scholarly journals Potential Phenotyping Methodologies to Assess Inter- and Intravarietal Variability and to Select Grapevine Genotypes Tolerant to Abiotic Stress

2021 ◽  
Vol 12 ◽  
Author(s):  
Luísa C. Carvalho ◽  
Elsa F. Gonçalves ◽  
Jorge Marques da Silva ◽  
J. Miguel Costa
Keyword(s):  
Abiotic Stress ◽  
Large Scale ◽  
Field Experiments ◽  
Multiple Scales ◽  
Plant Traits ◽  
Data Retrieval ◽  
Plant Phenotyping ◽  
Soil Conditions ◽  
Related Field ◽  
Field Phenotyping

Plant phenotyping is an emerging science that combines multiple methodologies and protocols to measure plant traits (e.g., growth, morphology, architecture, function, and composition) at multiple scales of organization. Manual phenotyping remains as a major bottleneck to the advance of plant and crop breeding. Such constraint fostered the development of high throughput plant phenotyping (HTPP), which is largely based on imaging approaches and automatized data retrieval and processing. Field phenotyping still poses major challenges and the progress of HTPP for field conditions can be relevant to support selection and breeding of grapevine. The aim of this review is to discuss potential and current methods to improve field phenotyping of grapevine to support characterization of inter- and intravarietal diversity. Vitis vinifera has a large genetic diversity that needs characterization, and the availability of methods to support selection of plant material (polyclonal or clonal) able to withstand abiotic stress is paramount. Besides being time consuming, complex and expensive, field experiments are also affected by heterogeneous and uncontrolled climate and soil conditions, mostly due to the large areas of the trials and to the high number of traits to be observed in a number of individuals ranging from hundreds to thousands. Therefore, adequate field experimental design and data gathering methodologies are crucial to obtain reliable data. Some of the major challenges posed to grapevine selection programs for tolerance to water and heat stress are described herein. Useful traits for selection and related field phenotyping methodologies are described and their adequacy for large scale screening is discussed.

An experiment in the control of the ground water-level in a fen peat soil

1951 ◽  
Vol 41 (1-2) ◽  
pp. 149-162 ◽  
Author(s):  
H. H. Nicholson ◽  
G. Alderman ◽  
D. H. Firth
Keyword(s):  
Ground Water ◽  
Water Level ◽  
Large Scale ◽  
Field Experiments ◽  
Peat Soil ◽  
Climatic Conditions ◽  
Ground Water Level ◽  
Effective Control ◽  
Soil Conditions ◽  
Local Soil

1. The methods of investigation of the effect of ground water-level on crop growth, together with tho field installations in use, are discussed.2. Direct field experiments are handicapped by the difficulties of achieving close control on a sufficiently large scale, due to considerable variations of surface level and depth of peat within individual fields and to rapid fluctuations in rainfall and evaporation. Many recorded experiments are associated with climatic conditions of substantial precipitation during the growing season.3. Seasonal fluctuations of ground water-level in Fen peat soils in England, in natural and agricultural conditions, are described.4. The local soil conditions are outlined and the implications of profile variations are discussed.5. The effective control of ground water-level on a field scale requires deep and commodious ditches and frequent large underdrains to ensure the movement of water underground with sufficient freedom to give rapid compensatory adjustment for marked disturbances of ground water-level following the incidence of heavy rain or excessive evaporation.6. A working installation for a field experiment in ordinary farming conditions is described and the measure of control attained is indicated.

scholarly journals Automatic Traits Extraction and Fitting for Field High-throughput Phenotyping Systems

2020 ◽  
Author(s):  
Xingche Guo ◽  
Yumou Qiu ◽  
Dan Nettleton ◽  
Cheng-Ting Yeh ◽  
Zihao Zheng ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Plant Traits ◽  
Network Models ◽  
Modern Technology ◽  
Neural Network Models ◽  
Novel Approach ◽  
Field Phenotyping ◽  
High Throughput Phenotyping ◽  
Rich Data

ABSTRACTHigh-throughput phenotyping is a modern technology to measure plant traits efficiently and in large scale by imaging systems over the whole growth season. Those images provide rich data for statistical analysis of plant phenotypes. We propose a pipeline to extract and analyze the plant traits for field phenotyping systems. The proposed pipeline include the following main steps: plant segmentation from field images, automatic calculation of plant traits from the segmented images, and functional curve fitting for the extracted traits. To deal with the challenging problem of plant segmentation for field images, we propose a novel approach on image pixel classification by transform domain neural network models, which utilizes plant pixels from greenhouse images to train a segmentation model for field images. Our results show the proposed procedure is able to accurately extract plant heights and is more stable than results from Amazon Turks, who manually measure plant heights from original images.

scholarly journals High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture

Agronomy ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 258 ◽  
Author(s):  
Aakash Chawade ◽  
Joost van Ham ◽  
Hanna Blomquist ◽  
Oscar Bagge ◽  
Erik Alexandersson ◽  
...  
Keyword(s):  
Plant Breeding ◽  
High Throughput ◽  
Precision Agriculture ◽  
Plant Traits ◽  
Early Stage ◽  
Plant Phenotyping ◽  
Plant Cultivation ◽  
Field Phenotyping ◽  
High Throughput Phenotyping ◽  
Major Attention

High-throughput field phenotyping has garnered major attention in recent years leading to the development of several new protocols for recording various plant traits of interest. Phenotyping of plants for breeding and for precision agriculture have different requirements due to different sizes of the plots and fields, differing purposes and the urgency of the action required after phenotyping. While in plant breeding phenotyping is done on several thousand small plots mainly to evaluate them for various traits, in plant cultivation, phenotyping is done in large fields to detect the occurrence of plant stresses and weeds at an early stage. The aim of this review is to highlight how various high-throughput phenotyping methods are used for plant breeding and farming and the key differences in the applications of such methods. Thus, various techniques for plant phenotyping are presented together with applications of these techniques for breeding and cultivation. Several examples from the literature using these techniques are summarized and the key technical aspects are highlighted.

scholarly journals Large-scale field phenotyping using backpack LiDAR and GUI-based CropQuant-3D to measure structural responses to different nitrogen treatments in wheat

2021 ◽  
Author(s):  
Yulei Zhu ◽  
Gang Sun ◽  
Guohui Ding ◽  
Jie Zhou ◽  
Mingxing Wen ◽  
...  
Keyword(s):  
Large Scale ◽  
Structural Changes ◽  
Field Experiments ◽  
Computing Time ◽  
Point Clouds ◽  
Combined System ◽  
Wheat Varieties ◽  
Wide Range ◽  
Field Phenotyping ◽  
Nitrogen Treatments

Plant phenomics is widely recognised as a key area to bridge the gap between traits of agricultural importance and genomic information. A wide range of field-based phenotyping solutions have been developed. Nevertheless, disadvantages of these current systems have been identified concerning mobility, affordability, accuracy, scalability, and the ability to analyse big data collected. Here, we present a novel solution that combines a commercial backpack LiDAR device and graphical user interface (GUI) based software called CropQuant-3D, which has been applied to analyse 3D morphological traits in wheat. To our knowledge, this is the first use of backpack LiDAR in field-based plant research, acquiring millions of 3D points to represent spatial features of crops. A key part of the innovation is the GUI-based software that can extract plot-based traits from large and complex point clouds with limited computing time. We describe how we developed and used the combined system to quantify canopy structural changes, impossible to measure previously. Also, we demonstrate the biological relevance of our work through a case study that examined wheat varieties to three different levels of nitrogen fertilisation in field experiments. The results indicate that the solution can differentiate significant genotype and treatment effects on key traits, with strong correlations with manual measurements. Hence, we believe that the solution presented here could consistently and speedily quantify traits at a larger scale, indicating the system could be used as a reliable research tool in large-scale and multi-location field phenotyping to contribute to the resolution of the phenotyping bottleneck.

scholarly journals Review: Application of Artificial Intelligence in Phenomics

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4363
Author(s):  
Shona Nabwire ◽  
Hyun-Kwon Suh ◽  
Moon S. Kim ◽  
Insuck Baek ◽  
Byoung-Kwan Cho
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Data Collection ◽  
New Technologies ◽  
Plant Traits ◽  
Imaging Techniques ◽  
Plant Phenotyping ◽  
Plant Phenomics ◽  
Field Phenotyping ◽  
High Throughput Phenotyping

Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which can enable the high-throughput phenotyping of complex plant traits. The application of artificial intelligence in various domains of science has also grown exponentially in recent years. Notably, the computer vision, machine learning, and deep learning aspects of artificial intelligence have been successfully integrated into non-invasive imaging techniques. This integration is gradually improving the efficiency of data collection and analysis through the application of machine and deep learning for robust image analysis. In addition, artificial intelligence has fostered the development of software and tools applied in field phenotyping for data collection and management. These include open-source devices and tools which are enabling community driven research and data-sharing, thereby availing the large amounts of data required for the accurate study of phenotypes. This paper reviews more than one hundred current state-of-the-art papers concerning AI-applied plant phenotyping published between 2010 and 2020. It provides an overview of current phenotyping technologies and the ongoing integration of artificial intelligence into plant phenotyping. Lastly, the limitations of the current approaches/methods and future directions are discussed.

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Large-scale transcriptomics to dissect 2 years of the life of a fungal phytopathogen interacting with its host plant

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Elise J. Gay ◽  
Jessica L. Soyer ◽  
Nicolas Lapalu ◽  
Juliette Linglin ◽  
Isabelle Fudal ◽  
...  
Keyword(s):  
Host Plant ◽  
Large Scale ◽  
Field Experiments ◽  
Plant Residues ◽  
Plant Infection ◽  
Effector Genes ◽  
Controlled Conditions ◽  
Niche Adaptation ◽  
Depth Analysis ◽  
Fungal Genes

Abstract Background The fungus Leptosphaeria maculans has an exceptionally long and complex relationship with its host plant, Brassica napus, during which it switches between different lifestyles, including asymptomatic, biotrophic, necrotrophic, and saprotrophic stages. The fungus is also exemplary of “two-speed” genome organisms in the genome of which gene-rich and repeat-rich regions alternate. Except for a few stages of plant infection under controlled conditions, nothing is known about the genes mobilized by the fungus throughout its life cycle, which may last several years in the field. Results We performed RNA-seq on samples corresponding to all stages of the interaction of L. maculans with its host plant, either alive or dead (stem residues after harvest) in controlled conditions or in field experiments under natural inoculum pressure, over periods of time ranging from a few days to months or years. A total of 102 biological samples corresponding to 37 sets of conditions were analyzed. We show here that about 9% of the genes of this fungus are highly expressed during its interactions with its host plant. These genes are distributed into eight well-defined expression clusters, corresponding to specific infection lifestyles or to tissue-specific genes. All expression clusters are enriched in effector genes, and one cluster is specific to the saprophytic lifestyle on plant residues. One cluster, including genes known to be involved in the first phase of asymptomatic fungal growth in leaves, is re-used at each asymptomatic growth stage, regardless of the type of organ infected. The expression of the genes of this cluster is repeatedly turned on and off during infection. Whatever their expression profile, the genes of these clusters are enriched in heterochromatin regions associated with H3K9me3 or H3K27me3 repressive marks. These findings provide support for the hypothesis that part of the fungal genes involved in niche adaptation is located in heterochromatic regions of the genome, conferring an extreme plasticity of expression. Conclusion This work opens up new avenues for plant disease control, by identifying stage-specific effectors that could be used as targets for the identification of novel durable disease resistance genes, or for the in-depth analysis of chromatin remodeling during plant infection, which could be manipulated to interfere with the global expression of effector genes at crucial stages of plant infection.

scholarly journals High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops

2021 ◽  
Vol 22 (15) ◽  
pp. 8266
Author(s):  
Minsu Kim ◽  
Chaewon Lee ◽  
Subin Hong ◽  
Song Lim Kim ◽  
Jeong-Ho Baek ◽  
...  
Keyword(s):  
Drought Stress ◽  
High Throughput ◽  
Large Scale ◽  
Crop Yields ◽  
Plant Traits ◽  
Rapid Development ◽  
Plant Responses ◽  
Phenotypic Data ◽  
Agricultural Technologies ◽  
High Throughput Phenotyping

Drought is a main factor limiting crop yields. Modern agricultural technologies such as irrigation systems, ground mulching, and rainwater storage can prevent drought, but these are only temporary solutions. Understanding the physiological, biochemical, and molecular reactions of plants to drought stress is therefore urgent. The recent rapid development of genomics tools has led to an increasing interest in phenomics, i.e., the study of phenotypic plant traits. Among phenomic strategies, high-throughput phenotyping (HTP) is attracting increasing attention as a way to address the bottlenecks of genomic and phenomic studies. HTP provides researchers a non-destructive and non-invasive method yet accurate in analyzing large-scale phenotypic data. This review describes plant responses to drought stress and introduces HTP methods that can detect changes in plant phenotypes in response to drought.

scholarly journals Assessing the Potential of Extra-Early Maturing Landraces for Improving Tolerance to Drought, Heat, and Both Combined Stresses in Maize

Agronomy ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 318 ◽  
Author(s):  
Charles Nelimor ◽  
Baffour Badu-Apraku ◽  
Antonia Yarney Tetteh ◽  
Ana Luísa Garcia-Oliveira ◽  
Assanvo Simon-Pierre N’guetta
Keyword(s):  
Heat Stress ◽  
Abiotic Stress ◽  
Field Experiments ◽  
Yield Loss ◽  
Yield Potential ◽  
Genetic Base ◽  
Maize Varieties ◽  
Early Maturing ◽  
Maize Landraces ◽  
Landrace Accessions

Maize landrace accessions constitute an invaluable gene pool of unexplored alleles that can be harnessed to mitigate the challenges of the narrowing genetic base, declined genetic gains, and reduced resilience to abiotic stress in modern varieties developed from repeated recycling of few superior breeding lines. The objective of this study was to identify extra-early maize landraces that express tolerance to drought and/or heat stress and maintain high grain yield (GY) with other desirable agronomic/morpho-physiological traits. Field experiments were carried out over two years on 66 extra-early maturing maize landraces and six drought and/or heat-tolerant populations under drought stress (DS), heat stress (HS), combined both stresses (DSHS), and non-stress (NS) conditions as a control. Wide variations were observed across the accessions for measured traits under each stress, demonstrating the existence of substantial natural variation for tolerance to the abiotic stresses in the maize accessions. Performance under DS was predictive of yield potential under DSHS, but tolerance to HS was independent of tolerance to DS and DSHS. The accessions displayed greater tolerance to HS (23% yield loss) relative to DS (49% yield loss) and DSHS (yield loss = 58%). Accessions TZm-1162, TZm-1167, TZm-1472, and TZm-1508 showed particularly good adaptation to the three stresses. These landrace accessions should be further explored to identify the genes underlying their high tolerance and they could be exploited in maize breeding as a resource for broadening the genetic base and increasing the abiotic stress resilience of elite maize varieties.

Development and Testing of an Unmanned Aerial Vehicle for Large Scale Particle Image Velocimetry

2014 ◽  
Author(s):  
Christopher Pagano ◽  
Flavia Tauro ◽  
Salvatore Grimaldi ◽  
Maurizio Porfiri
Keyword(s):  
Particle Image Velocimetry ◽  
Large Scale ◽  
Field Experiments ◽  
Particle Image ◽  
Low Cost ◽  
Surface Flow ◽  
Natural Environments ◽  
Natural Stream ◽  
Image Velocimetry ◽  
Scale Particle

Large scale particle image velocimetry (LSPIV) is a nonintrusive environmental monitoring methodology that allows for continuous characterization of surface flows in natural catchments. Despite its promise, the implementation of LSPIV in natural environments is limited to areas accessible to human operators. In this work, we propose a novel experimental configuration that allows for unsupervised LSPIV over large water bodies. Specifically, we design, develop, and characterize a lightweight, low cost, and stable quadricopter hosting a digital acquisition system. An active gimbal maintains the camera lens orthogonal to the water surface, thus preventing severe image distortions. Field experiments are performed to characterize the vehicle and assess the feasibility of the approach. We demonstrate that the quadricopter can hover above an area of 1×1m2 for 4–5 minutes with a payload of 500g. Further, LSPIV measurements on a natural stream confirm that the methodology can be reliably used for surface flow studies.

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