Stem–Leaf Segmentation and Phenotypic Trait Extraction of Individual Maize Using Terrestrial LiDAR Data

Accurate and high throughput extraction of crop phenotypic traits, as a crucial step of molecular breeding, is of great importance for yield increasing. However, automatic stem–leaf segmentation as a prerequisite of many precise phenotypic trait extractions is still a big challenge. Current works focus on the study of the 2-D image-based segmentation, which are sensitive to illumination and occlusion. Light detection and ranging (LiDAR) can obtain accurate 3-D information with its active laser scanning and strong penetration ability, which breaks through phenotyping from 2-D to 3-D. However, few researches have addressed the problem of the LiDAR-based stem–leaf segmentation. In this paper, we proposed a median normalized-vector growth (MNVG) algorithm, which can segment stem and leaf with four steps, i.e., preprocessing, stem growth, leaf growth, and postprocessing. The MNVG method was tested by 30 maize samples with different heights, compactness, leaf numbers, and densities from three growing stages. Moreover, phenotypic traits at leaf, stem, and individual levels were extracted with the truly segmented instances. The mean accuracy of segmentation at point level in terms of the recall, precision, F-score, and overall accuracy were 0.92, 0.93, 0.92, and 0.93, respectively. The accuracy of phenotypic trait extraction in leaf, stem, and individual levels ranged from 0.81 to 0.95, 0.64 to 0.97, and 0.96 to 1, respectively. To our knowledge, this paper proposed the first LiDAR-based stem–leaf segmentation and phenotypic trait extraction method in agriculture field, which may contribute to the study of LiDAR-based plant phonemics and precise agriculture.

[1]  Benmouiza Khalil,et al.  Density-based spatial clustering of application with noise algorithm for the classification of solar radiation time series , 2016, 2016 8th International Conference on Modelling, Identification and Control (ICMIC).

[2]  C. Klukas,et al.  Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis[W][OPEN] , 2014, Plant Cell.

[3]  Felix B. Fritschi,et al.  Ground‐Based Digital Imaging as a Tool to Assess Soybean Growth and Yield , 2014 .

[4]  Robert Finger,et al.  Food security: Close crop yield gap , 2011, Nature.

[5]  Jitendra Kumar,et al.  Phenomics in Crop Plants: Trends, Options and Limitations , 2015, Springer India.

[6]  W. Cohen,et al.  Lidar Remote Sensing for Ecosystem Studies , 2002 .

[7]  P. Benfey,et al.  Imaging and Analysis Platform for Automatic Phenotyping and Trait Ranking of Plant Root Systems1[W][OA] , 2010, Plant Physiology.

[8]  Éric Gaussier,et al.  A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation , 2005, ECIR.

[9]  Q. Guo,et al.  A geometric method for wood-leaf separation using terrestrial and simulated Lidar data , 2015 .

[10]  Ian Stavness,et al.  Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks , 2017, Front. Plant Sci..

[11]  N. Ramankutty,et al.  Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000 , 2008 .

[12]  David Suter,et al.  3D terrestrial LIDAR classifications with super-voxels and multi-scale Conditional Random Fields , 2009, Comput. Aided Des..

[13]  Q. Guo,et al.  Crop 3D—a LiDAR based platform for 3D high-throughput crop phenotyping , 2018, Science China Life Sciences.

[14]  Pedro J. Navarro,et al.  Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms , 2017, GigaScience.

[15]  L. Xiong,et al.  Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice , 2014, Nature Communications.

[16]  Ulrich Schurr,et al.  Future scenarios for plant phenotyping. , 2013, Annual review of plant biology.

[17]  R. MacCurdy,et al.  Three-Dimensional Root Phenotyping with a Novel Imaging and Software Platform1[C][W][OA] , 2011, Plant Physiology.

[18]  Léon Bottou,et al.  Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.

[19]  Qinghua Guo,et al.  Fine-resolution forest tree height estimation across the Sierra Nevada through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery , 2017, Int. J. Digit. Earth.

[20]  Ahmad Kamal Aijazi,et al.  Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation , 2013, Remote. Sens..

[21]  Daniel Cohen-Or,et al.  L1-medial skeleton of point cloud , 2013, ACM Trans. Graph..

[22]  Shengli Tao,et al.  Perspectives and prospects of LiDAR in forest ecosystem monitoring and modeling , 2014 .

[23]  M. Lefsky.  A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System , 2010 .

[24]  Thomas Speck,et al.  Plant Stems: Functional Design and Mechanics , 2011 .

[25]  Yi Lin,et al.  LiDAR: An important tool for next-generation phenotyping technology of high potential for plant phenomics? , 2015, Comput. Electron. Agric..

[26]  Yike Liu,et al.  Noise reduction by vector median filtering , 2013 .

[27]  D. Ehlert,et al.  Suitability of a laser rangefinder to characterize winter wheat , 2010, Precision Agriculture.

[28]  M. Ikeda,et al.  Analysis of rice panicle traits and detection of QTLs using an image analyzing method , 2010 .

[29]  Shang Gao,et al.  Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms , 2018, Front. Plant Sci..

[30]  Kerry Cawse-Nicholson,et al.  3D Tree Reconstruction from Simulated Small Footprint Waveform Lidar , 2013 .

[31]  Peng Li,et al.  Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories , 2015 .

[32]  Lingfeng Duan,et al.  A novel machine-vision-based facility for the automatic evaluation of yield-related traits in rice , 2011, Plant Methods.

[33]  Gang Pan,et al.  Digital camera based measurement of crop cover for wheat yield prediction , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[34]  A. Baccini,et al.  Mapping forest canopy height globally with spaceborne lidar , 2011 .

[35]  Maggi Kelly,et al.  A New Method for Segmenting Individual Trees from the Lidar Point Cloud , 2012 .

[36]  Loren H Rieseberg,et al.  Food security: crop species diversity. , 2010, Science.

[37]  Alan H. Strahler,et al.  Separating leaves from trunks and branches with dual-wavelength terrestrial lidar scanning , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[38]  Stan Szpakowicz,et al.  Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation , 2006, Australian Conference on Artificial Intelligence.

[39]  Jon Louis Bentley,et al.  Multidimensional binary search trees used for associative searching , 1975, CACM.

[40]  Léon Bottou,et al.  Stochastic Gradient Descent Tricks , 2012, Neural Networks: Tricks of the Trade.

[41]  Changying Li,et al.  In-field High Throughput Phenotyping and Cotton Plant Growth Analysis Using LiDAR , 2018, Front. Plant Sci..

[42]  Paolo Remagnino,et al.  Plant species identification using digital morphometrics: A review , 2012, Expert Syst. Appl..

[43]  Darren M. Wells,et al.  Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies , 2017, bioRxiv.

[44]  Alan H. Strahler,et al.  Three-dimensional forest reconstruction and structural parameter retrievals using a terrestrial full-waveform lidar instrument (Echidna®) , 2013 .

[45]  Q. Guo,et al.  A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data , 2014 .

[46]  D. Ehlert,et al.  Measuring crop biomass density by laser triangulation , 2008 .

[47]  N. Ramankutty,et al.  Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000 , 2008 .

[48]  Wouter Saeys,et al.  Estimation of the crop density of small grains using LiDAR sensors. , 2009 .

[49]  Guangjian Yan,et al.  Image-based 3D corn reconstruction for retrieval of geometrical structural parameters , 2009 .

[50]  L. Xiong,et al.  Plant phenomics and high-throughput phenotyping: accelerating rice functional genomics using multidisciplinary technologies. , 2013, Current opinion in plant biology.

[51]  Jin Liu,et al.  Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data , 2016, Remote. Sens..

[52]  Hervé Sinoquet,et al.  Light interception and partitioning between shoots in apple cultivars influenced by training. , 2008, Tree physiology.

[53]  S. Omholt,et al.  Phenomics: the next challenge , 2010, Nature Reviews Genetics.

[54]  S. Vermeulen,et al.  Breeding Technologies to Increase Crop Production in a Changing World , 2010 .

[55]  C. Klukas,et al.  Advanced phenotyping and phenotype data analysis for the study of plant growth and development , 2015, Front. Plant Sci..

[56]  Y. Heyden,et al.  Robust statistics in data analysis — A review: Basic concepts , 2007 .

[57]  Ashish Sharma,et al.  An Enhanced Density Based Spatial Clustering of Applications with Noise , 2009, 2009 IEEE International Advance Computing Conference.