Automatizing phenotype measurement will decisively contribute to increase plant breeding efficiency. Among phenotypes, morphological traits are relevant in many fruit breeding programs, as appearance influences consumer preference. Often, these traits are manually or semiautomatically obtained. Yet, fruit morphology evaluation can be enhanced using fully automatized procedures and digital images provide a cost-effective opportunity for this purpose. Here, we present an automatized pipeline for comprehensive phenomic and genetic analysis of morphology traits extracted from internal and external strawberry (Fragaria x ananassa) images. The pipeline segments, classifies, and labels the images and extracts conformation features, including linear (area, perimeter, height, width, circularity, shape descriptor, ratio between height and width) and multivariate (Fourier elliptical components and Generalized Procrustes) statistics. Internal color patterns are obtained using an autoencoder to smooth out the image. In addition, we develop a variational autoencoder to automatically detect the most likely number of underlying shapes. Bayesian modeling is employed to estimate both additive and dominance effects for all traits. As expected, conformational traits are clearly heritable. Interestingly, dominance variance is higher than the additive component for most of the traits. Overall, we show that fruit shape and color can be quickly and automatically evaluated and are moderately heritable. Although we study strawberry images, the algorithm can be applied to other fruits, as shown in the GitHub repository.

Laura M. Zingaretti

Amparo Monfort

Miguel P&#x00E9;rez-Enciso

Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry

Crop traits such as aboveground biomass (AGB), total leaf area (TLA), leaf chlorophyll content (LCC), and thousand kernel weight (TWK) are important indices in maize breeding. How to extract multiple crop traits at the same time is helpful to improve the efficiency of breeding. Compared with digital and multispectral images, the advantages of high spatial and spectral resolution of hyperspectral images derived from unmanned aerial vehicle (UAV) are expected to accurately estimate the similar traits among breeding materials. This study is aimed at exploring the feasibility of estimating AGB, TLA, SPAD value, and TWK using UAV hyperspectral images and at determining the optimal models for facilitating the process of selecting advanced varieties. The successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were used to screen sensitive bands for the maize traits. Partial least squares (PLS) and random forest (RF) algorithms were used to estimate the maize traits. The results can be summarized as follows: The sensitive bands for various traits were mainly concentrated in the near-red and red-edge regions. The sensitive bands screened by CARS were more abundant than those screened by SPA. For AGB, TLA, and SPAD value, the optimal combination was the CARS-PLS method. Regarding the TWK, the optimal combination was the CARS-RF method. Compared with the model built by RF, the model built by PLS was more stable. This study provides guiding significance and practical value for main trait estimation of maize inbred lines by UAV hyperspectral images at the plot level.

Meiyan Shu

Mengyuan Shen

Jinyu Zuo

Pengfei Yin

Min Wang

Ziwen Xie

Jihua Tang

Ruili Wang

Baoguo Li

Xiaohong Yang

Yuntao Ma

The Application of UAV-Based Hyperspectral Imaging to Estimate Crop Traits in Maize Inbred Lines

Patchy stomata are a common and characteristic phenomenon in plants. Understanding and studying the regulation mechanism of patchy stomata are of great significance to further supplement and improve the stomatal theory. Currently, the common methods for stomatal behavior observation are based on static images, which makes it difficult to reflect dynamic changes of stomata. With the rapid development of portable microscopes and computer vision algorithms, it brings new chances for stomatal movement observation. In this study, a stomatal behavior observation system (SBOS) was proposed for real-time observation and automatic analysis of each single stoma in wheat leaf using object tracking and semantic segmentation methods. The SBOS includes two modules: the real-time observation module and the automatic analysis module. The real-time observation module can shoot videos of stomatal dynamic changes. In the automatic analysis module, object tracking locates every single stoma accurately to obtain stomatal pictures arranged in time-series; semantic segmentation can precisely quantify the stomatal opening area (SOA), with a mean pixel accuracy (MPA) of 0.8305 and a mean intersection over union (MIoU) of 0.5590 in the testing set. Moreover, we designed a graphical user interface (GUI) so that researchers could use this automatic analysis module smoothly. To verify the performance of the SBOS, the dynamic changes of stomata were observed and analyzed under chilling. Finally, we analyzed the correlation between gas exchange and SOA under drought stress, and the correlation coefficients between mean SOA and net photosynthetic rate (Pn), intercellular CO2 concentration (Ci), stomatal conductance (Gs), and transpiration rate (Tr) are 0.93, 0.96, 0.96, and 0.97.

Zhuangzhuang Sun

Yunlin Song

Qing Li

Jian Cai

Xiao Wang

Qin Zhou

Mei Huang

Dong Jiang

An Integrated Method for Tracking and Monitoring Stomata Dynamics from Microscope Videos

The automation of plant phenotyping using 3D imaging techniques is indispensable. However, conventional methods for reconstructing the leaf surface from 3D point clouds have a trade-off between the accuracy of leaf surface reconstruction and the method&#x2019;s robustness against noise and missing points. To mitigate this trade-off, we developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy by capturing two components of the leaf (the shape and distortion of that shape) separately using leaf-specific properties. This separation simplifies leaf surface reconstruction compared with conventional methods while increasing the robustness against noise and missing points. To evaluate the proposed method, we reconstructed the leaf surfaces from 3D point clouds of leaves acquired from two crop species (soybean and sugar beet) and compared the results with those of conventional methods. The result showed that the proposed method robustly reconstructed the leaf surfaces, despite the noise and missing points for two different leaf shapes. To evaluate the stability of the leaf surface reconstructions, we also calculated the leaf surface areas for 14 consecutive days of the target leaves. The result derived from the proposed method showed less variation of values and fewer outliers compared with the conventional methods.

Ryuhei Ando

Yuko Ozasa

Wei Guo

Robust Surface Reconstruction of Plant Leaves from 3D Point Clouds

High-yield rice cultivation is an effective way to address the increasing food demand worldwide. Correct classification of high-yield rice is a key step of breeding. However, manual measurements within breeding programs are time consuming and have high cost and low throughput, which limit the application in large-scale field phenotyping. In this study, we developed an accurate large-scale approach and presented the potential usage of hyperspectral data for rice yield measurement using the XGBoost algorithm to speed up the rice breeding process for many breeders. In total, 13 japonica rice lines in regional trials in northern China were divided into different categories according to the manual measurement of yield. Using an Unmanned Aerial Vehicle (UAV) platform equipped with a hyperspectral camera to capture images over multiple time series, a rice yield classification model based on the XGBoost algorithm was proposed. Four comparison experiments were carried out through the intraline test and the interline test considering lodging characteristics at the midmature stage or not. The result revealed that the degree of lodging in the midmature stage was an important feature affecting the classification accuracy of rice. Thus, we developed a low-cost, high-throughput phenotyping and nondestructive method by combining UAV-based hyperspectral measurements and machine learning for estimation of rice yield to improve rice breeding efficiency.

Jian Wang

Bizhi Wu

Markus V. Kohnen

Daqi Lin

Changcai Yang

Xiaowei Wang

Ailing Qiang

Wei Liu

Jianbin Kang

Hua Li

Jing Shen

Tianhao Yao

Jun Su

Bangyu Li

Lianfeng Gu

Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature

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>. 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>