Predicting plant biomass accumulation from image-derived parameters

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.

Download Full-text

Digital Biomass Accumulation Using High-Throughput Plant Phenotype Data Analysis

Journal of Integrative Bioinformatics ◽

10.1515/jib-2017-0028 ◽

2017 ◽

Vol 14 (3) ◽

Cited By ~ 4

Author(s):

Md. Matiur Rahaman ◽

Md. Asif Ahsan ◽

Zeeshan Gillani ◽

Ming Chen

Keyword(s):

High Throughput ◽

Plant Biomass ◽

Biomass Accumulation ◽

Repeated Measurements ◽

Functional Ecology ◽

Plant Phenotyping ◽

Biomass Estimation ◽

Phenotypic Trait ◽

Phenotypic Traits ◽

Plant Area

AbstractBiomass is an important phenotypic trait in functional ecology and growth analysis. The typical methods for measuring biomass are destructive, and they require numerous individuals to be cultivated for repeated measurements. With the advent of image-based high-throughput plant phenotyping facilities, non-destructive biomass measuring methods have attempted to overcome this problem. Thus, the estimation of plant biomass of individual plants from their digital images is becoming more important. In this paper, we propose an approach to biomass estimation based on image derived phenotypic traits. Several image-based biomass studies state that the estimation of plant biomass is only a linear function of the projected plant area in images. However, we modeled the plant volume as a function of plant area, plant compactness, and plant age to generalize the linear biomass model. The obtained results confirm the proposed model and can explain most of the observed variance during image-derived biomass estimation. Moreover, a small difference was observed between actual and estimated digital biomass, which indicates that our proposed approach can be used to estimate digital biomass accurately.

Download Full-text

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

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2020.623705 ◽

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.

Download Full-text

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

10.1101/169045 ◽

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.

Download Full-text

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

Sensors ◽

10.3390/s20113319 ◽

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.

Download Full-text

Using High-Throughput Phenotyping to Explore Growth Responses to Mycorrhizal Fungi and Zinc in Three Plant Species

Plant Phenomics ◽

10.34133/2019/5893953 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 8

Author(s):

S. J. Watts-Williams ◽

N. Jewell ◽

C. Brien ◽

B. Berger ◽

T. Garnett ◽

...

Keyword(s):

Plant Species ◽

High Throughput ◽

Mycorrhizal Fungi ◽

Repeated Measures ◽

Plant Biomass ◽

Shoot Biomass ◽

Growth Responses ◽

High Throughput Phenotyping ◽

Soil Zn ◽

Amf Inoculation

There are many reported benefits to plants of arbuscular mycorrhizal fungi (AMF), including positive plant biomass responses; however, AMF can also induce biomass depressions in plants, and this response receives little attention in the literature. High-throughput phenotyping (HTP) technology permits repeated measures of an individual plant’s aboveground biomass. We examined the effect on AMF inoculation on the shoot biomass of three contrasting plant species: a vegetable crop (tomato), a cereal crop (barley), and a pasture legume (Medicago). We also considered the interaction of mycorrhizal growth responses with plant-available soil zinc (Zn) and phosphorus (P) concentrations. The appearance of a depression in shoot biomass due to inoculation with AMF occurred at different times for each plant species; depressions appeared earliest in tomato, then Medicago, and then barley. The usually positive-responding Medicago plants were not responsive at the high level of soil available P used. Mycorrhizal growth responsiveness in all three species was also highly interactive with soil Zn supply; tomato growth responded negatively to AMF inoculation in all soil Zn treatments except the toxic soil Zn treatment, where it responded positively. Our results illustrate how context-dependent mycorrhizal growth responses are and the value of HTP approaches to exploring the complexity of mycorrhizal responses.

Download Full-text

An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping

PeerJ ◽

10.7717/peerj.5727 ◽

2018 ◽

Vol 6 ◽

pp. e5727 ◽

Cited By ~ 8

Author(s):

Jeffrey C. Berry ◽

Noah Fahlgren ◽

Alexandria A. Pokorny ◽

Rebecca S. Bart ◽

Kira M. Veley

Keyword(s):

High Throughput ◽

Linear Models ◽

Correction Method ◽

Automated Image Analysis ◽

Plant Phenotyping ◽

Image Brightness ◽

Automated Method ◽

Color Profile ◽

High Throughput Phenotyping ◽

High Throughput Imaging

High-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurements within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV.

Download Full-text

Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding

Remote Sensing ◽

10.3390/rs13142670 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2670

Author(s):

Paul Herzig ◽

Peter Borrmann ◽

Uwe Knauer ◽

Hans-Christian Klück ◽

David Kilias ◽

...

Keyword(s):

Grain Yield ◽

Plant Breeding ◽

High Throughput ◽

Prediction Accuracy ◽

Vegetation Period ◽

Plant Phenotyping ◽

Yield Prediction ◽

Sensor Systems ◽

Barley Breeding ◽

High Throughput Phenotyping

With advances in plant genomics, plant phenotyping has become a new bottleneck in plant breeding and the need for reliable high-throughput plant phenotyping techniques has emerged. In the face of future climatic challenges, it does not seem appropriate to continue to solely select for grain yield and a few agronomically important traits. Therefore, new sensor-based high-throughput phenotyping has been increasingly used in plant breeding research, with the potential to provide non-destructive, objective and continuous plant characterization that reveals the formation of the final grain yield and provides insights into the physiology of the plant during the growth phase. In this context, we present the comparison of two sensor systems, Red-Green-Blue (RGB) and multispectral cameras, attached to unmanned aerial vehicles (UAV), and investigate their suitability for yield prediction using different modelling approaches in a segregating barley introgression population at three environments with weekly data collection during the entire vegetation period. In addition to vegetation indices, morphological traits such as canopy height, vegetation cover and growth dynamics traits were used for yield prediction. Repeatability analyses and genotype association studies of sensor-based traits were compared with reference values from ground-based phenotyping to test the use of conventional and new traits for barley breeding. The relative height estimation of the canopy by UAV achieved high precision (up to r = 0.93) and repeatability (up to R2 = 0.98). In addition, we found a great overlap of detected significant genotypes between the reference heights and sensor-based heights. The yield prediction accuracy of both sensor systems was at the same level and reached a maximum prediction accuracy of r2 = 0.82 with a continuous increase in precision throughout the entire vegetation period. Due to the lower costs and the consumer-friendly handling of image acquisition and processing, the RGB imagery seems to be more suitable for yield prediction in this study.

Download Full-text

Using High-Throughput Phenotyping to Explore Growth Responses to Mycorrhizal Fungi and Zinc in Three Plant Species

Plant Phenomics ◽

10.1155/2019/5893953 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12

Author(s):

S. J. Watts-Williams ◽

N. Jewell ◽

C. Brien ◽

B. Berger ◽

T. Garnett ◽

...

Keyword(s):

Plant Species ◽

High Throughput ◽

Mycorrhizal Fungi ◽

Repeated Measures ◽

Plant Biomass ◽

Shoot Biomass ◽

Growth Responses ◽

High Throughput Phenotyping ◽

Soil Zn ◽

Amf Inoculation

Download Full-text

High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat

10.1101/527911 ◽

2019 ◽

Cited By ~ 3

Author(s):

Xu Wang ◽

Hong Xuan ◽

Byron Evers ◽

Sandesh Shrestha ◽

Robert Pless ◽

...

Keyword(s):

Deep Learning ◽

Flowering Time ◽

High Throughput ◽

Developmental Stages ◽

Expert Knowledge ◽

Plant Traits ◽

Plant Morphology ◽

Ground Truth ◽

Plant Phenotyping ◽

High Throughput Phenotyping

ABSTRACTBackgroundPrecise measurement of plant traits with precision and speed on large populations has emerged as a critical bottleneck in connecting genotype to phenotype in genetics and breeding. This bottleneck limits advancements in understanding plant genomes and the development of improved, high-yielding crop varieties.ResultsHere we demonstrate the application of deep learning on proximal imaging from a mobile field vehicle to directly score plant morphology and developmental stages in wheat under field conditions. We developed and trained a convolutional neural network with image datasets labeled from expert visual scores and used this ‘breeder-trained’ network to directly score wheat morphology and developmental stages. For both morphological (awned) and phenological (flowering time) traits, we demonstrate high heritability and extremely high accuracy against the ‘ground-truth’ values from visual scoring. Using the traits scored by the network, we tested genotype-to-phenotype association using the deep learning phenotypes and uncovered novel epistatic interactions for flowering time. Enabled by the time-series high-throughput phenotyping, we describe a new phenotype as the rate of flowering and show heritable genetic control.ConclusionsWe demonstrated a field-based high-throughput phenotyping approach using deep learning that can directly score morphological and developmental phenotypes in genetic populations. Most powerfully, the deep learning approach presented here gives a conceptual advancement in high-throughput plant phenotyping as it can potentially score any trait in any plant species through leveraging expert knowledge from breeders, geneticist, pathologists and physiologists.

Download Full-text

Issues of under-representation in quantitative DNA metabarcoding weaken the inference about diet of the tundra vole Microtus oeconomus

PeerJ ◽

10.7717/peerj.11936 ◽

2021 ◽

Vol 9 ◽

pp. e11936

Author(s):

Magne Neby ◽

Stefaniya Kamenova ◽

Olivier Devineau ◽

Rolf A. Ims ◽

Eeva M. Soininen

Keyword(s):

High Throughput ◽

Dna Sequences ◽

High Throughput Sequencing ◽

Plant Biomass ◽

Quantitative Relationship ◽

Food Plants ◽

Small Rodents ◽

Microtus Oeconomus ◽

Dna Metabarcoding ◽

Tundra Vole

During the last decade, methods based on high-throughput sequencing such as DNA metabarcoding have opened up for a range of new questions in animal dietary studies. One of the major advantages of dietary metabarcoding resides in the potential to infer a quantitative relationship between sequence read proportions and biomass of ingested food. However, this relationship’s robustness is highly dependent on the system under study, calling for case-specific assessments. Herbivorous small rodents often play important roles in the ecosystem, and the use of DNA metabarcoding for analyses of rodent diets is increasing. However, there has been no direct validation of the quantitative reliability of DNA metabarcoding for small rodents. Therefore, we used an experimental approach to assess the relationship between input plant biomass and sequence reads proportions from DNA metabarcoding in the tundra vole Microtus oeconomus. We found a weakly positive relationship between the number of high-throughput DNA sequences and the expected biomass proportions of food plants. The weak relationship was possibly caused by a systematic under-amplification of one of the three plant taxa fed. Generally, our results add to the growing evidence that case-specific validation studies are required to reliably make use of sequence read abundance as a proxy of relative food proportions in the diet.

Download Full-text