scholarly journals PI-Plat: A high-resolution image-based 3D reconstruction method to estimate growth dynamics of rice inflorescence traits

2019 ◽  
Author(s):  
Jaspreet Sandhu ◽  
Feiyu Zhu ◽  
Puneet Paul ◽  
Tian Gao ◽  
Balpreet K. Dhatt ◽  
...  
Keyword(s):  
High Resolution ◽  
3D Reconstruction ◽  
Low Cost ◽  
Crop Improvement ◽  
Growth Dynamics ◽  
Superior Performance ◽  
Plant Phenotyping ◽  
Reconstruction Method ◽  
Color Intensity ◽  
Non Destructive

AbstractBackgroundRecent advances in image-based plant phenotyping have improved our capability to study vegetative stage growth dynamics. However, more complex agronomic traits such as inflorescence architecture (IA), which predominantly contributes to grain crop yield are more challenging to quantify and hence are relatively less explored. Previous efforts to estimate inflorescence-related traits using image-based phenotyping have been limited to destructive end-point measurements. Development of non-destructive inflorescence phenotyping platforms could accelerate the discovery of the phenotypic variation with respect to inflorescence dynamics and mapping of the underlying genes regulating critical yield components.ResultsThe major objective of this study is to evaluate post-fertilization development and growth dynamics of inflorescence at high spatial and temporal resolution in rice. For this, we developed the Panicle Imaging Platform (PI-Plat) to comprehend multi-dimensional features of IA in a non-destructive manner. We used 11 rice genotypes to capture multi-view images of primary panicle on weekly basis after the fertilization. These images were used to reconstruct a 3D point cloud of the panicle, which enabled us to extract digital traits such as voxel count and color intensity. We found that the voxel count of developing panicles is positively correlated with seed number and weight at maturity. The voxel count from developing panicles projected overall volumes that increased during the grain filling phase, wherein quantification of color intensity estimated the rate of panicle maturation. Our 3D based phenotyping solution showed superior performance compared to conventional 2D based approaches.ConclusionsFor harnessing the potential of the existing genetic resources, we need a comprehensive understanding of the genotype-to-phenotype relationship. Relatively low-cost sequencing platforms have facilitated high-throughput genotyping, while phenotyping, especially for complex traits, has posed major challenges for crop improvement. PI-Plat offers a low cost and high-resolution platform to phenotype inflorescence-related traits using 3D reconstruction-based approach. Further, the non-destructive nature of the platform facilitates analyses of the same panicle at multiple developmental time points, which can be utilized to explore the genetic variation for dynamic inflorescence traits in cereals.

scholarly journals PI-Plat: a high-resolution image-based 3D reconstruction method to estimate growth dynamics of rice inflorescence traits

Plant Methods ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Jaspreet Sandhu ◽  
Feiyu Zhu ◽  
Puneet Paul ◽  
Tian Gao ◽  
Balpreet K. Dhatt ◽  
...  
Keyword(s):  
High Resolution ◽  
3D Reconstruction ◽  
Low Cost ◽  
Crop Improvement ◽  
Growth Dynamics ◽  
Superior Performance ◽  
Plant Phenotyping ◽  
Reconstruction Method ◽  
Color Intensity ◽  
Non Destructive

Abstract Background Recent advances in image-based plant phenotyping have improved our capability to study vegetative stage growth dynamics. However, more complex agronomic traits such as inflorescence architecture (IA), which predominantly contributes to grain crop yield are more challenging to quantify and hence are relatively less explored. Previous efforts to estimate inflorescence-related traits using image-based phenotyping have been limited to destructive end-point measurements. Development of non-destructive inflorescence phenotyping platforms could accelerate the discovery of the phenotypic variation with respect to inflorescence dynamics and mapping of the underlying genes regulating critical yield components. Results The major objective of this study is to evaluate post-fertilization development and growth dynamics of inflorescence at high spatial and temporal resolution in rice. For this, we developed the Panicle Imaging Platform (PI-Plat) to comprehend multi-dimensional features of IA in a non-destructive manner. We used 11 rice genotypes to capture multi-view images of primary panicle on weekly basis after the fertilization. These images were used to reconstruct a 3D point cloud of the panicle, which enabled us to extract digital traits such as voxel count and color intensity. We found that the voxel count of developing panicles is positively correlated with seed number and weight at maturity. The voxel count from developing panicles projected overall volumes that increased during the grain filling phase, wherein quantification of color intensity estimated the rate of panicle maturation. Our 3D based phenotyping solution showed superior performance compared to conventional 2D based approaches. Conclusions For harnessing the potential of the existing genetic resources, we need a comprehensive understanding of the genotype-to-phenotype relationship. Relatively low-cost sequencing platforms have facilitated high-throughput genotyping, while phenotyping, especially for complex traits, has posed major challenges for crop improvement. PI-Plat offers a low cost and high-resolution platform to phenotype inflorescence-related traits using 3D reconstruction-based approach. Further, the non-destructive nature of the platform facilitates analyses of the same panicle at multiple developmental time points, which can be utilized to explore the genetic variation for dynamic inflorescence traits in cereals.

scholarly journals LeafScope: A Portable High-Resolution Multispectral Imager for In Vivo Imaging Soybean Leaf

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2194
Author(s):  
Liangju Wang ◽  
Yunhong Duan ◽  
Libo Zhang ◽  
Jialei Wang ◽  
Yikai Li ◽  
...  
Keyword(s):  
High Resolution ◽  
Low Cost ◽  
Small Region ◽  
Plant Phenotyping ◽  
Portable Devices ◽  
Hardware System ◽  
Soybean Plants ◽  
Water Treatments ◽  
Multispectral Imager

Portable devices for measuring plant physiological features with their isolated measuring chamber are playing an increasingly important role in plant phenotyping. However, currently available commercial devices of this type, such as soil plant analysis development (SPAD) meter and spectrometer, are dot meters that only measure a small region of the leaf, which does not perfectly represent the highly varied leaf surface. This study developed a portable and high-resolution multispectral imager (named LeafScope) to in-vivo image a whole leaf of dicotyledon plants while blocking the ambient light. The hardware system is comprised of a monochrome camera, an imaging chamber, a lightbox with different bands of light-emitting diodes (LEDs) array, and a microcontroller. During measuring, the device presses the leaf to lay it flat in the imaging chamber and acquires multiple images while alternating the LED bands within seconds in a certain order. The results of an experiment with soybean plants clearly showed the effect of nitrogen and water treatments as well as the genotype differences by the color and morphological features from image processing. We conclude that the low cost and easy to use LeafScope can provide promising imaging quality for dicotyledon plants, so it has great potential to be used in plant phenotyping.

scholarly journals A Study of 3D Digitisation Modalities for Crime Scene Investigation

2021 ◽  
Vol 1 (2) ◽  
pp. 56-85
Author(s):  
George Galanakis ◽  
Xenophon Zabulis ◽  
Theodore Evdaimon ◽  
Sven-Eric Fikenscher ◽  
Sebastian Allertseder ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Low Cost ◽  
Crime Scene ◽  
Reconstruction Method ◽  
Crime Scene Investigation ◽  
The Public ◽  
Wide Range ◽  
Time Availability ◽  
Scene Investigation ◽  
3D Digitisation

A valuable aspect during crime scene investigation is the digital documentation of the scene. Traditional means of documentation include photography and in situ measurements from experts for further analysis. Although 3D reconstruction of pertinent scenes has already been explored as a complementary tool in investigation pipelines, such technology is considered unfamiliar and not yet widely adopted. This is explained by the expensive and specialised digitisation equipment that is available so far. However, the emergence of high-precision but low-cost devices capable of scanning scenes or objects in 3D has been proven as a reliable alternative to their counterparts. This paper summarises and analyses the state-of-the-art technologies in scene documentation using 3D digitisation and assesses the usefulness in typical police-related situations and the forensics domain in general. We present the methodology for acquiring data for 3D reconstruction of various types of scenes. Emphasis is placed on the applicability of each technique in a wide range of situations, ranging in type and size. The application of each reconstruction method is considered in this context and compared with respect to additional constraints, such as time availability and simplicity of operation of the corresponding scanning modality. To further support our findings, we release a multi-modal dataset obtained from a hypothetical indoor crime scene to the public.

scholarly journals Automated 3D Scenes Reconstruction Using Multiple Stereo Pairs from Portable Four-Camera Photographic Measurement System

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Qi Peng ◽  
Lifen Tu ◽  
Kaibing Zhang ◽  
Sidong Zhong
Keyword(s):  
3D Reconstruction ◽  
Measurement System ◽  
3D Model ◽  
Low Cost ◽  
Reconstruction Method ◽  
Feature Points ◽  
Point Matching ◽  
Mogao Grottoes ◽  
Feature Point Matching ◽  
3D Scene

An effective automatic 3D reconstruction method using a portable four-camera photographic measurement system (PFCPMS) is proposed. By taking advantage of the complementary stereo information from four cameras, a fast and highly accurate feature point matching algorithm is developed for 3D reconstruction. Specifically, we first utilize a projection method to obtain a large number of dense feature points. And then a reduction and clustering treatment is applied to simplify the Delaunay triangulation process and reconstruct a 3D model for each scene. In addition, a 3D model stitching approach is proposed to further improve the performance of the limited field-of-view for image-based method. The experimental results tested on the 172 cave in Mogao Grottoes indicate that the proposed method is effective to reconstruct a 3D scene with a low-cost four-camera photographic measurement system.

scholarly journals Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256340
Author(s):  
David Schunck ◽  
Federico Magistri ◽  
Radu Alexandru Rosu ◽  
André Cornelißen ◽  
Nived Chebrolu ◽  
...  
Keyword(s):  
High Resolution ◽  
3D Reconstruction ◽  
Point Clouds ◽  
Training Data ◽  
Plant Phenotyping ◽  
Growth Stages ◽  
Plant Analysis ◽  
Rigid Registration ◽  
3D Point Clouds ◽  
Spatio Temporal

Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.

scholarly journals A low-cost DIY device for high resolution, continuous measurement of microbial growth dynamics

2018 ◽  
Author(s):  
Kalesh Sasidharan ◽  
Andrea S. Martinez-Vernon ◽  
Jing Chen ◽  
Tiantian Fu ◽  
Orkun S Soyer
Keyword(s):  
High Resolution ◽  
Open Source ◽  
Scientific Community ◽  
Microbial Growth ◽  
Continuous Measurement ◽  
Low Cost ◽  
Growth Dynamics ◽  
Genetic Alterations ◽  
Data Logging ◽  
Culture Density

ABSTRACTHigh-resolution data on microbial growth dynamics allow characterisation of microbial physiology, as well as optimisation of genetic alterations thereof. Such data are routinely collected using bench-top spectrophotometers or so-called plate readers. These equipments present several drawbacks: (i) measurements from different devices cannot be compared directly, (ii) proprietary nature of devices makes it difficult for standardisation methods to be developed across devices, and (iii) high costs limit access to devices, which can become a bottleneck for researchers, especially for those working with anaerobic organisms or at higher containment level laboratories. These limitations could be lifted, and data reproducibility improved, if the scientific community could adopt standardised, low-cost and open-source devices that can be built in-house. Here, we present such a device, MicrobeMeter, which is a do-it-yourself (DIY), simple, yet robust photometer with continuous data-logging capability. It is built using 3D-printing and open-source Arduino platform, combined with purpose-built electronic circuits. We show that MicrobeMeter displays linear relation between culture density and turbidity measurement for microbes from different phylogenetic domains. In addition, culture density estimated from MicrobeMeter measurements produced less variance compared against three commercial bench-top spectrophotometers, indicating that its measurements are less affected by the differences in cell types. We show the utility of MicrobeMeter, as a programmable wireless continuous measurement device, by collecting long-term growth dynamics up to 458 hours from aerobic and anaerobic cultures. We provide a full open-source description of MicrobeMeter and its implementation for faster adaptation and future development by the scientific community. The blueprints of the device, as well as ready-to-assemble kit versions are also made available throughwww.humanetechnologies.co.uk.

3D Shape Estimation for Smooth Surfaces Using Double Mirror System

2021 ◽  
Vol 2021 (18) ◽  
pp. 69-1-69-11
Author(s):  
Yin Wang ◽  
Davi He ◽  
Zillion Lin ◽  
George Chiu ◽  
Jan Allebach
Keyword(s):  
3D Reconstruction ◽  
Camera Calibration ◽  
Low Cost ◽  
Depth Map ◽  
Mirror System ◽  
Reconstruction Method ◽  
Single Camera ◽  
Shape Estimation ◽  
Calibration Process ◽  
3D Shape

In this paper, a low cost, single camera, double mirror system that can be built in a desktop nail printer will be described. The usage of this system is to capture an image of a fingernail and to generate the 3D shape of the nail. The nail’s depth map will be estimated from this rendered 3D nail shape. The paper will describe the camera calibration process and explain the calibration theory for this proposed system. Then a 3D reconstruction method will be introduced, as well. Experimental results will be shown in the paper, which illustrate the accuracy of the system to handle the rendering task.

scholarly journals Research on Super-resolution Reconstruction Method of Lidar 3D Range Profile

2021 ◽  
Author(s):  
Jianfeng Sun ◽  
Di Liu ◽  
Daoran Gong ◽  
Le Ma ◽  
Xin Zhang ◽  
...  
Keyword(s):  
High Resolution ◽  
Low Cost ◽  
Super Resolution ◽  
Reconstruction Method ◽  
Range Image ◽  
Low Resolution ◽  
Range Profile ◽  
Markov Random ◽  
Guidance Algorithm ◽  
Regional Similarity

At present, how to use low-cost and superior algorithms to obtain high-resolution 3D range image is the focus of lidar research. In this letter, the low-resolution Gm-APD lidar is combined with the high-resolution ICCD lidar to obtain the registered low-resolution range image and high-resolution intensity image. This letter proposes an improved image guidance algorithm. The algorithm uses a Markov random field model to define a global energy function. This function combines the distance fidelity term and the regularization term to obtain a high-resolution 3D range image by solving the optimization model. The experimental results show that compared with the traditional algorithms, the algorithm improves the resolution of the range images, the edge of the reconstructed image is sharper than the regional similarity guidance algorithm, and the image quality evaluation index has the better value.

3D Reconstruction Based on Single Defocused Microscopic Image

2012 ◽  
Author(s):  
Z. G. Xing ◽  
C. M. Zhao ◽  
J. Wei ◽  
Z. Wei
Keyword(s):  
3D Reconstruction ◽  
High Speed ◽  
Measurement Accuracy ◽  
Low Cost ◽  
Three Dimensional ◽  
Reconstruction Method ◽  
Microscopic Image ◽  
Depth Of Focus ◽  
Fuzzy Degree

Microscope has being limited by the depth of focus, while the focused image is clear, the defocused images are fuzzy and fuzzy degree of the object images vary with different defocused distances. This paper presented a 3D reconstruction method based on a defocused microscopic image. After the defocused microscopic image is divided the microscopic into M × N regions, the fuzzy degree of each region is quantitatively evaluated. A corresponding curve of the relation between fuzzy degree and defocus distance is drawn by the presented algorithm in this paper, and then the three-dimensional characteristics of objects are reconstructed. This method has the merits of little computation, low cost and high speed. And M and N values can be changed according to the needs of the measurement accuracy.

scholarly journals Using Plant Phenomics to Exploit the Gains of Genomics

Agronomy ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 126 ◽  
Author(s):  
Aditya Pratap ◽  
Sanjeev Gupta ◽  
Ramakrishnan Nair ◽  
S. Gupta ◽  
Roland Schafleitner ◽  
...  
Keyword(s):  
Complex Traits ◽  
Low Cost ◽  
Crop Improvement ◽  
Minimal Invasive ◽  
Plant Phenotyping ◽  
Great Promise ◽  
Genetic Dissection ◽  
Plant Structure ◽  
Resource Limited ◽  
And Function

Agricultural scientists face the dual challenge of breeding input-responsive, widely adoptable and climate-resilient varieties of crop plants and developing such varieties at a faster pace. Integrating the gains of genomics with modern-day phenomics will lead to increased breeding efficiency which in turn offers great promise to develop such varieties rapidly. Plant phenotyping techniques have impressively evolved during the last two decades. The low-cost, automated and semi-automated methods for data acquisition, storage and analysis are now available which allow precise quantitative analysis of plant structure and function; and genetic dissection of complex traits. Appropriate plant types can now be quickly developed that respond favorably to low input and resource-limited environments and address the challenges of subsistence agriculture. The present review focuses on the need of systematic, rapid, minimal invasive and low-cost plant phenotyping. It also discusses its evolution to modern day high throughput phenotyping (HTP), traits amenable to HTP, integration of HTP with genomics and the scope of utilizing these tools for crop improvement.

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