Fluxomics links cellular functional analyses to whole-plant phenotyping

A transition from qualitative to quantitative descriptors of morphology has been facilitated through the growing field of morphometrics, representing the conversion of shapes and patterns into numbers. The analysis of plant form at the macromorphological scale using morphometric approaches quantifies what is commonly referred to as a phenotype. Quantitative phenotypic analysis of individuals with contrasting genotypes in turn provides a means to establish links between genes and shapes. The path from a gene to a morphological phenotype is, however, not direct, with instructive information progressing both across multiple scales of biological complexity and through nonintuitive feedback, such as mechanical signals. In this review, we explore morphometric approaches used to perform whole-plant phenotyping and quantitative approaches in capture processes in the mesoscales, which bridge the gaps between genes and shapes in plants. Quantitative frameworks involving both the computational simulation and the discretization of data into networks provide a putative path to predicting emergent shape from underlying genetic programs.

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PocketMaize: An Android-Smartphone Application for Maize Plant Phenotyping

Frontiers in Plant Science ◽

10.3389/fpls.2021.770217 ◽

2021 ◽

Vol 12 ◽

Author(s):

Lingbo Liu ◽

Lejun Yu ◽

Dan Wu ◽

Junli Ye ◽

Hui Feng ◽

...

Keyword(s):

Plant Traits ◽

Low Cost ◽

Leaf Traits ◽

Smartphone Application ◽

Plant Phenotyping ◽

Phenotypic Characterization ◽

Whole Plant ◽

Outdoor Environments ◽

Calibration Algorithm ◽

Trait Measurement

A low-cost portable wild phenotyping system is useful for breeders to obtain detailed phenotypic characterization to identify promising wild species. However, compared with the larger, faster, and more advanced in-laboratory phenotyping systems developed in recent years, the progress for smaller phenotyping systems, which provide fast deployment and potential for wide usage in rural and wild areas, is quite limited. In this study, we developed a portable whole-plant on-device phenotyping smartphone application running on Android that can measure up to 45 traits, including 15 plant traits, 25 leaf traits and 5 stem traits, based on images. To avoid the influence of outdoor environments, we trained a DeepLabV3+ model for segmentation. In addition, an angle calibration algorithm was also designed to reduce the error introduced by the different imaging angles. The average execution time for the analysis of a 20-million-pixel image is within 2,500 ms. The application is a portable on-device fast phenotyping platform providing methods for real-time trait measurement, which will facilitate maize phenotyping in field and benefit crop breeding in future.

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Phenotypic Analysis of Germination Time of Individual Seeds Affected by Microenvironment and Management Factors for Cohort Research in Plant Factory

Agronomy ◽

10.3390/agronomy10111680 ◽

2020 ◽

Vol 10 (11) ◽

pp. 1680

Author(s):

Eri Hayashi ◽

Yumiko Amagai ◽

Toru Maruo ◽

Toyoki Kozai

Keyword(s):

Plant Growth ◽

Growth Process ◽

Germination Percentage ◽

Plant Phenotyping ◽

Phenotypic Analysis ◽

Germination Time ◽

Commercial Cultivation ◽

Whole Plant ◽

Plant Factory ◽

Management Factors

Plant phenotyping plays a crucial role in understanding variations in the phenotype of individual plants affected by environment, management, and genotype. Measurement of seed germination is an important phenotyping stage as germination impacts on the whole plant growth process. However, germination measurement has been limited to germination percentage of a seed population. Understanding of the germination time, from sowing to outbreak of the radicle from seed coat, at a single seed level is essential. How individual germination time and further plant growth are affected by its microenvironment and management factors remains elusive. Plant phenotype measurement system was developed to assess individual germination time of romaine lettuce (Lactuca sativa L. var. longifolia), using time-series two-dimensional camera images, and to analyze how microenvironment (volumetric water percent in seed tray, individual seed surface temperature and air temperature) and management factors (coated/uncoated seeds) affect the germination time for plant cohort research, emphasizing practicality in commercial cultivation. Germination experiments were conducted to demonstrate the performance of the system and its applicability for a whole plant growth process in a plant factory for commercial production and/or breeding. The developed phenotyping platform revealed the effects of microenvironment and management factors on germination time of individual seeds.

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Automated Detection and Segmentation of Grain Spikes in Greenhouse Images Using Shallow and Deep Learning Neural Networks: A Comparison of Six Methods

10.21203/rs.3.rs-252740/v1 ◽

2021 ◽

Author(s):

Sajid Ullah ◽

Michael Henke ◽

Narendra Narisetti ◽

Jan Hejatko ◽

Evgeny Gladilin

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Image Data ◽

Automated Analysis ◽

Machine Learning Techniques ◽

Superior Performance ◽

Plant Phenotyping ◽

Learning Techniques ◽

Whole Plant ◽

Comprehensive Comparison

Abstract Image-based plant phenotyping is the major approach to quantitative assessment of important plant properties. For automated analysis of a large amount of image data from high-throughput greenhouse measurements, efficient techniques for image segmentation are required. However, conventional approaches to whole plant and plant organ segmentation are hampered by high variability of plant and background illumination, and naturally occurring changes in geometry and colors of growing plants. Consequently, application of advanced machine learning techniques for automated image segmentation is required. Here, we investigate six advanced neural network (NN) methods for detection and segmentation of grain spikes in RGB images including three detection deep NNs (SSD, Faster-RCNN, YOLOv3/v4), two deep (U-Net, DeepLabv3+) and one shallow segmentation NNs. Our experimental results show superior performance of deep learning NNs that achieve in average more than 90% accuracy by detection and segmentation of wheat as well as barley and rye spikes. However, different methods demonstrate different performance on matured, emergent and occluded spikes. In addition to comprehensive comparison of six NN methods, a GUI-based tool (SpikeApp) provided with this work demonstrates the application of detection and segmentation NNs to fully automated spike phenotyping. Further improvements of evaluated NN approaches are discussed.

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