AI Chat Paper
Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
{{expandStatus?'Exit ':''}}Advanced Search
The field phenotyping platforms that can obtain high-throughput and time-series phenotypes of plant populations at the 3-dimensional level are crucial for plant breeding and management. However, it is difficult to align the point cloud data and extract accurate phenotypic traits of plant populations. In this study, high-throughput, time-series raw data of field maize populations were collected using a field rail-based phenotyping platform with light detection and ranging (LiDAR) and an RGB (red, green, and blue) camera. The orthorectified images and LiDAR point clouds were aligned via the direct linear transformation algorithm. On this basis, time-series point clouds were further registered by the time-series image guidance. The cloth simulation filter algorithm was then used to remove the ground points. Individual plants and plant organs were segmented from maize population by fast displacement and region growth algorithms. The plant heights of 13 maize cultivars obtained using the multi-source fusion data were highly correlated with the manual measurements (R2 = 0.98), and the accuracy was higher than only using one source point cloud data (R2 = 0.93). It demonstrates that multi-source data fusion can effectively improve the accuracy of time series phenotype extraction, and rail-based field phenotyping platforms can be a practical tool for plant growth dynamic observation of phenotypes in individual plant and organ scales.
Watt M, Fiorani F, Usadel B, Rascher U, Muller O, Schurr U. Phenotyping: New windows into the plant for breeders. Annu Rev Plant Biol. 2020, 2020;71:689–712.
Zhao C, Zhang Y, Du J, Guo X, Wen W, Gu S, Wang J, Fan J. Crop phenomics: Current status and perspectives. Front Plant Sci. 2019;10:714.
Pieruschka R, Schurr U. Plant Phenotyping: Past, present, and future. Plant Phenomics. 2019;2019:7507131.
Yang W, Feng H, Zhang X, Zhang J, Doonan JH, Batchelor WD, Xiong L, Yan J. Crop phenomics and high-throughput phenotyping: Past decades, current challenges, and future perspectives. Mol Plant. 2020;13(2):187–214.
Tardieu F, Cabrera-Bosquet L, Pridmore T, Bennett M. Plant phenomics, from sensors to knowledge. Curr Biol. 2017;27(15):R770–R783.
Hamuda E, Glavin M, Jones E. A survey of image processing techniques for plant extraction and segmentation in the field. Comput Electron Agric. 2016;125:184–199.
Jiang Y, Li C. Convolutional neural networks for image-based high-throughput plant phenotyping: A review. Plant Phenomics. 2020;2020:4152816.
Underhill AN, Hirsch CD, Clark MD. Evaluating and mapping grape color using image-based phenotyping. Plant Phenomics. 2020;2020: Article 8086309.
Wang R, Qiu Y, Zhou Y, Liang Z, Schnable JC. A high-throughput phenotyping pipeline for image processing and functional growth curve analysis. Plant Phenomics. 2020;2020: Article 7481687.
Deery DM, Rebetzke GJ, Jimenez-Berni JA, Condon AG, Smith DJ, Bechaz KM, Bovill WD. Ground-based LiDAR improves phenotypic repeatability of above-ground biomass and crop growth rate in wheat. Plant Phenomics. 2020;2020:8329798.
Jin SC, Sun XL, Wu FF, Su YJ, Li YM, Song SL, Xu KX, Ma Q, Baret F, Jiang D, et al. Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects. ISPRS J Photogramm Remote Sens. 2021;171:202–223.
Li D, Li J, Xiang S, Pan A. PSegNet: Simultaneous semantic and instance segmentation for point clouds of plants. Plant Phenomics. 2022;2022: Article 9787643.
Jin S, Su Y, Zhang Y, Song S, Li Q, Liu Z, Ma Q, Ge Y, Liu L, Ding Y, et al. Exploring seasonal and circadian rhythms in structural traits of field maize from LiDAR time series. Plant Phenomics. 2021;2021:9895241.
Liu S, Martre P, Buis S, Abichou M, Andrieu B, Baret F. Estimation of plant and canopy architectural traits using the digital plant phenotyping platform. Plant Physiol. 2019;181(3):881–890.
Shu M, Fei S, Zhang B, Yang X, Guo Y, Li B, Ma Y. Application of UAV multisensor data and ensemble approach for high-throughput estimation of maize phenotyping traits. Plant Phenomics. 2022;2022: Article 9802585.
Caldwell D, Iyer-Pascuzzi AS. A straightforward high-throughput aboveground phenotyping platform for small- to medium-sized plants. Methods Mol Biol. 2022;2539:37–48.
Guo QH, Wu FF, Pang SX, Zhao XQ, Chen LH, Liu J, Xue BL, Xu GC, Li L, Jing HC, et al. Crop 3D—A LiDAR based platform for 3D high-throughput crop phenotyping. Sci China Life Sci. 2018;61(3):328–339.
Song P, Wang J, Guo X, Yang W, Zhao C. High-throughput phenotyping: Breaking through the bottleneck in future crop breeding. Crop J. 2021;9(3):633–645.
Knecht AC, Campbell MT, Caprez A, Swanson DR, Walia H. Image harvest: An open-source platform for high-throughput plant image processing and analysis. J Exp Bot. 2016;67(11):3587–3599.
Lu N, Zhou J, Han Z, Li D, Cao Q, Yao X, Tian Y, Zhu Y, Cao W, Cheng T. Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system. Plant Methods. 2019;15:17.
Jin XL, Liu SY, Baret F, Hemerle M, Comar A. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sens Environ. 2017;198:105–114.
Liu S, Baret F, Abichou M, Boudon F, Thomas S, Zhao K, Fournier C, Andrieu B, Irfan K, Hemmerlé M, et al. Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model. Agric For Meteorol. 2017;247:12–20.
Zhu Q, Wu J, Hu H, Xiao C, Chen W. LIDAR point cloud registration for sensing and reconstruction of unstructured terrain. Appl Sci. 2018;8(11):2318.
Jimenez-Berni JA, Deery DM, Rozas-Larraondo P, Condon ATG, Rebetzke GJ, James RA, Bovill WD, Furbank RT, Sirault XRR. High throughput determination of plant height, ground cover, and above-ground biomass in wheat with LiDAR. Front Plant Sci. 2018;9:237.
Walter JDC, Edwards J, McDonald G, Kuchel H. Estimating biomass and canopy height with LiDAR for field crop breeding. Front Plant Sci. 2019;10:1145.
El Khazari A, Que Y, Sung TL, Lee HJ. Deep global features for point cloud alignment. Sensors. 2020;20(14):4032.
Jin S, Su Y, Gao S, Wu F, Ma Q, Xu K, Ma Q, Hu T, Liu J, Pang S, et al. Separating the structural components of maize for field phenotyping using terrestrial LiDAR data and deep convolutional neural networks. IEEE Trans Geosci Remote Sens. 2020;58(4):2644–2658.
Jin S, Su Y, Wu F, Pang S, Gao S, Hu T, Liu J, Guo Q. Stem–leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data. IEEE Trans Geosci Remote Sens. 2019;57(3):1336–1346.
Yao Z, Zhao Q, Li X, Bi Q. Point cloud registration algorithm based on curvature feature similarity. Measurement. 2021;177.
Zeng L, Wardlow BD, Xiang D, Hu S, Li D. A review of vegetation phenological metrics extraction using time-series, multispectral satellite data. Remote Sens Environ. 2020;237.
Liu H, Wang S, Zhao D. Initial alignment for point cloud registration by improved differential evolution algorithm. Optik. 2021;243.
Quan S, Ma J. Keypoint domain triangular features for fast initial alignment of 3D point clouds. Electron Lett. 2019;55(14):787–789.
Zagar BL, Yurtsever E, Peters A, Knoll AC. Point cloud registration with object-centric alignment. IEEE Access. 2022;10:76586–76595.
Fan J, Li Y, Yu S, Gou W, Guo X, Zhao C. Application of internet of things to agriculture-the LQ-FieldPheno platform: A high-throughput platform for obtaining crop phenotypes in field. Research. 2023;6:0059.
Fan J, Zhang Y, Wen W, Gu S, Lu X, Guo X. The future of Internet of Things in agriculture: Plant high-throughput phenotypic platform. J Clean Prod. 2021;280:123651.
Li Y, Wen W, Guo X, Yu Z, Gu S, Yan H, Zhao C. High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network. PLOS ONE. 2021;16(1).
Seedahmed GH. Direct retrieval of exterior orientation parameters using a 2D projective transformation. Photogramm Rec. 2006;21(115):211–231.
Li F, Zhu H, Luo Z, Shen H, Li L. An adaptive surface interpolation filter using cloth simulation and relief amplitude for airborne laser scanning data. Remote Sens. 2021;13(15).
Shi XJ, Liu T, Han X. Improved Iterative Closest Point (ICP) 3D point cloud registration algorithm based on point cloud filtering and adaptive fireworks for coarse registration. Int J Remote Sens. 2020;41(8):3197–3220.
Jin S, Su Y, Gao S, Wu F, Hu T, Liu J, Li W, Wang D, Chen S, Jiang Y, et al. Deep learning: Individual maize segmentation from terrestrial Lidar data using faster R-CNN and regional growth algorithms. Front Plant Sci. 2018;9:866.
Chene Y, Rousseau D, Lucidarme P, Bertheloot J, Caffier V, Morel P, Belin E, Chapeau-Blondeau F. On the use of depth camera for 3D phenotyping of entire plants. Comput Electron Agric. 2012;82:122–127.
Li Y, Wen W, Miao T, Wu S, Yu Z, Wang X, Guo X, Zhao C. Automatic organ-level point cloud segmentation of maize shoots by integrating high-throughput data acquisition and deep learning. Comput Electron Agric. 2022;193.
Kang Z, Li J, Zhang L, Zhao Q, Zlatanova S. Automatic registration of terrestrial laser scanning point clouds using panoramic reflectance images. Sensors. 2009;9(4):2621–2646.
Sun S, Li C, Paterson AH. In-field high-throughput phenotyping of cotton plant height using LiDAR. Remote Sens. 2017;9(4).
Sun SP, Li CY, Paterson AH, Jiang Y, Xu R, Robertson JS, Snider JL, Chee PW. In-field high throughput phenotyping and cotton plant growth analysis using LiDAR. Front Plant Sci. 2018;9.
Jin XL, Li ZH, Atzberger C. Editorial for the special issue "Estimation of crop phenotyping traits using unmanned ground vehicle and unmanned aerial vehicle imagery". Remote Sens. 2020;12(6).
Distributed under a Creative Commons Attribution License (CC BY 4.0).
京公网安备11010802044758号