The tassel of the maize plant is responsible for the production and dispersal of pollen for subsequent capture by the silk (stigma) and fertilization of the ovules. Both the amount and timing of pollen shed are physiological traits that impact the production of a hybrid seed. This study describes an automated end-to-end pipeline that combines deep learning and image processing approaches to extract tassel flowering patterns from time-lapse camera images of plants grown under field conditions. Inbred lines from the SAM and NAM diversity panels were grown at the Curtiss farm at Iowa State University, Ames, IA, during the summer of 2016. Using a set of around 500 pole-mounted cameras installed in the field, images of plants were captured every 10 minutes of daylight hours over a three-week period. Extracting data from imaging performed under field conditions is challenging due to variabilities in weather, illumination, and the morphological diversity of tassels. To address these issues, deep learning algorithms were used for tassel detection, classification, and segmentation. Image processing approaches were then used to crop the main spike of the tassel to track reproductive development. The results demonstrated that deep learning with well-labeled data is a powerful tool for detecting, classifying, and segmenting tassels. Our sequential workflow exhibited the following metrics: mAP for tassel detection was 0.91, F1 score obtained for tassel classification was 0.93, and accuracy of semantic segmentation in creating a binary image from the RGB tassel images was 0.95. This workflow was used to determine spatiotemporal variations in the thickness of the main spike&#x2014;which serves as a proxy for anthesis progression.

Seyed Vahid Mirnezami

Srikant Srinivasan

Yan Zhou

Patrick S. Schnable

Baskar Ganapathysubramanian

Detection of the Progression of Anthesis in Field-Grown Maize Tassels: A Case Study

High throughput phenotyping is important to bridge the gap between genotype and phenotype. The methods used to describe the phenotype therefore should be robust to measurement errors, relatively stable over time, and most importantly, provide a reliable estimate of elementary phenotypic components. In this study, we use functional-structural modeling to evaluate quantitative phenotypic metrics used to describe root architecture to determine how they fit these criteria. Our results show that phenes such as root number, root diameter, and lateral root branching density are stable, reliable measures and are not affected by imaging method or plane. Metrics aggregating multiple phenes such as total length, total volume, convex hull volume, and bushiness index estimate different subsets of the constituent phenes; they however do not provide any information regarding the underlying phene states. Estimates of phene aggregates are not unique representations of underlying constituent phenes: multiple phenotypes having phenes in different states could have similar aggregate metrics. Root growth angle is an important phene which is susceptible to measurement errors when 2D projection methods are used. Metrics that aggregate phenes which are complex functions of root growth angle and other phenes are also subject to measurement errors when 2D projection methods are used. These results support the hypothesis that estimates of phenes are more useful than metrics aggregating multiple phenes for phenotyping root architecture. We propose that these concepts are broadly applicable in phenotyping and phenomics.

Harini Rangarajan

Jonathan P. Lynch

A Comparative Analysis of Quantitative Metrics of Root Architecture

Background. Computed X-ray tomography (CTX) is a high-end nondestructive approach for the visual assessment of root architecture in soil. Nevertheless, in order to evaluate high-resolution CTX data of root architectures, manual segmentation of the depicted root systems from large-scale volume data is currently necessary, which is both time consuming and error prone. The duration of such a segmentation is of importance, especially for time-resolved growth analysis, where several instances of a plant need to be segmented and evaluated. Specifically, in our application, the contrast between soil and root data varies due to different growth stages and watering situations at the time of scanning. Additionally, the root system itself is expanding in length and in the diameter of individual roots. Objective. For semiautomated and robust root system segmentation from CTX data, we propose the RootForce approach, which is an extension of Frangi’s “multi-scale vesselness” method and integrates a 3D local variance. It allows a precise delineation of roots with diameters down to several μm in pots with varying diameters. Additionally, RootForce is not limited to the segmentation of small below-ground organs, but is also able to handle storage roots with a diameter larger than 40 voxels. Results. Using CTX volume data of full-grown bean plants as well as time-resolved (<svg xmlns="http://www.w3.org/2000/svg" id="M1" style="vertical-align:-0.6075296pt" height="9.3427pt" version="1.1" viewBox="-0.0498162 -8.73517 51.8539 9.3427" width="51.8539pt">


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</svg>) growth studies of cassava plants, RootForce produces similar (and much faster) results compared to manual segmentation of the regarded root architectures. Furthermore, RootForce enables the user to obtain traits not possible to be calculated before, such as total root volume (<svg xmlns="http://www.w3.org/2000/svg" id="M2" style="vertical-align:-3.25759pt" height="11.5729pt" version="1.1" viewBox="-0.0498162 -8.31531 22.8048 11.5729" width="22.8048pt">


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</svg>, or form fraction <svg xmlns="http://www.w3.org/2000/svg" id="M8" style="vertical-align:-0.0498209pt" height="8.36513pt" version="1.1" viewBox="-0.0498162 -8.31531 9.11084 8.36513" width="9.11084pt">


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</svg>. Discussion. The proposed RootForce tool can provide a higher efficiency for the semiautomatic high-throughput assessment of the root architectures of different types of plants from large-scale CTX. Furthermore, for all datasets within a growth experiment, only a single set of parameters is needed. Thus, the proposed tool can be used for a wide range of growth experiments in the field of plant phenotyping.

Stefan Gerth

Joelle Clau&#x00DF;en

Anja Eggert

Norbert W&#x00F6;rlein

Michael Waininger

Thomas Wittenberg

Norman Uhlmann

Semiautomated 3D Root Segmentation and Evaluation Based on X-Ray CT Imagery

Consumers hesitate to purchase field-grown shoot-tops of amaranths in Sri Lanka, citing the low-cleanliness making growers focus on greenhouse farming. However, the photosynthetic and growth variations in relation to the organoleptic preference of the greenhouse-grown amaranths in comparison to field-grown counterparts have not been studied. Also, the species delimits of the amaranths in Sri Lanka have not been identified, limiting our ability to interpret species-specific production characteristics. Thus, we assessed the common types of amaranths under greenhouse and field conditions. The photosynthesis was measured using a MultispeQ device of the PhotosynQ phenomic platform, which records chlorophyll fluorescence-based parameters. The shoot-tops were harvested and prepared as dishes according to the typical recipe for amaranths in Sri Lanka. The dishes were subjected to an organoleptic assessment for the parameters color, aroma, bitterness, texture, and overall taste. The differences in plant and the shoot-top biomass were also assessed. The markers atpB-rbcL, matk-trnT, and ITS were used to define the species delimits. The field-grown and greenhouse-grown amaranths exhibited species/cultivar-specific photosynthetic variations. The texture and overall taste of the dishes were different among greenhouse and field-grown material. The tasters preferred the texture and the overall taste of the greenhouse-grown shoot-tops. The greenhouse-grown plants also yielded higher shoot-top harvests compared to field-grown counterparts. Out of the tested markers, ITS defines the delimits of amaranth species. The higher organoleptic preference, the appreciable yield levels, unique photosynthetic patterns of the greenhouse-grown amaranths, and species definitions provide the much-needed platform for clean shoot-top production guaranteeing the highest end-user trust.

S. D. S. S. Sooriyapathirana

L. T. Ranaweera

H. S. M. Jayarathne

T. H. I. Gayathree

P. G. R. G. Rathnayake

S. I. Karunarathne

S. M. N. K. Thilakarathne

R. Salih

C. K. Weebadde

C. P. Weebadde

Photosynthetic Phenomics of Field- and Greenhouse-Grown Amaranths vs. Sensory and Species Delimits

Panax quinquefolium is a perennial herbaceous plant that contains many beneficial ginsenosides with diverse pharmacological effects. 24(R)-pseudoginsenoside F11 is specific to P. quinquefolium, a useful biomarker for distinguishing this species from other related plants. However, because of its nonconjugated property and the complexity of existing detection methods, this biomarker cannot be used as the identification standard. We herein present a stable 24(R)-pseudoginsenoside F11 fingerprint spectrum in the terahertz band, thereby proving that F11 can be detected and quantitatively analyzed via terahertz spectroscopy. We also analyzed the sample by high-performance liquid chromatography-triple quadrupole mass spectrometry. The difference between the normalized data for the two analytical methods was less than 5&#x0025;. Furthermore, P. quinquefolium from different areas and other substances can be clearly distinguished based on these terahertz spectra with a standard principal component analysis. Our method is a fast, simple, and cost-effective approach for identifying and quantitatively analyzing P. quinquefolium.

Tianyi Kou

Ji Ye

Jing Wang

Yan Peng

Zefang Wang

Chenjun Shi

Xu Wu

Xitian Hu

Haihong Chen

Ling Zhang

Xiaohong Chen

Yiming Zhu

Huiliang Li

Songlin Zhuang

Terahertz Spectroscopy for Accurate Identification of Panax quinquefolium Basing on Nonconjugated 24(R)-Pseudoginsenoside F11

Plants have developed multiple strategies to respond to salt stress. In order to identify new traits related to salt tolerance, with potential breeding application, the research focus has recently been shifted to include root system architecture (RSA) and root plasticity. Using a simple but effective root phenotyping system containing soil (rhizotrons), RSA of several tomato cultivars and their response to salinity was investigated. We observed a high level of root plasticity of tomato seedlings under salt stress. The general root architecture was substantially modified in response to salt, especially with respect to position of the lateral roots in the soil. At the soil surface, where salt accumulates, lateral root emergence was most strongly inhibited. Within the set of tomato cultivars, H1015 was the most tolerant to salinity in both developmental stages studied. A significant correlation between several root traits and aboveground growth parameters was observed, highlighting a possible role for regulation of both ion content and root architecture in salt stress resilience.

Jacinto Gandullo

Safarina Ahmad

Essam Darwish

Rumyana Karlova

Christa Testerink

Phenotyping Tomato Root Developmental Plasticity in Response to Salinity in Soil Rhizotrons

About this journal

The open access journal Plant Phenomics, published in association with NAU, publishes novel research that advances plant phenotyping and connects phenomics with other research domains.

Journal profile

Meet the editorial board

Plant Phenomics' editorial board is led by Seishi Ninomiya (University of Tokyo),&nbsp;Frédéric Baret (French National Institute of Agricultural Research), and&nbsp;Zong-Ming Cheng (Nanjing Agricultural University/University of Tennessee) and is comprised of leading experts in the field.

Editorial board

More special issue details

Published Special Issues:

 
<a href="https://spj.sciencemag.org/plant-phenomics-special-issues/image-analysis">Image Analysis and Machine Learning for Cyber-Agricultural Systems</a> 
&nbsp;

<a href="https://spj.sciencemag.org/plant-phenomics-special-issues/statistics-bioinformatics/">Statistics, Bioinformatics, and Machine Learning Methods in Phenomics</a>