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| 1 | Plant Phenotyping:Past,Present,and Future显示文摘A plant develops the dynamic phenotypes from the interaction of the plant with the environment.Understanding these processes that span plant’s lifetime in a permanently changing environment is essential for the advancement of basic plant science and its translation into application including breeding and crop management.The plant research community was thus confronted with the need to accurately measure diverse traits of an increasingly large number of plants to help plants to adapt to resource-limiting environment and low-input agriculture.In this overview,we outline the development of plant phenotyping as a multidisciplinary field.We sketch the technological advancement that laid the foundation for the development of phenotyping centers and evaluate the upcoming challenges for further advancement of plant phenotyping specifically with respect to standardization of data acquisition and reusability.Finally,we describe the development of the plant phenotyping community as an essential step to integrate the community and effectively use the emerging synergies. | Roland Pieruschka Uli Schurr | 2019 | Plant Phenomics2019，1，1： | 17 |
| 2 | A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting显示文摘The yield of cereal crops such as sorghum(Sorghum bicolor L.Moench)depends on the distribution of crop-heads in varying branching arrangements.Therefore,counting the head number per unit area is critical for plant breeders to correlate with the genotypic variation in a specific breeding field.However,measuring such phenotypic traitsmanually is an extremely labor-intensive process and suffers from low efficiency and human errors.Moreover,the process is almost infeasible for large-scale breeding plantations or experiments.Machine learning-based approaches like deep convolutional neural network(CNN)based object detectors are promising tools for efficient object detection and counting.However,a significant limitation of such deep learningbased approaches is that they typically require a massive amount of hand-labeled images for training,which is still a tedious process.Here,we propose an active learning inspired weakly supervised deep learning framework for sorghum head detection and counting from UAV-based images.We demonstrate that it is possible to significantly reduce human labeling effort without compromising final model performance(��2 between human count and machine count is 0.88)by using a semitrained CNN model(i.e.,trained with limited labeled data)to perform synthetic annotation.In addition,we also visualize key features that the network learns.This improves trustworthiness by enabling users to better understand and trust the decisions that the trained deep learning model makes. | Sambuddha Ghosal Bangyou Zheng Scott CChapman Andries BPotgieter David RJordan Xuemin Wang Asheesh KSingh Arti Singh Masayuki Hirafuji Seishi Ninomiya Baskar Ganapathysubramanian Soumik Sarkar Wei Guo | 2019 | Plant Phenomics2019，1，1： | 16 |
| 3 | Global Wheat Head Detection(GWHD)Dataset:A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods显示文摘The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health,size,maturity stage,and the presence of awns.Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms.However,these methods have generally been calibrated and validated on limited datasets.High variability in observational conditions,genotypic differences,development stages,and head orientation makes wheat head detection a challenge for computer vision.Further,possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex.Through a joint international collaborative effort,we have built a large,diverse,and well-labelled dataset of wheat images,called the Global Wheat Head Detection(GWHD)dataset.It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes.Guidelines for image acquisition,associating minimum metadata to respect FAIR principles,and consistent head labelling methods are proposed when developing new head detection datasets.The GWHD dataset is publicly available at http://gffzzef49fa24bca249bbh00o50u50ffuo6nfw.ffgz.tsg.suse.edu.cn/and aimed at developing and benchmarking methods for wheat head detection. | Etienne David Simon Madec Pouria Sadeghi-Tehran Helge Aasen Bangyou Zheng Shouyang Liu Norbert Kirchgessner Goro Ishikawa Koichi Nagasawa Minhajul A.Badhon Curtis Pozniak Benoit de Solan Andreas Hund Scott C.Chapman Frédéric Baret Ian Stavness Wei Guo | 2020 | Plant Phenomics2020，2，1： | 13 |
| 4 | MVS-Pheno:A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction显示文摘Plant phenotyping technologies play important roles in plant research and agriculture.Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to analyze the morphological differences in response to environments for crop cultivation.Accordingly,high-throughput phenotyping technologies for individual plants grown in field conditions are urgently needed,and MVS-Pheno,a portable and low-cost phenotyping platform for individual plants,was developed.The platform is composed of four major components:a semiautomatic multiview stereo(MVS)image acquisition device,a data acquisition console,data processing and phenotype extraction software for maize shoots,and a data management system.The platform’s device is detachable and adjustable according to the size of the target shoot.Image sequences for each maize shoot can be captured within 60-120 seconds,yielding 3D point clouds of shoots are reconstructed using MVS-based commercial software,and the phenotypic traits at the organ and individual plant levels are then extracted by the software.The correlation coefficient(R^(2))between the extracted and manually measured plant height,leaf width,and leaf area values are 0.99,0.87,and 0.93,respectively.A data management system has also been developed to store and manage the acquired raw data,reconstructed point clouds,agronomic information,and resulting phenotypic traits.The platform offers an optional solution for high-throughput phenotyping of field-grown plants,which is especially useful for large populations or experiments across many different ecological regions. | Sheng Wu Weiliang Wen Yongjian Wang Jiangchuan Fan Chuanyu Wang Wenbo Gou Xinyu Guo | 2020 | Plant Phenomics2020，2，1： | 11 |
| 5 | Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping:A Review显示文摘Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs,understanding plantenvironment interactions,and managing agricultural systems.In the past five years,imaging approaches have shown great potential for high-throughput plant phenotyping,resulting in more attention paid to imaging-based plant phenotyping.With this increased amount of image data,it has become urgent to develop robust analytical tools that can extract phenotypic traits accurately and rapidly.The goal of this review is to provide a comprehensive overview of the latest studies using deep convolutional neural networks(CNNs)in plant phenotyping applications.We specifically review the use of various CNN architecture for plant stress evaluation,plant development,and postharvest quality assessment.We systematically organize the studies based on technical developments resulting from imaging classification,object detection,and image segmentation,thereby identifying state-of-the-art solutions for certain phenotyping applications.Finally,we provide several directions for future research in the use of CNN architecture for plant phenotyping purposes. | Yu Jiang Changying Li | 2020 | Plant Phenomics2020，2，1： | 8 |
| 6 | How Convolutional Neural Networks Diagnose Plant Disease显示文摘Deep learning with convolutional neural networks(CNNs)has achieved great success in the classification of various plant diseases.However,a limited number of studies have elucidated the process of inference,leaving it as an untouchable black box.Revealing the CNN to extract the learned feature as an interpretable form not only ensures its reliability but also enables the validation of the model authenticity and the training dataset by human intervention.In this study,a variety of neuron-wise and layer-wise visualization methods were applied using a CNN,trained with a publicly available plant disease image dataset.We showed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis,which resembles human decision-making.While several visualizationmethods were used as they are,others had to be optimized to target a specific layer that fully captures the features to generate consequential outputs.Moreover,by interpreting the generated attention maps,we identified several layers that were not contributing to inference and removed such layers inside the network,decreasing the number of parameters by 75%without affecting the classification accuracy.The results provide an impetus for the CNN black box users in the field of plant science to better understand the diagnosis process and lead to further efficient use of deep learning for plant disease diagnosis. | Yosuke Toda Fumio Okura | 2019 | Plant Phenomics2019，1，1： | 6 |
| 7 | Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography显示文摘The traits of rice panicles play important roles in yield assessment,variety classification,rice breeding,and cultivation management.Most traditional grain phenotyping methods require threshing and thus are time-consuming and labor-intensive;moreover,these methods cannot obtain 3D grain traits.In this work,based on X-ray computed tomography,we proposed an image analysis method to extract twenty-two 3D grain traits.After 104 samples were tested,the R^(2) values between the extracted and manual measurements of the grain number and grain length were 0.980 and 0.960,respectively.We also found a high correlation between the total grain volume and weight.In addition,the extracted 3D grain traits were used to classify the rice varieties,and the support vector machine classifier had a higher recognition accuracy than the stepwise discriminant analysis and random forest classifiers.In conclusion,we developed a 3D image analysis pipeline to extract rice grain traits using X-ray computed tomography that can provide more 3D grain information and could benefit future research on rice functional genomics and rice breeding. | Weijuan Hu Can Zhang Yuqiang Jiang Chenglong Huang Qian Liu Lizhong Xiong Wanneng Yang Fan Chen | 2020 | Plant Phenomics2020，2，1： | 6 |
| 8 | Applying FAIR Principles to Plant Phenotypic Data Management in GnpIS显示文摘GnpIS is a data repository for plant phenomics that stores whole field and greenhouse experimental data including environment measures.It allows long-term access to datasets following the FAIR principles:Findable,Accessible,Interoperable,and Reusable,by using a flexible and original approach.It is based on a generic and ontology driven data model and an innovative software architecture that uncouples data integration,storage,and querying.It takes advantage of international standards including the Crop Ontology,MIAPPE,and the Breeding API.GnpIS allows handling data for a wide range of species and experiment types,including multiannual perennial plants experimental network or annual plant trials with either raw data,i.e.,direct measures,or computed traits.It also ensures the integration and the interoperability among phenotyping datasets and with genotyping data.This is achieved through a careful curation and annotation of the key resources conducted in close collaboration with the communities providing data.Our repository follows the Open Science data publication principles by ensuring citability of each dataset.Finally,GnpIS compliance with international standards enables its interoperability with other data repositories hence allowing data links between phenotype and other data types.GnpIS can therefore contribute to emerging international federations of information systems. | C.Pommier C.Michotey G.Cornut P.Roumet E.Duchêne R.Flores A.Lebreton M.Alaux S.Durand E.Kimmel T.Letellier G.Merceron M.Laine C.Guerche M.Loaec D.Steinbach M.A.Laporte E.Arnaud H.Quesneville A.F.Adam-Blondon | 2019 | Plant Phenomics2019，1，1： | 6 |
| 9 | An Exploration of Deep-Learning Based Phenotypic Analysis to Detect Spike Regions in Field Conditions for UK Bread Wheat显示文摘Wheat is one of the major crops in the world,with a global demand expected to reach 850 million tons by 2050 that is clearly outpacing current supply.The continual pressure to sustain wheat yield due to the world’s growing population under fluctuating climate conditions requires breeders to increase yield and yield stability across environments.We are working to integrate deep learning into field-based phenotypic analysis to assist breeders in this endeavour.We have utilised wheat images collected by distributed CropQuant phenotyping workstations deployed for multiyear field experiments of UK bread wheat varieties.Based on these image series,we have developed a deep-learning based analysis pipeline to segment spike regions from complicated backgrounds.As a first step towards robust measurement of key yield traits in the field,we present a promising approach that employ Fully Convolutional Network(FCN)to performsemantic segmentation of images to segment wheat spike regions.We also demonstrate the benefits of transfer learning through the use of parameters obtained from other image datasets.We found that the FCN architecture had achieved a Mean classification Accuracy(MA)>82%on validation data and>76%on test data and Mean Intersection over Union value(MIoU)>73%on validation data and and>64%on test datasets.Through this phenomics research,we trust our attempt is likely to form a sound foundation for extracting key yield-related traits such as spikes per unit area and spikelet number per spike,which can be used to assist yield-focused wheat breeding objectives in near future. | Tahani Alkhudaydi Daniel Reynolds Simon Griffiths Ji Zhou Beatriz de la Iglesia | 2019 | Plant Phenomics2019，1，1： | 5 |
| 10 | Exploring Seasonal and Circadian Rhythms in Structural Traits of Field Maize from LiDAR Time Series显示文摘Plant growth rhythm in structural traits is important for better understanding plant response to the ever-changing environment.Terrestrial laser scanning(TLS)is a well-suited tool to study structural rhythm under field conditions.Recent studies have used TLS to describe the structural rhythm of trees,but no consistent patterns have been drawn.Meanwhile,whether TLS can capture structural rhythm in crops is unclear.Here,we aim to explore the seasonal and circadian rhythms in maize structural traits at both the plant and leaf levels from time-series TLS.The seasonal rhythm was studied using TLS data collected at four key growth periods,including jointing,bell-mouthed,heading,and maturity periods.Circadian rhythms were explored by using TLS data acquired around every 2 hours in a whole day under standard and cold stress conditions.Results showed that TLS can quantify the seasonal and circadian rhythm in structural traits at both plant and leaf levels.(1)Leaf inclination angle decreased significantly between the jointing stage and bell-mouthed stage.Leaf azimuth was stable after the jointing stage.(2)Some individual-level structural rhythms(e.g.,azimuth and projected leaf area/PLA)were consistent with leaf-level structural rhythms.(3)The circadian rhythms of some traits(e.g.,PLA)were not consistent under standard and cold stress conditions.(4)Environmental factors showed better correlations with leaf traits under cold stress than standard conditions.Temperature was the most important factor that significantly correlated with all leaf traits except leaf azimuth.This study highlights the potential of time-series TLS in studying outdoor agricultural chronobiology. | Shichao Jin Yanjun Su Yongguang Zhang Shilin Song Qing Li Zhonghua Liu Qin Ma Yan Ge LingLi Liu Yanfeng Ding Frédéric Baret Qinghua Guo | 2021 | Plant Phenomics2021，3，1： | 5 |
| 11 | High-Throughput Measurements of Stem Characteristics to Estimate Ear Density and Above-Ground Biomass显示文摘Total above-ground biomass at harvest and ear density are two important traits that characterize wheat genotypes.Two experiments were carried out in two different sites where several genotypes were grown under contrasted irrigation and nitrogen treatments.A high spatial resolution RGB camera was used to capture the residual stems standing straight after the cutting by the combine machine during harvest.It provided a ground spatial resolution better than 0.2 mm.A Faster Regional Convolutional Neural Network(Faster-RCNN)deep-learning model was first trained to identify the stems cross section.Results showed that the identification provided precision and recall close to 95%.Further,the balance between precision and recall allowed getting accurate estimates of the stem density with a relative RMSE close to 7%and robustness across the two experimental sites.The estimated stem density was also compared with the ear density measured in the field with traditional methods.A very high correlation was found with almost no bias,indicating that the stem density could be a good proxy of the ear density.The heritability/repeatability evaluated over 16 genotypes in one of the two experiments was slightly higher(80%)than that of the ear density(78%).The diameter of each stem was computed from the profile of gray values in the extracts of the stem cross section.Results show that the stem diameters follow a gamma distribution over eachmicroplot with an average diameter close to 2.0mm.Finally,the biovolume computed as the product of the average stem diameter,the stem density,and plant height is closely related to the above-ground biomass at harvest with a relative RMSE of 6%.Possible limitations of the findings and future applications are finally discussed. | Xiuliang Jin Simon Madec Dan Dutartre Benoit de Solan Alexis Comar Frédéric Baret | 2019 | Plant Phenomics2019，1，1： | 5 |
| 12 | Monitoring Maize Lodging Grades via Unmanned Aerial Vehicle Multispectral Image显示文摘Lodging is one of the main factors affecting the quality and yield of crops.Timely and accurate determination of crop lodging grade is of great significance for the quantitative and objective evaluation of yield losses.The purpose of this study was to analyze the monitoring ability of a multispectral image obtained by an unmanned aerial vehicle(UAV)for determination of the maize lodging grade.A multispectral Parrot Sequoia camera is specially designed for agricultural applications and provides new information that is useful in agricultural decision-making.Indeed,a near-infrared image which cannot be seen with the naked eye can be used to make a highly precise diagnosis of the vegetation condition.The images obtained constitute a highly effective tool for analyzing plant health.Maize samples with different lodging grades were obtained by visual interpretation,and the spectral reflectance,texture feature parameters,and vegetation indices of the training samples were extracted.Different feature transformations were performed,texture features and vegetation indices were combined,and various feature images were classified by maximum likelihood classification(MLC)to extract four lodging grades.Classification accuracy was evaluated using a confusion matrix based on the verification samples,and the features suitable for monitoring the maize lodging grade were screened.The results showed that compared with a multispectral image,the principal components,texture features,and combination of texture features and vegetation indices were improved by varying degrees.The overall accuracy of the combination of texture features and vegetation indices is 86.61%,and the Kappa coefficient is 0.8327,which is higher than that of other features.Therefore,the classification result based on the feature combinations of the UAV multispectral image is useful for monitoring of maize lodging grades. | Qian Sun Lin Sun Meiyan Shu Xiaohe Gu Guijun Yang Longfei Zhou | 2019 | Plant Phenomics2019，1，1： | 5 |
| 13 | Development of Optimized Phenomic Predictors for Efficient Plant Breeding Decisions Using Phenomic-Assisted Selection in Soybean显示文摘The rate of advancement made in phenomic-assisted breeding methodologies has lagged those of genomic-assisted techniques,which is now a critical component of mainstream cultivar development pipelines.However,advancements made in phenotyping technologies have empowered plant scientists with affordable high-dimensional datasets to optimize the operational efficiencies of breeding programs.Phenomic and seed yield data was collected across six environments for a panel of 292 soybean accessions with varying genetic improvements.Random forest,a machine learning(ML)algorithm,was used to map complex relationships between phenomic traits and seed yield and prediction performance assessed using two cross-validation(CV)scenarios consistent with breeding challenges.To develop a prescriptive sensor package for future high-throughput phenotyping deployment to meet breeding objectives,feature importance in tandem with a genetic algorithm(GA)technique allowed selection of a subset of phenotypic traits,specifically optimal wavebands.The results illuminated the capability of fusingML and optimization techniques to identify a suite of in-season phenomic traits that will allow breeding programs to decrease the dependence on resource-intensive end-season phenotyping(e.g.,seed yield harvest).While we illustrate with soybean,this study establishes a template for deploying multitrait phenomic prediction that is easily amendable to any crop species and any breeding objective。 | Kyle Parmley Koushik Nagasubramanian Soumik Sarkar Baskar Ganapathysubramanian Asheesh K.Singh | 2019 | Plant Phenomics2019，1，1： | 5 |
| 14 | A High-Throughput Phenotyping System Using Machine Vision to Quantify Severity of Grapevine Powdery Mildew显示文摘Powdery mildews present specific challenges to phenotyping systems that are based on imaging.Having previously developed lowthroughput,quantitative microscopy approaches for phenotyping resistance to Erysiphe necator on thousands of grape leaf disk samples for genetic analysis,here we developed automated imaging and analysis methods for E.necator severity on leaf disks.By pairing a 46-megapixel CMOS sensor camera,a long-working distance lens providing 3.5×magnification,X-Y sample positioning,and Z-axis focusing movement,the system captured 78%of the area of a 1-cm diameter leaf disk in 3 to 10 focus-stacked images within 13.5 to 26 seconds.Each image pixel represented 1.44 m2 of the leaf disk.A convolutional neural network(CNN)based on GoogLeNet determined the presence or absence of E.necator hyphae in approximately 800 subimages per leaf disk as an assessment of severity,with a training validation accuracy of 94.3%.For an independent image set the CNN was in agreement with human experts for 89.3%to 91.7%of subimages.This live-imaging approach was nondestructive,and a repeated measures time course of infection showed differentiation among susceptible,moderate,and resistant samples.Processing over one thousand samples per day with good accuracy,the system can assess host resistance,chemical or biological efficacy,or other phenotypic responses of grapevine to E.necator.In addition,new CNNs could be readily developed for phenotyping within diverse pathosystems or for diverse traits amenable to leaf disk assays. | Andrew Bierman Tim LaPlumm Lance Cadle-Davidson David Gadoury Dani Martinez Surya Sapkota Mark Rea | 2019 | Plant Phenomics2019，1，1： | 4 |
| 15 | Ground-Based LiDAR Improves Phenotypic Repeatability of Above-Ground Biomass and Crop Growth Rate in Wheat显示文摘Highly repeatable,nondestructive,and high-throughput measures of above-ground biomass(AGB)and crop growth rate(CGR)are important for wheat improvement programs.This study evaluates the repeatability of destructive AGB and CGR measurements in comparison to two previously described methods for the estimation of AGB from LiDAR:3D voxel index(3DVI)and 3D profile index(3DPI).Across three field experiments,contrasting in available water supply and comprising up to 98 wheat genotypes varying for canopy architecture,several concurrent measurements of LiDAR and AGB were made from jointing to anthesis.Phenotypic correlations at discrete events between AGB and the LiDAR-derived biomass indices were significant,ranging from 0.31(P<0:05)to 0.86(P<0:0001),providing confidence in the LiDAR indices as effective surrogates for AGB.The repeatability of the LiDAR biomass indices at discrete events was at least similar to and often higher than AGB,particularly under water limitation.The correlations between calculated CGR for AGB and the LiDAR indices were moderate to high and varied between experiments.However,across all experiments,the repeatabilities of the CGR derived from the LiDAR indices were appreciably greater than those for AGB,except for the 3DPI in the water-limited environment.In our experiments,the repeatability of either LiDAR index was consistently higher than that of AGB,both at discrete time points and when CGR was calculated.These findings provide promising support for the reliable use of ground-based LiDAR,as a surrogate measure of AGB and CGR,for screening germplasm in research and wheat breeding. | David M.Deery Greg J.Rebetzke Jose A.Jimenez-Berni Anthony G.Condon David J.Smith Kathryn M.Bechaz William D.Bovill | 2020 | Plant Phenomics2020，2，1： | 4 |
| 16 | Simultaneous Prediction of Wheat Yield and Grain Protein Content Using Multitask Deep Learning from Time-Series Proximal Sensing显示文摘Wheat yield and grain protein content(GPC)are two main optimization targets for breeding and cultivation.Remote sensing provides nondestructive and early predictions of yield and GPC,respectively.However,whether it is possible to simultaneously predict yield and GPC in one model and the accuracy and influencing factors are still unclear.In this study,we made a systematic comparison of different deep learning models in terms of data fusion,time-series feature extraction,and multitask learning.The results showed that time-series data fusion significantly improved yield and GPC prediction accuracy with R 2 values of 0.817 and 0.809. | Zhuangzhuang Sun Qing Li Shichao Jin Yunlin Song Shan Xu Xiao Wang Jian Cai Qin Zhou Yan Ge Ruinan Zhang Jingrong Zang Dong Jiang | 2022 | Plant Phenomics2022，4，1： | 4 |
| 17 | Robust Surface Reconstruction of Plant Leaves from 3D Point Clouds显示文摘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’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 | 2021 | Plant Phenomics2021，3，1： | 4 |
| 18 | A Deep Learning-Based Phenotypic Analysis of Rice Root Distribution from Field Images显示文摘Root distribution in the soil determines plants’nutrient and water uptake capacity.Therefore,root distribution is one of the most important factors in crop production.The trench profile method is used to observe the root distribution underground by making a rectangular hole close to the crop,providing informative images of the root distribution compared to other root phenotyping methods.However,much effort is required to segment the root area for quantification.In this study,we present a promising approach employing a convolutional neural network for root segmentation in trench profile images.We defined two parameters,Depth50 and Width50,representing the vertical and horizontal centroid of root distribution,respectively.Quantified parameters for root distribution in rice(Oryza sativa L.)predicted by the trained model were highly correlated with parameters calculated by manual tracing.These results indicated that this approach is useful for rapid quantification of the root distribution from the trench profile images.Using the trained model,we quantified the root distribution parameters among 60 rice accessions,revealing the phenotypic diversity of root distributions.We conclude that employing the trench profile method and a convolutional neural network is reliable for root phenotyping and it will furthermore facilitate the study of crop roots in the field. | S.Teramoto Y.Uga | 2020 | Plant Phenomics2020，2，1： | 4 |
| 19 | Estimates of Maize Plant Density from UAV RGB Images Using Faster-RCNN Detection Model:Impact of the Spatial Resolution显示文摘Early-stage plant density is an essential trait that determines the fate of a genotype under given environmental conditions and management practices.The use of RGB images taken from UAVs may replace the traditional visual counting in fields with improved throughput,accuracy,and access to plant localization.However,high-resolution images are required to detect the small plants present at the early stages.This study explores the impact of image ground sampling distance(GSD)on the performances of maize plant detection at three-to-five leaves stage using Faster-RCNN object detection algorithm.Data collected at high resolution(GSD≈0:3 cm)over six contrasted sites were used for model training.Two additional sites with images acquired both at high and low(GSD≈0:6 cm)resolutions were used to evaluate the model performances.Results show that Faster-RCNN achieved very good plant detection and counting(rRMSE=0:08)performances when native high-resolution images are used both for training and validation.Similarly,good performances were observed(rRMSE=0:11)when the model is trained over synthetic low-resolution images obtained by downsampling the native training high-resolution images and applied to the synthetic low-resolution validation images.Conversely,poor performances are obtained when the model is trained on a given spatial resolution and applied to another spatial resolution.Training on a mix of high-and low-resolution images allows to get very good performances on the native high-resolution(rRMSE=0:06)and synthetic low-resolution(rRMSE=0:10)images.However,very low performances are still observed over the native low-resolution images(rRMSE=0:48),mainly due to the poor quality of the native low-resolution images.Finally,an advanced super resolution method based on GAN(generative adversarial network)that introduces additional textural information derived from the native high-resolution images was applied to the native low-resolution validation images.Results show some significant improvement(rRMSE=0:22)compared to bicubic upsampling approach,while still far below the performances achieved over the native high-resolution images. | K.Velumani R.Lopez-Lozano S.Madec W.Guo J.Gillet A.Comar F.Baret | 2021 | Plant Phenomics2021，3，1： | 4 |
| 20 | Soybean Root System Architecture Trait Study through Genotypic,Phenotypic,and Shape-Based Clusters显示文摘We report a root system architecture(RSA)traits examination of a larger scale soybean accession set to study trait genetic diversity.Suffering from the limitation of scale,scope,and susceptibility to measurement variation,RSA traits are tedious to phenotype.Combining 35,448 SNPs with an imaging phenotyping platform,292 accessions(replications=14)were studied for RSA traits to decipher the genetic diversity.Based on literature search for root shape and morphology parameters,we used an ideotypebased approach to develop informative root(iRoot)categories using root traits.The RSA traits displayed genetic variability for root shape,length,number,mass,and angle.Soybean accessions clustered into eight genotype-and phenotype-based clusters and displayed similarity.Genotype-based clusters correlated with geographical origins.SNP profiles indicated that much of US origin genotypes lack genetic diversity for RSA traits,while diverse accession could infuse useful genetic variation for these traits.Shape-based clusters were created by integrating convolution neural net and Fourier transformation methods,enabling trait cataloging for breeding and research applications.The combination of genetic and phenotypic analyses in conjunction with machine learning and mathematical models provides opportunities for targeted root trait breeding efforts to maximize the beneficial genetic diversity for future genetic gains. | Kevin G.Falk Talukder Zaki Jubery Jamie A.O’Rourke Arti Singh Soumik Sarkar Baskar Ganapathysubramanian Asheesh K.Singh | 2020 | Plant Phenomics2020，2，1： | 3 |
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