High-throughput analysis of maize azimuth and spacing from Lidar data

Zilong Wang, Yiming Zhang, Liqing Chen, Delin Wu, Yuwei Wang, Lu Liu

Abstract


Efficient leaf azimuth angles and plant spacing are crucial for enhancing light interception efficiency in maize, thereby increasing yield per unit area. Traditional methods for measuring these traits are labor-intensive and prone to error. This study aimed to develop an accurate and efficient method for determining leaf azimuth angles and plant spacing in maize to improve understanding of field competition and support breeding programs. Utilizing light detection and ranging (Lidar) technology, 3D point cloud data of maize plants were collected, enabling effective 3D morphological reconstruction through multi-frame stitching. Principal component analysis (PCA) was employed to determine the leaf azimuth angles of individual maize plants. Additionally, a method based on point density analysis was developed to identify the central axis position of single maize plants. Specifically, point density in the neighborhood of each point in the maize point cloud was calculated, with the central axis determined along the direction of highest point density. The integration of PCA-based leaf azimuth detection and point density analysis provided a robust framework for accurately determining leaf azimuth angles and plant spacing. In the detection of leaf azimuth angles, this method achieved an R2 of 0.87 and an RMSE of 5.19°. For plant spacing detection, the R2 was 0.83 and the RMSE was 0.08 m. This approach facilitates parameterized modeling of field competition, significantly enhancing the efficiency of breeding programs by providing detailed and precise phenotypic data. Despite the high accuracy demonstrated by the proposed methods, further investigation is needed to evaluate their effectiveness under varying environmental conditions and across different maize varieties. Additionally, challenges related to partial occlusions and complex canopy structures may impact the accuracy of point cloud data analysis, necessitating further refinement of the algorithms.
Keywords: Lidar, maize azimuth angle, 3D point cloud, principal component analysis
DOI: 10.25165/j.ijabe.20241705.8645

Citation: Wang Z L, Zhang Y M, Chen L Q, Wu D L, Wang Y W, Liu L. High-throughput analysis of maize azimuth and spacing from Lidar data. Int J Agric & Biol Eng, 2024; 17(5): 105-111.

Keywords


Lidar, maize azimuth angle, 3D point cloud, principal component analysis

Full Text:

PDF

References


Liao J, Wang Y, Zhu D Q, Zou Y, Zhang S, Zhou H Y. Automatic segmentation of crop/background based on luminance partition correction and adaptive threshold. IEEE Access, 2020; 8: 202611–202622.

Stomph T J, Dordas C, Baranger A, Rijk J d, Dong B, Evers J, et al. Designing intercrops for high yield, yield stability and efficient use of resources: Are there principles? In: Sparks D L （Ed.）. Advances in Agronomy. Newark: University of Delaware. 2020; pp.1–50.

Song Q F, Liu F S, Bu H Y, Zhu X G. Quantifying contributions of different factors to canopy photosynthesis in 2 maize varieties: development of a novel 3D canopy modeling pipeline. Plant Phenomics, 2023; 5: 0075.

Elmore R W, Marx D B, Klein R G, Abendroth L J. Wind effect on corn leaf azimuth. Crop Science, 2005; 45（6）: 2598–2604.

Wang Y W, Cai J X, Zhang D S, Chen X C, Wang Y J. Nonlinear correction for fringe projection profilometry with shifted-phase histogram equalization. IEEE Transactions on Instrumentation and Measurement, 2022; 71: 1–9.

Xu R, Li C Y. A review of high-throughput field phenotyping systems: focusing on ground robots. Plant Phenomics, 2022; 4（1）: 215–234.

Li W, Niu Z, Chen H Y, Li D, Wu M Q, Zhao W. Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecological Indicators, 2016; 67: 637–648.

Li M H, Shamshiri R R, Schirrmann M, Weltzien C, Shafian S, Laursen M S. UAV oblique imagery with an adaptive micro-terrain model for estimation of leaf area index and height of maize canopy from 3D point clouds. Remote Sensing, 2022; 14（3）: 585.

Xie D B, Chen L, Liu L C, Chen L Q, Wang H. Actuators and sensors for application in agricultural robots: A review. Machines, 2022; 10（10）: 913.

Wang H, Qian X J, Zhang L, Xu S L, Li H F, Xia X J, et al. A method of high throughput monitoring crop physiology using chlorophyll fluorescence and multispectral imaging. Frontiers in Plant Science, 2018; 9: 407.

Wang Y W, Xu H Z, Zhu H J, Chen X C, Wang Y J. Pixel-wise phase unwrapping with adaptive reference phase estimation for 3-D shape measurement. IEEE Transactions on Instrumentation and Measurement, 2023; 72: 1–9.

Li Y L, Wen W L, Fan J C, Gou W B, Gu S H, Lu X J, et al. Multi-source data fusion improves time-series phenotype accuracy in maize under a field high-throughput phenotyping platform. Plant phenomics, 2023; 5: 0043.

Friedli M, Kirchgessner N, Grieder C, Liebisch F, Mannale M, Walter A. Terrestrial 3D laser scanning to track the increase in canopy height of both monocot and dicot crop species under field conditions. Plant Methods, 2016; 12: 9.

Jin S C, Su Y J, Wu F F, Pang S X, Gao S, Hu T U, et al. Stem–leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LIDAR data. IEEE Transactions on Geoscience Remote Sensing, 2019; 57（3）: 1336–1346.

Gao M, Yang F B, Wei H, Liu X X. Individual maize location and height estimation in field from UAV-Borne LiDAR and RGB images. Remote Sensing, 2022; 14（10）: 2292.

Zhou C Q, Yang G J, Liang D, Yang X D, Xu B. An integrated skeleton extraction and pruning method for spatial recognition of maize seedlings in MGV and UAV remote images. IEEE Transactions on Geoscience Remote Sensing, 2018; 56（8）: 4618–4632.

Lei L, Li Z H, Wu J T, Zhang C J, Zhu Y H, Chen R Q, et al. Extraction of maize leaf base and inclination angles using terrestrial laser scanning （TLS） data. IEEE Transactions on Geoscience Remote Sensing, 2022; 60: 1–17.

Li Y L, Wen W L, Guo X Y, Yu Z T, Gu S H, Yan H P, et al. High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network. PLoS ONE, 2021; 16（1）: e0241528.

Lin C D, Hu F Z, Peng J W, Wang J, Zhai R F. Segmentation and stratification methods of field maize terrestrial LiDAR point cloud. Agriculture, 2022; 12（9）: 1450.

Wu S, Wen W L, Xiao B X, Guo X Y, Du J J, Wang C Y, et al. An accurate skeleton extraction approach from 3D point clouds of maize plants. Frontiers in Plant Science, 2019; 10: 248.

Su Y J, Wu F F, Ao Z R, Jin S C, Qin F, Liu B X, Pang S X, et al. Evaluating maize phenotype dynamics under drought stress using terrestrial lidar. Plant Methods, 2019; 15: 1–16.

Lei L, Qiu C X, Li Z H, Han D, Han L, Zhu Y H, et al. Effect of leaf occlusion on leaf area index inversion of maize using UAV–LiDAR data. Remote Sensing, 2019; 11（9）: 1067.

Jin S C, Su Y J, Song S L, Xu K X, Hu T Y, Yang Q L, et al. Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level. Plant Methods, 2020; 16: 69.

Drouet J L, Moulia B. Spatial re-orientation of maize leaves affected by initial plant orientation and density. Agricultural and Forest Meteorology, 1997; 88（1-4）: 85–100.

Girardin P, Tollenaar M. Leaf azimuth in maize : origin and effects on canopy patterns. European Journal of Agronomy, 1992; 1（4）: 227–233.

Xu S, Zaidan M A, Honkavaara E, Hakala T, Viljanen N, Porcar-Castell A, et al. On the estimation of the leaf angle distribution from drone based photogrammetry. In: IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa: IEEE, 2020; pp.4379–4382.

Chelle M, Toulouse P, Combes D. Should be taken into account the plant azimuth to estimate the light phylloclimate within a virtual maize canopy? In: 2009 Third International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications, Beijing: IEEE, 2009; pp.103–106.

He W L, Gage J L, Rellán-Álvarez R, Xiang L R. Swin-Roleaf: A new method for characterizing leaf azimuth angle in large-scale maize plants. Computers and Electronics in Agriculture, 2024; 224: 109120.




Copyright (c) 2024 International Journal of Agricultural and Biological Engineering

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

2023-2026 Copyright IJABE Editing and Publishing Office