scholarly journals Improving High-Throughput Phenotyping Using Fusion of Close-Range Hyperspectral Camera and Low-Cost Depth Sensor

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2711 ◽  
Author(s):  
Peikui Huang ◽  
Xiwen Luo ◽  
Jian Jin ◽  
Liangju Wang ◽  
Libo Zhang ◽  
...  
Keyword(s):  
High Throughput ◽  
Complex Geometry ◽  
Low Cost ◽  
Hyperspectral Images ◽  
Biological Processes ◽  
Depth Sensor ◽  
3D Point Clouds ◽  
Close Range ◽  
High Throughput Phenotyping ◽  
Hyperspectral Camera

Hyperspectral sensors, especially the close-range hyperspectral camera, have been widely introduced to detect biological processes of plants in the high-throughput phenotyping platform, to support the identification of biotic and abiotic stress reactions at an early stage. However, the complex geometry of plants and their interaction with the illumination, severely affects the spectral information obtained. Furthermore, plant structure, leaf area, and leaf inclination distribution are critical indexes which have been widely used in multiple plant models. Therefore, the process of combination between hyperspectral images and 3D point clouds is a promising approach to solve these problems and improve the high-throughput phenotyping technique. We proposed a novel approach fusing a low-cost depth sensor and a close-range hyperspectral camera, which extended hyperspectral camera ability with 3D information as a potential tool for high-throughput phenotyping. An exemplary new calibration and analysis method was shown in soybean leaf experiments. The results showed that a 0.99 pixel resolution for the hyperspectral camera and a 3.3 millimeter accuracy for the depth sensor, could be achieved in a controlled environment using the method proposed in this paper. We also discussed the new capabilities gained using this new method, to quantify and model the effects of plant geometry and sensor configuration. The possibility of 3D reflectance models can be used to minimize the geometry-related effects in hyperspectral images, and to significantly improve high-throughput phenotyping. Overall results of this research, indicated that the proposed method provided more accurate spatial and spectral plant information, which helped to enhance the precision of biological processes in high-throughput phenotyping.

scholarly journals Low-Cost Automated Vectors and Modular Environmental Sensors for Plant Phenotyping

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3319
Author(s):  
Stuart A. Bagley ◽  
Jonathan A. Atkinson ◽  
Henry Hunt ◽  
Michael H. Wilson ◽  
Tony P. Pridmore ◽  
...  
Keyword(s):  
High Throughput ◽  
Low Cost ◽  
Time Lapse ◽  
Plant Phenotyping ◽  
Environmental Sensors ◽  
Controlled Conditions ◽  
Sensor Platform ◽  
High Throughput Phenotyping ◽  
Controlled Environments ◽  
Time Lapse Imaging

High-throughput plant phenotyping in controlled environments (growth chambers and glasshouses) is often delivered via large, expensive installations, leading to limited access and the increased relevance of “affordable phenotyping” solutions. We present two robot vectors for automated plant phenotyping under controlled conditions. Using 3D-printed components and readily-available hardware and electronic components, these designs are inexpensive, flexible and easily modified to multiple tasks. We present a design for a thermal imaging robot for high-precision time-lapse imaging of canopies and a Plate Imager for high-throughput phenotyping of roots and shoots of plants grown on media plates. Phenotyping in controlled conditions requires multi-position spatial and temporal monitoring of environmental conditions. We also present a low-cost sensor platform for environmental monitoring based on inexpensive sensors, microcontrollers and internet-of-things (IoT) protocols.

scholarly journals Novel 3D Imaging Systems for High-Throughput Phenotyping of Plants

2021 ◽  
Vol 13 (11) ◽  
pp. 2113
Author(s):  
Tian Gao ◽  
Feiyu Zhu ◽  
Puneet Paul ◽  
Jaspreet Sandhu ◽  
Henry Akrofi Doku ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
High Throughput ◽  
Imaging System ◽  
Point Clouds ◽  
Imaging Systems ◽  
Phenotypic Traits ◽  
Reconstruction Algorithms ◽  
High Quality ◽  
3D Point Clouds ◽  
High Throughput Phenotyping

The use of 3D plant models for high-throughput phenotyping is increasingly becoming a preferred method for many plant science researchers. Numerous camera-based imaging systems and reconstruction algorithms have been developed for the 3D reconstruction of plants. However, it is still challenging to build an imaging system with high-quality results at a low cost. Useful comparative information for existing imaging systems and their improvements is also limited, making it challenging for researchers to make data-based selections. The objective of this study is to explore the possible solutions to address these issues. We introduce two novel systems for plants of various sizes, as well as a pipeline to generate high-quality 3D point clouds and meshes. The higher accuracy and efficiency of the proposed systems make it a potentially valuable tool for enhancing high-throughput phenotyping by integrating 3D traits for increased resolution and measuring traits that are not amenable to 2D imaging approaches. The study shows that the phenotype traits derived from the 3D models are highly correlated with manually measured phenotypic traits (R2 > 0.91). Moreover, we present a systematic analysis of different settings of the imaging systems and a comparison with the traditional system, which provide recommendations for plant scientists to improve the accuracy of 3D construction. In summary, our proposed imaging systems are suggested for 3D reconstruction of plants. Moreover, the analysis results of the different settings in this paper can be used for designing new customized imaging systems and improving their accuracy.

scholarly journals Custom built scanner and simple image processing pipeline enables low-cost, high-throughput phenotyping of maize ears

2019 ◽  
Author(s):  
Cedar Warman ◽  
John E Fowler
Keyword(s):  
Image Processing ◽  
High Throughput ◽  
Low Cost ◽  
Digital Camera ◽  
Cost Effective ◽  
Source Image ◽  
Processing Pipeline ◽  
Effective Combination ◽  
Transmission Data ◽  
High Throughput Phenotyping

AbstractHigh-throughput phenotyping systems are becoming increasingly powerful, dramatically changing our ability to document, measure, and detect phenomena. Unfortunately, taking advantage of these trends can be difficult for scientists with few resources, particularly when studying nonstandard biological systems. Here, we describe a powerful, cost-effective combination of a custom-built imaging platform and open-source image processing pipeline. Our maize ear scanner was built with off-the-shelf parts for <$80. When combined with a cellphone or digital camera, videos of rotating maize ears were captured and digitally flattened into projections covering the entire surface of the ear. Segregating GFP and anthocyanin seed markers were clearly distinguishable in ear projections, allowing manual annotation using ImageJ. Using this method, statistically powerful transmission data can be collected for hundreds of maize ears, accelerating the phenotyping process.

Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform

2019 ◽  
Vol 162 ◽  
pp. 749-758 ◽  
Author(s):  
Mohd Shahrimie Mohd Asaari ◽  
Stien Mertens ◽  
Stijn Dhondt ◽  
Dirk Inzé ◽  
Nathalie Wuyts ◽  
...  
Keyword(s):  
Drought Stress ◽  
High Throughput ◽  
Hyperspectral Images ◽  
High Throughput Phenotyping ◽  
Maize Plants

scholarly journals A Low‐Cost Automated System for High‐Throughput Phenotyping of Single Oat Seeds

tppj ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 1-13 ◽  
Author(s):  
James W. Clohessy ◽  
Duke Pauli ◽  
Kevin M. Kreher ◽  
Edward S. Buckler ◽  
Paul R. Armstrong ◽  
...  
Keyword(s):  
High Throughput ◽  
Low Cost ◽  
Automated System ◽  
Oat Seeds ◽  
High Throughput Phenotyping

scholarly journals MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Sheng Wu ◽  
Weiliang Wen ◽  
Yongjian Wang ◽  
Jiangchuan Fan ◽  
Chuanyu Wang ◽  
...  
Keyword(s):  
Data Management ◽  
High Throughput ◽  
Management System ◽  
Low Cost ◽  
Point Clouds ◽  
Data Management System ◽  
Plant Phenotyping ◽  
Phenotypic Traits ◽  
High Throughput Phenotyping ◽  
Multiview Stereo

Plant phenotyping technologies play important roles in plant research and agriculture. Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to analyze the morphological differences in response to environments for crop cultivation. Accordingly, high-throughput phenotyping technologies for individual plants grown in field conditions are urgently needed, and MVS-Pheno, a portable and low-cost phenotyping platform for individual plants, was developed. The platform is composed of four major components: a semiautomatic multiview stereo (MVS) image acquisition device, a data acquisition console, data processing and phenotype extraction software for maize shoots, and a data management system. The platform’s device is detachable and adjustable according to the size of the target shoot. Image sequences for each maize shoot can be captured within 60-120 seconds, yielding 3D point clouds of shoots are reconstructed using MVS-based commercial software, and the phenotypic traits at the organ and individual plant levels are then extracted by the software. The correlation coefficient (R2) between the extracted and manually measured plant height, leaf width, and leaf area values are 0.99, 0.87, and 0.93, respectively. A data management system has also been developed to store and manage the acquired raw data, reconstructed point clouds, agronomic information, and resulting phenotypic traits. The platform offers an optional solution for high-throughput phenotyping of field-grown plants, which is especially useful for large populations or experiments across many different ecological regions.

Towards high-throughput phenotyping of complex patterned behaviors: Lessons from mouse self-grooming

2011 ◽  
Author(s):  
E. Kyzar ◽  
S. Gaikwad ◽  
M. Pham ◽  
J. Green ◽  
A. Roth ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Phenotyping

Characterization of a Polymeric Membrane for the Separation of Hydrogen in a Mixture with CO2

2016 ◽  
Vol 9 (1) ◽  
pp. 126-136 ◽  
Author(s):  
Dionisio H. Malagón-Romero ◽  
Alexander Ladino ◽  
Nataly Ortiz ◽  
Liliana P. Green
Keyword(s):  
Carbon Dioxide ◽  
Hydrogen Production ◽  
Test Performance ◽  
Low Cost ◽  
Time Lag ◽  
Polymeric Membrane ◽  
Biological Processes ◽  
Scanning Calorimetry ◽  
Environmentally Sustainable ◽  
Production Of Hydrogen

Hydrogen is expected to play an important role as a clean, reliable and renewable energy source. A key challenge is the production of hydrogen in an economically and environmentally sustainable way on an industrial scale. One promising method of hydrogen production is via biological processes using agricultural resources, where the hydrogen is found to be mixed with other gases, such as carbon dioxide. Thus, to separate hydrogen from the mixture, it is challenging to implement and evaluate a simple, low cost, reliable and efficient separation process. So, the aim of this work was to develop a polymeric membrane for hydrogen separation. The developed membranes were made of polysulfone via phase inversion by a controlled evaporation method with 5 wt % and 10 wt % of polysulfone resulting in thicknesses of 132 and 239 micrometers, respectively. Membrane characterization was performed using scanning electron microscopy (SEM), differential scanning calorimetry (DSC), atomic force microscopy (AFM), and ASTM D882 tensile test. Performance was characterized using a 23 factorial experiment using the time lag method, comparing the results with those from gas chromatography (GC). As a result, developed membranes exhibited dense microstructures, low values of RMS roughness, and glass transition temperatures of approximately 191.75 °C and 190.43 °C for the 5 wt % and 10 wt % membranes, respectively. Performance results for the given membranes showed a hydrogen selectivity of 8.20 for an evaluated gas mixture 54% hydrogen and 46% carbon dioxide. According to selectivity achieved, H2 separation from carbon dioxide is feasible with possibilities of scalability. These results are important for consolidating hydrogen production from biological processes.

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