Structured Light-Based 3D Reconstruction System for Plants

Nguyen, Thuy Tuong; Slaughter, David C.; Max, Nelson; Maloof, Julin N.; Sinha, Neelima

doi:10.3390/s150818587

Cited by 150 publications

(93 citation statements)

References 38 publications

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“…The specific methodology also allowed to track the development of the maize plant under different growth stages, which is important for computer simulations. Structured light-based 3D reconstruction is presented in [7] using multiple pairs of high-resolution digital cameras. A time-of-flight (ToF) camera for corn plants' 3D holographic reconstruction under a controlled environment is utilized in [8].…”

Section: Introductionmentioning

confidence: 99%

3D Maize Plant Reconstruction Based on Georeferenced Overlapping LiDAR Point Clouds

et al. 2015

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3D crop reconstruction with a high temporal resolution and by the use of non-destructive measuring technologies can support the automation of plant phenotyping processes. Thereby, the availability of such 3D data can give valuable information about the plant development and the interaction of the plant genotype with the environment. This article presents a new methodology for georeferenced 3D reconstruction of maize plant structure. For this purpose a total station, an IMU, and several 2D LiDARs with different orientations were mounted on an autonomous vehicle. By the multistep methodology presented, based on the application of the ICP algorithm for point cloud fusion, it was possible to perform the georeferenced point clouds overlapping. The overlapping point cloud algorithm showed that the aerial points (corresponding mainly to plant parts) were reduced to 1.5%-9% of the total registered data. The remaining were redundant or ground points. Through the inclusion of different LiDAR point of views of the scene, a more realistic representation of the surrounding is obtained by the incorporation of new useful information but also of noise. The use of georeferenced 3D maize plant reconstruction at different growth stages, combined with the total station accuracy could be highly useful when performing precision agriculture at the crop plant level.

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Section: Introductionmentioning

confidence: 99%

3D Maize Plant Reconstruction Based on Georeferenced Overlapping LiDAR Point Clouds

et al. 2015

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“…Spatial encoders in the scanning arm provide automatic 3D registration in X-Y-Z space. This technique avoids common issues in imaging-based approaches that require segmentation, alignment, and thresholding [65, 66] because scanning only captures plant material, without any background. The accuracy of scanning-based measurements have been well-validated compared to reference measurements [67, 68], justifying their use here.…”

Section: Methodsmentioning

confidence: 99%

A Statistical Description of Plant Shoot Architecture

et al. 2017

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SUMMARY Plant architectures can be characterized statistically by their spatial density function, which specifies the probability of finding a branch at each location in the territory occupied by a plant. Using high-precision 3D scanning, we analyzed 557 plant shoot architectures, representing three species, grown across three to five environmental conditions, and through 20–30 developmental time points. We found two elegant properties in the spatial density functions of these architectures: all functions could be nearly modified in one direction without affecting the density in orthogonal directions (called “separability”), and all functions shared the same underlying shape, aside from stretching and compression (called “self-similarity”). Surprisingly, despite their striking visual diversity, we discovered that all architectures could be described as variations on a single underlying function: a Gaussian density function truncated at roughly two SDs. We also observed systematic variation in the spatial density functions across species, growth conditions, and time, which suggests functional specialization despite following the same general design form.

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“…Several different cameras [20] b. A moving camera [21] Figure 1 Plant height measuring 3.1.2 Plant height measurement based on lidar/laser sensors Lidar or laser sensors have been used to measure plant height because they present good adaptation to illumination and provide considerable data.…”

Section: Plant Height Measurementsmentioning

confidence: 99%

Sensors for measuring plant phenotyping: A review

Qiu¹,

Wei²,

Zhang³

et al. 2018

International Journal of Agricultural and Biological Engineering

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Food crisis is a matter of prime importance because it becomes more severe as the global population grows. Among the solutions to this crisis, breeding is deemed one of the most effective ways. However, traditional phenotyping in breeding is time consuming and laborious, and the database is insufficient to meet the requirements of plant breeders, which hinders the development of breeding. Accordingly, innovations in phenotyping are urgent to solve this bottleneck. The morphometric and physiological parameters of plant are particularly interested to breeders. Numerous sensors have been employed and novel algorithms have been proposed to collect data on such parameters. This paper presents a brief review on the parameter measurement for phenotyping to describe its development in recent years. Some parameters that have been measured in phenotyping are introduced and discussed, including plant height, leaf parameters, in-plant space, chlorophyll, water stress, and biomass. And the measurement methods of each parameter with different sensors were classified and compared. Some comprehensive measurement platforms were also summarized, which are able to measure several parameters simultaneously. Besides, some deficiencies of phenotyping should be addressed, and novel methods should be proposed to reduce cost, improve efficiency, and promote phenotyping in the future.

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Structured Light-Based 3D Reconstruction System for Plants

Cited by 150 publications

References 38 publications

3D Maize Plant Reconstruction Based on Georeferenced Overlapping LiDAR Point Clouds

3D Maize Plant Reconstruction Based on Georeferenced Overlapping LiDAR Point Clouds

A Statistical Description of Plant Shoot Architecture

Sensors for measuring plant phenotyping: A review

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