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 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.

scholarly journals Exploiting open source 3D printer architecture for laboratory robotics to automate high-throughput time-lapse imaging for analytical microbiology

PLoS ONE ◽  
2019 ◽  
Vol 14 (11) ◽  
pp. e0224878 ◽  
Author(s):  
Sarah H. Needs ◽  
Tai The Diep ◽  
Stephanie P. Bull ◽  
Anton Lindley-Decaire ◽  
Partha Ray ◽  
...  
Keyword(s):  
Open Source ◽  
High Throughput ◽  
Time Lapse ◽  
3D Printer ◽  
Throughput Time ◽  
Time Lapse Imaging

scholarly journals Automated profiling of individual cell–cell interactions from high-throughput time-lapse imaging microscopy in nanowell grids (TIMING)

2015 ◽  
Vol 31 (19) ◽  
pp. 3189-3197 ◽  
Author(s):  
Amine Merouane ◽  
Nicolas Rey-Villamizar ◽  
Yanbin Lu ◽  
Ivan Liadi ◽  
Gabrielle Romain ◽  
...  
Keyword(s):  
High Throughput ◽  
Individual Cell ◽  
Time Lapse ◽  
Cell Interactions ◽  
Throughput Time ◽  
Time Lapse Imaging ◽  
Cell Cell

scholarly journals Time-lapse imaging of molecular evolution by high-throughput sequencing

2018 ◽  
Vol 46 (15) ◽  
pp. 7480-7494 ◽  
Author(s):  
Nam Nguyen Quang ◽  
Clément Bouvier ◽  
Adrien Henriques ◽  
Benoit Lelandais ◽  
Frédéric Ducongé
Keyword(s):  
Molecular Evolution ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Time Lapse ◽  
Time Lapse Imaging

scholarly journals Predicting plant biomass accumulation from image-derived parameters

2016 ◽  
Author(s):  
Dijun Chen ◽  
Rongli Shi ◽  
Jean-Michel Pape ◽  
Christian Klukas
Keyword(s):  
High Throughput ◽  
Plant Biomass ◽  
Quantitative Relationship ◽  
Biomass Accumulation ◽  
Plant Phenotyping ◽  
Suitable Model ◽  
The European Union ◽  
Relative Contribution ◽  
Crop Plant Research ◽  
High Throughput Phenotyping

AbstractImage-based high-throughput phenotyping technologies have been rapidly developed in plant science recently and they provide a great potential to gain more valuable information than traditionally destructive methods. Predicting plant biomass is regarded as a key purpose for plant breeders and ecologist. However, it is a great challenge to find a suitable model to predict plant biomass in the context of high-throughput phenotyping. In the present study, we constructed several models to examine the quantitative relationship between image-based features and plant biomass accumulation. Our methodology has been applied to three consecutive barley experiments with control and stress treatments. The results proved that plant biomass can be accurately predicted from image-based parameters using a random forest model. The high prediction accuracy based on this model, in particular the cross-experiment performance, is promising to relieve the phenotyping bottleneck in biomass measurement in breeding applications. The relative contribution of individual features for predicting biomass was further quantified, revealing new insights into the phenotypic determinants of plant biomass outcome. What’s more, the methods could also be used to determine the most important image-based features related to plant biomass accumulation, which would be promising for subsequent genetic mapping to uncover the genetic basis of biomass.One-sentence SummaryWe demonstrated that plant biomass can be accurately predicted from image-based parameters in the context of high-throughput phenotyping.FootnotesThis work was supported by the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), the Robert Bosch Stiftung (32.5.8003.0116.0) and the Federal Agency for Agriculture and Food (BEL, 15/12-13, 530-06.01-BiKo CHN) and the Federal Ministry of Education and Research (BMBF, 0315958A and 031A053B). This research was furthermore enabled with support of the European Plant Phenotyping Network (EPPN, grant agreement no. 284443) funded by the FP7 Research Infrastructures Programme of the European Union.

scholarly journals High-throughput live-imaging of embryos in microwell arrays using a modular, inexpensive specimen mounting system

2017 ◽  
Author(s):  
Seth Donoughe ◽  
Chiyoung Kim ◽  
Cassandra G. Extavour
Keyword(s):  
High Throughput ◽  
Fruit Fly ◽  
Time Lapse ◽  
Live Imaging ◽  
Model Systems ◽  
Wide Range ◽  
Amphipod Crustacean ◽  
Live Image ◽  
Time Lapse Imaging ◽  
Annelid Worm

AbstractLive-imaging embryos in a high-throughput manner is essential for shedding light on a wide range of questions in developmental biology, but it is difficult and costly to mount and image embryos in consistent conditions. Here, we present OMMAwell, a simple, reusable device that makes it easy to mount up to hundreds of embryos in arrays of agarose microwells with customizable dimensions and spacing. OMMAwell can be configured to mount specimens for upright or inverted microscopes, and includes a reservoir to hold live-imaging medium to maintain constant moisture and osmolarity of specimens during time-lapse imaging. All device components can be cut from a sheet of acrylic using a laser cutter. Even a novice user will be able to cut the pieces and assemble the device in less than an hour. At the time of writing, the total materials cost is less than five US dollars. We include all device design files in a commonly used format, as well as complete instructions for its fabrication and use. We demonstrate a detailed workflow for designing a custom mold and employing it to simultaneously live-image dozens of embryos at a time for more than five days, using embryos of the cricket Gryllus bimaculatus as an example. Further, we include descriptions, schematics, and design files for molds that can be used with 14 additional vertebrate and invertebrate species, including most major traditional laboratory models and a number of emerging model systems. Molds have been user-tested for embryos including zebrafish (Danio rerio), fruit fly (Drosophila melanogaster), coqui frog (Eleutherodactylus coqui), annelid worm (Capitella teleta), amphipod crustacean (Parhyale hawaiensis), red flour beetle (Tribolium castaneum), and three-banded panther worm (Hofstenia miamia), as well mouse organoids (Mus musculus). Finally, we provide instructions for researchers to customize OMMAwell inserts for embryos or tissues not described herein.Summary StatementThis Techniques and Resources article describes an inexpensive, customizable device for mounting and live-imaging a wide range of tissues and species; complete design files and instructions for assembly are included.

scholarly journals High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field

2021 ◽  
Vol 8 ◽  
Author(s):  
Daoliang Li ◽  
Chaoqun Quan ◽  
Zhaoyang Song ◽  
Xiang Li ◽  
Guanghui Yu ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Agronomic Traits ◽  
Global Climate ◽  
Plant Phenotyping ◽  
Phenotypic Data ◽  
Operating Modes ◽  
Sensing Platform ◽  
Exploitation And Utilization ◽  
High Throughput Phenotyping

Food scarcity, population growth, and global climate change have propelled crop yield growth driven by high-throughput phenotyping into the era of big data. However, access to large-scale phenotypic data has now become a critical barrier that phenomics urgently must overcome. Fortunately, the high-throughput plant phenotyping platform (HT3P), employing advanced sensors and data collection systems, can take full advantage of non-destructive and high-throughput methods to monitor, quantify, and evaluate specific phenotypes for large-scale agricultural experiments, and it can effectively perform phenotypic tasks that traditional phenotyping could not do. In this way, HT3Ps are novel and powerful tools, for which various commercial, customized, and even self-developed ones have been recently introduced in rising numbers. Here, we review these HT3Ps in nearly 7 years from greenhouses and growth chambers to the field, and from ground-based proximal phenotyping to aerial large-scale remote sensing. Platform configurations, novelties, operating modes, current developments, as well the strengths and weaknesses of diverse types of HT3Ps are thoroughly and clearly described. Then, miscellaneous combinations of HT3Ps for comparative validation and comprehensive analysis are systematically present, for the first time. Finally, we consider current phenotypic challenges and provide fresh perspectives on future development trends of HT3Ps. This review aims to provide ideas, thoughts, and insights for the optimal selection, exploitation, and utilization of HT3Ps, and thereby pave the way to break through current phenotyping bottlenecks in botany.

scholarly journals Conventional and hyperspectral time-series imaging of maize lines widely used in field trials

2017 ◽  
Author(s):  
Zhikai Liang ◽  
Piyush Pandey ◽  
Vincent Stoerger ◽  
Yuhang Xu ◽  
Yumou Qiu ◽  
...  
Keyword(s):  
High Throughput ◽  
Measurement Errors ◽  
Hyperspectral Image ◽  
Image Data ◽  
Field Trials ◽  
Plant Phenotyping ◽  
Wide Range ◽  
High Throughput Phenotyping ◽  
University Of Nebraska Lincoln ◽  
The Cost

ABSTRACTMaize (Zea mays ssp. mays) is one of three crops, along with rice and wheat, responsible for more than 1/2 of all calories consumed around the world. Increasing the yield and stress tolerance of these crops is essential to meet the growing need for food. The cost and speed of plant phenotyping is currently the largest constraint on plant breeding efforts. Datasets linking new types of high throughput phenotyping data collected from plants to the performance of the same genotypes under agronomic conditions across a wide range of environments are essential for developing new statistical approaches and computer vision based tools. A set of maize inbreds – primarily recently off patent lines – were phenotyped using a high throughput platform at University of Nebraska-Lincoln. These lines have been previously subjected to high density genotyping, and scored for a core set of 13 phenotypes in field trials across 13 North American states in two years by the Genomes to Fields consortium. A total of 485 GB of image data including RGB, hyperspectral, fluorescence and thermal infrared photos has been released. Correlations between image-based measurements and manual measurements demonstrated the feasibility of quantifying variation in plant architecture using image data. However, naive approaches to measuring traits such as biomass can introduce nonrandom measurement errors confounded with genotype variation. Analysis of hyperspectral image data demonstrated unique signatures from stem tissue. Integrating heritable phenotypes from high-throughput phenotyping data with field data from different environments can reveal previously unknown factors influencing yield plasticity.

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