Automatic organ-level point cloud segmentation of maize shoots by integrating high-throughput data acquisition and deep learning

Leaf angle and leaf area index together influence canopy light interception and canopy photosynthesis. However, so far, there is no effective method to identify the optimal combination of these two parameters for canopy photosynthesis. In this study, first a robust high-throughput method for accurate segmentation of maize organs based on 3D point clouds data was developed, then the segmented plant organs were used to generate new 3D point clouds for the canopy of altered architectures. With this, we simulated the synergistic effect of leaf area and leaf angle on canopy photosynthesis. The results show that, compared to the traditional parameters describing the canopy photosynthesis including leaf area index, facet angle and canopy coverage, a new parameter - the canopy occupation volume (COV) - can better explain the variations of canopy photosynthetic capacity. Specifically, COV can explain > 79% variations of canopy photosynthesis generated by changing leaf angle and > 84% variations of canopy photosynthesis generated by changing leaf area. As COV can be calculated in a high-throughput manner based on the canopy point clouds, it can be used to evaluate canopy architecture in breeding and agronomic research.

Canopy occupation volume as an indicator of canopy photosynthetic capacity.

This paper reviews the past and current trends of three-dimensional (3D) modeling and reconstruction of plants and trees. These topics have been studied in multiple research fields, including computer vision, graphics, plant phenotyping, and forestry. This paper, therefore, provides a cross-cutting review. Representations of plant shape and structure are first summarized, where every method for plant modeling and reconstruction is based on a shape/structure representation. The methods were then categorized into 1) creating non-existent plants (modeling) and 2) creating models from real-world plants (reconstruction). This paper also discusses the limitations of current methods and possible future directions.

/pdf/3d-modeling-and-reconstruction-of-plants-and-trees-a-cross-la17sr6u.pdf

3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping

Maize (Zea mays L.) is one of the three major cereal crops in the world. Leaf angle is an important architectural trait of crops due to its substantial role in light interception by the canopy and hence photosynthetic efficiency. Traditionally, leaf angle has been measured using a protractor, a process that is both slow and laborious. Efficiently measuring leaf angle under field conditions via imaging is challenging due to leaf density in the canopy and the resulting occlusions. However, advances in imaging technologies and machine learning have provided new tools for image acquisition and analysis that could be used to characterize leaf angle using three‐dimensional (3D) models of field‐grown plants. In this study, PhenoBot 3.0, a robotic vehicle designed to traverse between pairs of agronomically spaced rows of crops, was equipped with multiple tiers of PhenoStereo cameras to capture side‐view images of maize plants in the field. PhenoStereo is a customized stereo camera module with integrated strobe lighting for high‐speed stereoscopic image acquisition under variable outdoor lighting conditions. An automated image processing pipeline (AngleNet) was developed to measure leaf angles of nonoccluded leaves. In this pipeline, a novel representation form of leaf angle as a triplet of keypoints was proposed. The pipeline employs convolutional neural networks to detect each leaf angle in two‐dimensional images and 3D modeling approaches to extract quantitative data from reconstructed models. Satisfactory accuracies in terms of correlation coefficient (r) and mean absolute error (MAE) were achieved for leaf angle ( r > 0.87 ,   M A E < 5 ° $r\gt 0.87,\unicode{x02007}MAE\lt \phantom{\rule{}{0ex}}{5}^{^\circ }$ ) and internode heights ( r > 0.99 ,   M A E < 3.5   cm $r\gt 0.99,\unicode{x02007}MAE\lt \phantom{\rule{}{0ex}}3.5\unicode{x0200A}\mathrm{cm}$ ). Our study demonstrates the feasibility of using stereo vision to investigate the distribution of leaf angles in maize under field conditions. The proposed system is an efficient alternative to traditional leaf angle phenotyping and thus could accelerate breeding for improved plant architecture.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/rob.22166

Field‐based robotic leaf angle detection and characterization of maize plants using stereo vision and deep convolutional neural networks

A calculation method of phenotypic traits based on three-dimensional reconstruction of tomato canopy

Automatic stem-leaf segmentation of maize shoots using three-dimensional point cloud

Nowadays, there are many approaches to acquire three-dimensional (3D) point clouds of maize plants. However, automatic stem-leaf segmentation of maize shoots from three-dimensional (3D) point clouds remains challenging, especially for new emerging leaves that are very close and wrapped together during the seedling stage. To address this issue, we propose an automatic segmentation method consisting of three main steps: skeleton extraction, coarse segmentation based on the skeleton, fine segmentation based on stem-leaf classification. The segmentation method was tested on 30 maize seedlings and compared with manually obtained ground truth. The mean precision, mean recall, mean micro F1 score and mean over accuracy of our segmentation algorithm were 0.964, 0.966, 0.963 and 0.969. Using the segmentation results, two applications were also developed in this paper, including phenotypic trait extraction and skeleton optimization. Six phenotypic parameters can be accurately and automatically measured, including plant height, crown diameter, stem height and diameter, leaf width and length. Furthermore, the values of R2 for the six phenotypic traits were all above 0.94. The results indicated that the proposed algorithm could automatically and precisely segment not only the fully expanded leaves, but also the new leaves wrapped together and close together. The proposed approach may play an important role in further maize research and applications, such as genotype-to-phenotype study, geometric reconstruction and dynamic growth animation. We released the source code and test data at the web site this https URL

Stem-leaf segmentation and phenotypic trait extraction of maize shoots from three-dimensional point cloud.

Accurate determination of the moisture content in maize kernels conduces to screen maize germplasm materials with efficient dehydration. Low-field nuclear magnetic resonance (LF-NMR) single-kernel non-destructive testing technology was used to determine the moisture content at different phase states in the kernels for selected types of maize. The T2 relaxation inversion spectrum was monitored in maize kernels to determine the variation in the moisture content in different phase states with time. The total water and free water peaked at the filling stage of the maize kernels and then declined to a minimum at physiological maturity. The semi-bound water generally increased to a long-lasting peak in the dough stage and then declined. The bound water increased from kernel formation to maturity and then remained stable. The contents of total water, free water, semi-bound water, and bound water had significant differences among kernel types but not among varieties of the same type. The contents of semi-bound water and free water were linearly correlated with the dehydration rates of the kernels. The results of this study can provide a means for creating new germplasm materials. Keywords: Maize (Zea mays L.), LF-NMR, moisture phase state, kernel type, dehydration rate DOI: 10.25165/j.ijabe.20231601.7898 Citation: Li N, Xu T Y, Hao N. Phase states of moisture content in different maize kernel types. Int J Agric & Biol Eng, 2023; 16(1): 250–259. 

Phase states of moisture content in different maize kernel types

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Automatic stem-leaf segmentation of maize shoots using three-dimensional point cloud

Stem-leaf segmentation and phenotypic trait extraction of maize shoots from three-dimensional point cloud.

Phase states of moisture content in different maize kernel types