Improved PointNet+.

    Huang, Shihao / Lu, Zhihao / Shi, Yuxuan / Dong, Jiale / Hu, Lin / Yang, Wanneng / Huang, Chenglong

    Sensors (Basel, Switzerland)

    2023  Volume 23, Issue 14

    Abstract: China is the largest producer and consumer of rice, and the classification of filled/unfilled rice grains is of great significance for rice breeding and genetic analysis. The traditional method for filled/unfilled rice grain identification was generally ... ...

    Abstract China is the largest producer and consumer of rice, and the classification of filled/unfilled rice grains is of great significance for rice breeding and genetic analysis. The traditional method for filled/unfilled rice grain identification was generally manual, which had the disadvantages of low efficiency, poor repeatability, and low precision. In this study, we have proposed a novel method for filled/unfilled grain classification based on structured light imaging and Improved PointNet++. Firstly, the 3D point cloud data of rice grains were obtained by structured light imaging. And then the specified processing algorithms were developed for the single grain segmentation, and data enhancement with normal vector. Finally, the PointNet++ network was improved by adding an additional Set Abstraction layer and combining the maximum pooling of normal vectors to realize filled/unfilled rice grain point cloud classification. To verify the model performance, the Improved PointNet++ was compared with six machine learning methods, PointNet and PointConv. The results showed that the optimal machine learning model is XGboost, with a classification accuracy of 91.99%, while the classification accuracy of Improved PointNet++ was 98.50% outperforming the PointNet 93.75% and PointConv 92.25%. In conclusion, this study has demonstrated a novel and effective method for filled/unfilled grain recognition.
    Language English
    Publishing date 2023-07-12
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s23146331
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: Intelligent identification on cotton verticillium wilt based on spectral and image feature fusion.

    Lu, Zhihao / Huang, Shihao / Zhang, Xiaojun / Shi, Yuxuan / Yang, Wanneng / Zhu, Longfu / Huang, Chenglong

    Plant methods

    2023  Volume 19, Issue 1, Page(s) 75

    Abstract: Background: Verticillium wilt is the major disease of cotton, which would cause serious yield reduction and economic losses, and the identification of cotton verticillium wilt is of great significance to cotton research. However, the traditional method ... ...

    Abstract Background: Verticillium wilt is the major disease of cotton, which would cause serious yield reduction and economic losses, and the identification of cotton verticillium wilt is of great significance to cotton research. However, the traditional method is still manual, which is subjective, inefficient, and labor-intensive, and therefore, this study has proposed a novel method for cotton verticillium wilt identification based on spectral and image feature fusion. The cotton hyper-spectral images have been collected, while the regions of interest (ROI) have been extracted as samples including 499 healthy leaves and 498 diseased leaves, and the average spectral information and RGB image of each sample were obtained. In spectral feature processing, the preprocessing methods including Savitzky-Golay smoothing (SG), multiplicative scatter correction (MSC), de-trending (DT) and mean normalization (MN) algorithms have been adopted, while the feature band extraction methods have adopted principal component analysis (PCA) and successive projections algorithm (SPA). In RGB image feature processing, the EfficientNet was applied to build classification model and 16 image features have been extracted from the last convolutional layer. And then, the obtained spectral and image features were fused, while the classification model was established by support vector machine (SVM) and back propagation neural network (BPNN). Additionally, the spectral full bands and feature bands were used as comparison for SVM and BPNN classification respectively.
    Result: The results showed that the average accuracy of EfficientNet for cotton verticillium wilt identification was 93.00%. By spectral full bands, SG-MSC-BPNN model obtained the better performance with classification accuracy of 93.78%. By feature bands, SG-MN-SPA-BPNN model obtained the better performance with classification accuracy of 93.78%. By spectral and image fused features, SG-MN-SPA-FF-BPNN model obtained the best performance with classification accuracy of 98.99%.
    Conclusions: The study demonstrated that it was feasible and effective to use fused spectral and image features based on hyper-spectral imaging to improve identification accuracy of cotton verticillium wilt. The study provided theoretical basis and methods for non-destructive and accurate identification of cotton verticillium wilt.
    Language English
    Publishing date 2023-07-29
    Publishing country England
    Document type Journal Article
    ZDB-ID 2203723-8
    ISSN 1746-4811
    ISSN 1746-4811
    DOI 10.1186/s13007-023-01056-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Plant microphenotype: from innovative imaging to computational analysis.

    Zhang, Ying / Gu, Shenghao / Du, Jianjun / Huang, Guanmin / Shi, Jiawei / Lu, Xianju / Wang, Jinglu / Yang, Wanneng / Guo, Xinyu / Zhao, Chunjiang

    Plant biotechnology journal

    2024  Volume 22, Issue 4, Page(s) 802–818

    Abstract: The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of ... ...

    Abstract The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of microphenotyping in plant science over the past two decades. We then point out several challenges in this field and suggest that cross-scale image acquisition strategies, powerful artificial intelligence algorithms, advanced genetic analysis, and computational phenotyping need to be established and performed to better understand interactions among genotype, environment, and management. Microphenotyping has entered the era of Microphenotyping 3.0 and will largely advance functional genomics and plant science.
    MeSH term(s) Artificial Intelligence ; Phenotype ; Genomics/methods ; Genotype ; Plants/genetics
    Language English
    Publishing date 2024-01-13
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 2136367-5
    ISSN 1467-7652 ; 1467-7652
    ISSN (online) 1467-7652
    ISSN 1467-7652
    DOI 10.1111/pbi.14244
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Maturity Classification of Rapeseed Using Hyperspectral Image Combined with Machine Learning.

    Feng, Hui / Chen, Yongqi / Song, Jingyan / Lu, Bingjie / Shu, Caixia / Qiao, Jiajun / Liao, Yitao / Yang, Wanneng

    Plant phenomics (Washington, D.C.)

    2024  Volume 6, Page(s) 139

    Abstract: Oilseed rape is an important oilseed crop planted worldwide. Maturity classification plays a crucial role in enhancing yield and expediting breeding research. Conventional methods of maturity classification are laborious and destructive in nature. In ... ...

    Abstract Oilseed rape is an important oilseed crop planted worldwide. Maturity classification plays a crucial role in enhancing yield and expediting breeding research. Conventional methods of maturity classification are laborious and destructive in nature. In this study, a nondestructive classification model was established on the basis of hyperspectral imaging combined with machine learning algorithms. Initially, hyperspectral images were captured for 3 distinct ripeness stages of rapeseed, and raw spectral data were extracted from the hyperspectral images. The raw spectral data underwent preprocessing using 5 pretreatment methods, namely, Savitzky-Golay, first derivative, second derivative (D2nd), standard normal variate, and detrend, as well as various combinations of these methods. Subsequently, the feature wavelengths were extracted from the processed spectra using competitive adaptive reweighted sampling, successive projection algorithm (SPA), iterative spatial shrinkage of interval variables (IVISSA), and their combination algorithms, respectively. The classification models were constructed using the following algorithms: extreme learning machine,
    Language English
    Publishing date 2024-03-26
    Publishing country United States
    Document type Journal Article
    ISSN 2643-6515
    ISSN (online) 2643-6515
    DOI 10.34133/plantphenomics.0139
    Database MEDical Literature Analysis and Retrieval System OnLINE

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