Calculation Method for Phenotypic Traits Based on the 3D Reconstruction of Maize Canopies

Ma, Xiaodan; Zhu, Ke; Guan, Haibing; Feng, Jing; Shen, Yu; Liu, Gang

doi:10.3390/s19051201

Cited by 21 publications

(5 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2022) collected 3D point cloud data of maize across extensive fields employing terrestrial laser scanning and obtained the stem diameters by applying elliptical fitting techniques, achieving an R ² in excess of 0.8. Ma et al. (2019) employed a handheld lidar to obtain the diameter of potted maize stems, achieving an R ² of 0.89.…”

Section: Discussionmentioning

confidence: 99%

“…Miao et al (2022) collected 3D point cloud data of maize across extensive fields employing terrestrial laser scanning and obtained the stem diameters by applying elliptical fitting techniques, achieving an R² in excess of 0.8. Ma et al (2019) employed a handheld lidar to obtain the diameter of potted maize stems, achieving an R² of 0.89. Nonetheless, given that lidar technology utilizes laser beams to gauge object surface distances, its utility is primarily confined to the acquisition of 3D point cloud data of maize plants, with an inherent limitation in gathering chromatic information.…”

Section: Figure 11mentioning

confidence: 99%

See 1 more Smart Citation

Detection of maize stem diameter by using RGB-D cameras’ depth information under selected field condition

Zhou,

Cui,

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

Stem diameter is a critical phenotypic parameter for maize, integral to yield prediction and lodging resistance assessment. Traditionally, the quantification of this parameter through manual measurement has been the norm, notwithstanding its tedious and laborious nature. To address these challenges, this study introduces a non-invasive field-based system utilizing depth information from RGB-D cameras to measure maize stem diameter. This technology offers a practical solution for conducting rapid and non-destructive phenotyping. Firstly, RGB images, depth images, and 3D point clouds of maize stems were captured using an RGB-D camera, and precise alignment between the RGB and depth images was achieved. Subsequently, the contours of maize stems were delineated using 2D image processing techniques, followed by the extraction of the stem’s skeletal structure employing a thinning-based skeletonization algorithm. Furthermore, within the areas of interest on the maize stems, horizontal lines were constructed using points on the skeletal structure, resulting in 2D pixel coordinates at the intersections of these horizontal lines with the maize stem contours. Subsequently, a back-projection transformation from 2D pixel coordinates to 3D world coordinates was achieved by combining the depth data with the camera’s intrinsic parameters. The 3D world coordinates were then precisely mapped onto the 3D point cloud using rigid transformation techniques. Finally, the maize stem diameter was sensed and determined by calculating the Euclidean distance between pairs of 3D world coordinate points. The method demonstrated a Mean Absolute Percentage Error (MAPE) of 3.01%, a Mean Absolute Error (MAE) of 0.75 mm, a Root Mean Square Error (RMSE) of 1.07 mm, and a coefficient of determination (R²) of 0.96, ensuring accurate measurement of maize stem diameter. This research not only provides a new method of precise and efficient crop phenotypic analysis but also offers theoretical knowledge for the advancement of precision agriculture.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Figure 11mentioning

confidence: 99%

Detection of maize stem diameter by using RGB-D cameras’ depth information under selected field condition

Zhou,

Cui,

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…In this study, the image-based 3D reconstruction method with simple operations was used, where the RGB camera, which was cheap, could be integrated onto the self-established phenotype platform, thereby providing an effective solution to the extraction of crop phenotypes. In addition, studies on 3D reconstruction of maize have been more focused on the ears and grains stage (Ma et al, 2019;Wang et al, 2019;Zhu et al, 2020;Zhang et al, 2022a), while less attention has been paid to 3D reconstruction of seedlings.…”

Section: Discussionmentioning

confidence: 99%

Three-dimensional reconstruction and phenotype measurement of maize seedlings based on multi-view image sequences

Liu²,

Zhang³

et al. 2022

Front. Plant Sci.

View full text Add to dashboard Cite

As an important method for crop phenotype quantification, three-dimensional (3D) reconstruction is of critical importance for exploring the phenotypic characteristics of crops. In this study, maize seedlings were subjected to 3D reconstruction based on the imaging technology, and their phenotypic characters were analyzed. In the first stage, a multi-view image sequence was acquired via an RGB camera and video frame extraction method, followed by 3D reconstruction of maize based on structure from motion algorithm. Next, the original point cloud data of maize were preprocessed through Euclidean clustering algorithm, color filtering algorithm and point cloud voxel filtering algorithm to obtain a point cloud model of maize. In the second stage, the phenotypic parameters in the development process of maize seedlings were analyzed, and the maize plant height, leaf length, relative leaf area and leaf width measured through point cloud were compared with the corresponding manually measured values, and the two were highly correlated, with the coefficient of determination (R2) of 0.991, 0.989, 0.926 and 0.963, respectively. In addition, the errors generated between the two were also analyzed, and results reflected that the proposed method was capable of rapid, accurate and nondestructive extraction. In the third stage, maize stem leaves were segmented and identified through the region growing segmentation algorithm, and the expected segmentation effect was achieved. In general, the proposed method could accurately construct the 3D morphology of maize plants, segment maize leaves, and nondestructively and accurately extract the phenotypic parameters of maize plants, thus providing a data support for the research on maize phenotypes.

show abstract

“…Our method has four advantages. First of all, the 3D mesh model of the plant obtained by ZGScan 717 has higher precision and richer details compared to those reconstructed by the SFM method [24], time-of-flight (ToF) method [22], binocular stereo vision method [27], LIDAR method [34], and FastSCAN hand-held scanner [35]. The 3D mesh model is reconstructed in real-time during data scanning and can be output directly, thus saving data processing time for 3D reconstruction in most contemporary research.…”

Section: Comparison Of Related Methodsmentioning

confidence: 99%

Automatic Measurement of Morphological Traits of Typical Leaf Samples

Huang

Zheng

Gui

2021

Sensors

View full text Add to dashboard Cite

It is still a challenging task to automatically measure plants. A novel method for automatic plant measurement based on a hand-held three-dimensional (3D) laser scanner is proposed. The objective of this method is to automatically select typical leaf samples and estimate their morphological traits from different occluded live plants. The method mainly includes data acquisition and processing. Data acquisition is to obtain the high-precision 3D mesh model of the plant that is reconstructed in real-time during data scanning by a hand-held 3D laser scanner (ZGScan 717, made in Zhongguan Automation Technology, Wuhan, China). Data processing mainly includes typical leaf sample extraction and morphological trait estimation based on a multi-level region growing segmentation method using two leaf shape models. Four scale-related traits and six corresponding scale-invariant traits can be automatically estimated. Experiments on four groups of different canopy-occluded plants are conducted. Experiment results show that for plants with different canopy occlusions, 94.02% of typical leaf samples can be scanned well and 87.61% of typical leaf samples can be automatically extracted. The automatically estimated morphological traits are correlated with the manually measured values EF (the modeling efficiency) above 0.8919 for scale-related traits and EF above 0.7434 for scale-invariant traits). It takes an average of 196.37 seconds (186.08 seconds for data scanning, 5.95 seconds for 3D plant model output, and 4.36 seconds for data processing) for a plant measurement. The robustness and low time cost of the proposed method for different canopy-occluded plants show potential applications for real-time plant measurement and high-throughput plant phenotype.

show abstract

Calculation Method for Phenotypic Traits Based on the 3D Reconstruction of Maize Canopies

Cited by 21 publications

References 62 publications

Detection of maize stem diameter by using RGB-D cameras’ depth information under selected field condition

Detection of maize stem diameter by using RGB-D cameras’ depth information under selected field condition

Three-dimensional reconstruction and phenotype measurement of maize seedlings based on multi-view image sequences

Automatic Measurement of Morphological Traits of Typical Leaf Samples

Contact Info

Product

Resources

About