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From Prototype to Inference:A Pipeline to Apply Deep Learning in Sorghum Panicle Detection

Plant Phenomics 2023年第1期5卷 94-109页

作者： Chrisbin James Yanyang Gu Andries Potgieter Etienne David simon madec Wei Guo Frédéric Baret Anders Eriksson Scott Chapman School of Agriculture and Food Sciences The University of QueenslandBrisbaneAustralia School of Information Technology and Electrical Engineering The University of QueenslandBrisbaneAustralia Queensland Alliance for Agriculture and Food Innovation The University of QueenslandBrisbaneAustralia Arvalis Institut du VégétalParisFrance Graduate School of Agricultural and Life Sciences The University of TokyoTokyoJapan Institut National de la Recherche Agronomique ParisFrance

Head(panicle)density is a major component in understanding crop yield,especially in crops that produce variable numbers of tillers such as sorghum and *** of panicle density both in plant breeding and in the agronomy scouting of commercial crops typically relies on manual counts observation,which is an inefficient and tedious *** of the easy availability of red–green–blue images,machine learning approaches have been applied to replacing manual ***,much of this research focuses on detection per se in limited testing conditions and does not provide a general protocol to utilize deep-learning-based *** this paper,we provide a comprehensive pipeline from data collection to model deployment in deep-learning-assisted panicle yield estimation for *** pipeline provides a basis from data collection and model training,to model validation and model deployment in commercial *** model training is the foundation of the ***,in natural environments,the deployment dataset is frequently different from the training data(domain shift)causing the model to fail,so a robust model is essential to build a reliable *** we demonstrate our pipeline in a sorghum field,the pipeline can be generalized to other grain *** pipeline provides a high-resolution head density map that can be utilized for diagnosis of agronomic variability within a field,in a pipeline built without commercial software.

关键词： crops breeding utilize

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SegVeg: Segmenting RGB Images into Green and Senescent Vegetation by Combining Deep and Shallow Methods

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Plant Phenomics 2022年第1期4卷 26-42页

作者： Mario Serouart simon madec Etienne David Kaaviya Velumani Raul LopezLozano Marie Weiss Frederic Baret Arvalis Institutduvegetal228routedel’aerodrome-CS4050984914 Avignon Cedex 9France INRAE Avignon UniversiteUMREMMAHUMTCAPTE228routedel’aerodrome-CS4050984914 Avignon Cedex 9France CIRAD UMRTETISF-34398 MontpellierFrance Hiphen SAS 228routedel’aerodrome-CS4050984914 Avignon Cedex 9France

Pixel segmentation of high-resolution RGB images into chlorophyll-active or nonactive vegetation classes is a first step often required before estimating key traits of *** have developed the SegVeg approach for semantic segmentation of RGB images into three classes(background,green,and senescent vegetation).This is achieved in two steps:A U-net model is first trained on a very large dataset to separate whole vegetation from *** green and senescent vegetation pixels are then separated using SVM,a shallow machine learning technique,trained over a selection of pixels extracted from *** performances of the SegVeg approach is then compared to a 3-class U-net model trained using weak supervision over RGB images segmented with SegVeg as groundtruth *** show that the SegVeg approach allows to segment accurately the three ***,some confusion is observed mainly between the background and senescent vegetation,particularly over the dark and bright regions of the *** U-net model achieves similar performances,with slight degradation over the green vegetation:the SVM pixel-based approach provides more precise delineation of the green and senescent patches as compared to the convolutional nature of *** use of the components of several color spaces allows to better classify the vegetation pixels into green and ***,the models are used to predict the fraction of three classes over whole images or regularly spaced *** show that green fraction is very well estimated(R^(2)=0.94)by the SegVeg model,while the senescent and background fractions show slightly degraded performances(R^(2)=0.70 and 0.73,respectively)with a mean 95%confidence error interval of 2.7%and 2.1%for the senescent vegetation and background,versus 1%for green *** have made SegVeg publicly available as a ready-to-use script and model,along with the entire annotated grid-pixels *** thus hope to render segmentation acc

关键词： Deep offering render

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High-Throughput Measurements of Stem Characteristics to Estimate Ear Density and Above-Ground Biomass

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Plant Phenomics 2019年第1期1卷 80-89页

作者： Xiuliang Jin simon madec Dan Dutartre Benoit de Solan Alexis Comar Frédéric Baret INRA EMMAH UMR 1114228 route de l’A′erodrome84914 AvignonFrance Institute of Crop Sciences Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and EcologyMinistry of AgricultureBeijing 100081China HIPHEN Rue Charrue84000 AvignonFrance ARVALIS-Institut du V′eg′etal Station Exp′erimentale91720 BoignevilleFrance

Total above-ground biomass at harvest and ear density are two important traits that characterize wheat *** 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 *** 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 *** 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 *** 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 *** 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 *** show that the stem diameters follow a gamma distribution over eachmicroplot with an average diameter close to ***,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.

关键词： Biomass straight finally

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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

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Plant Phenomics 2020年第1期2卷 243-254页

作者： 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 Arvalis Institut du végétal3 Rue Joseph et Marie Hackin75116 ParisFrance UMR1114 EMMAH INRAECentre PACABâtiment ClimatDomaine Saint-Paul228 Route de l’AérodromeCS 4050984914 Avignon CedexFrance Plant Sciences Department Rothamsted ResearchHarpendenUK Institute of Agricultural Sciences ETH ZurichUniversitätstrasse 28092 ZurichSwitzerland CSIRO Agriculture and Food Queensland Biosciences Precinct306 Carmody RoadSt Lucia4067 QLDAustralia Plant Phenomics Research Center Nanjing Agricultural UniversityNanjingChina Institute of Crop Science National Agriculture and Food Research OrganizationJapan Hokkaido Agricultural Research Center National Agriculture and Food Research OrganizationJapan Department of Computer Science University of SaskatchewanCanada Department of Plant Sciences University of SaskatchewanCanada School of Food and Agricultural Sciences The University of QueenslandGatton4343 QLDAustralia Graduate School of Agricultural and Life Sciences The University of Tokyo1-1-1 Midori-choNishitokyo CityTokyoJapan

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 *** studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning ***,these methods have generally been calibrated and validated on limited *** variability in observational conditions,genotypic differences,development stages,and head orientation makes wheat head detection a challenge for computer ***,possible blurring due to motion or wind and overlap between heads for dense populations make this task even more *** 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)*** 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 *** for image acquisition,associating minimum metadata to respect FAIR principles,and consistent head labelling methods are proposed when developing new head detection *** GWHD dataset is publicly available at http://***/and aimed at developing and benchmarking methods for wheat head detection.

关键词： Wheat wheat maturity

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A Double Swath Configuration for Improving Throughput and Accuracy of Trait Estimate from UAV Images

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Plant Phenomics 2021年第1期3卷 378-388页

作者： Wenjuan Li Alexis Comar Marie Weiss Sylvain Jay Gallian Colombeau Raul Lopez-Lozano simon madec Frédéric Baret HIPHEN 228 Route de l’Aérodrome84000 AvignonFrance INRAE Avignon UniversitéUMR EMMAH84000 AvignonFrance ARVALIS Institut du Végétal 3 Rue Joseph et Marie Hackin75116 ParisFrance

Multispectral observations from unmanned aerial vehicles(UAVs)are currently used for precision agriculture and crop phenotyping applications to monitor a series of traits allowing the characterization of the vegetation ***,the limited autonomy of UAVs makes the completion of flights difficult when sampling large *** the throughput of data acquisition while not degrading the ground sample distance(GSD)is,therefore,a critical issue to be *** propose here a new image acquisition configuration based on the combination of two focal length(f)optics:an optics with f=4:2 mm is added to the standard f=8 mm(SS:single swath)of the multispectral camera(DS:double swath,double of the standard one).Two flights were completed consecutively in 2018 over a maize field using the AIRPHEN multispectral camera at 52 m *** DS flight plan was designed to get 80%overlap with the 4.2 mm optics,while the SS one was designed to get 80%overlap with the 8 mm *** a result,the time required to cover the same area is halved for the DS as compared to the *** georeferencing accuracy was improved for the DS configuration,particularly for the Z dimension due to the larger view angles available with the small focal length *** to plant height estimates demonstrates that the DS configuration provides similar results as the SS ***,for both the DS and SS configurations,degrading the quality level used to generate the 3D point cloud significantly decreases the plant height estimates.

关键词： optics overlap altitude

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Global Wheat Head Detection 2021:An Improved Dataset for Benchmarking Wheat Head Detection Methods

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Plant Phenomics 2021年第1期3卷 277-285页

作者： Etienne David Mario Serouart Daniel Smith simon madec Kaaviya Velumani Shouyang Liu Xu Wang Francisco Pinto Shahameh Shafiee Izzat SATahir Hisashi Tsujimoto Shuhei Nasuda Bangyou Zheng Norbert Kirchgessner Helge Aasen Andreas Hund Pouria Sadhegi-Tehran Koichi Nagasawa Goro Ishikawa Sébastien Dandrifosse Alexis Carlier Benjamin Dumont Benoit Mercatoris Byron Evers Ken Kuroki Haozhou Wang Masanori Ishii Minhajul ABadhon Curtis Pozniak David Shaner LeBauer Morten Lillemo Jesse Poland Scott Chapman Benoit de Solan Frédéric Baret Ian Stavness Wei Guo Arvalis Institut du Végétal3 Rue Joseph et Marie Hackin75116 ParisFrance UMR1114 EMMAH INRAECentre PACABâtiment ClimatDomaine Saint-Paul228 Route de l’AérodromeCS 4050984914 Avignon CedexFrance School of Food and Agricultural Sciences The University of QueenslandGatton4343 QLDAustralia Hiphen SAS 120 Rue Jean DaussetAgroparcBâtiment Technicité84140 AvignonFrance Plant Phenomics Research Center Nanjing Agricultural UniversityNanjingChina Wheat Genetics Resource Center Dep.of Plant PathologyKansas State Univ.4024 Throckmorton Plant Sciences CenterManhattanKansasUSA Global Wheat Program International Maize and Wheat Improvement Centre(CIMMYT)MexicoD.F.Mexico Faculty of Biosciences Norwegian University of Life SciencesP.O.Box 5003NO-1432ÅsNorway Agricultural Research Corporation Wheat Research ProgramP.O.Box 126Wad MedaniSudan Arid Land Research Center Tottori UniversityTottori 680-0001Japan Laboratories of Plant Genetics and Plant Breeding Graduate School of AgricultureKyoto UniversityJapan CSIRO Agriculture and Food Queensland Biosciences Precinct306 Carmody RoadSt Lucia4067 QLDAustralia Institute of Agricultural Sciences ETH ZurichUniversitätstrasse 28092 ZurichSwitzerland Plant Sciences Department Rothamsted ResearchHarpendenUK Institute of Crop Science National Agriculture and Food Research OrganizationJapan Hokkaido Agricultural Research Center National Agriculture and Food Research OrganizationJapan Biosystems Dynamics and Exchanges TERRA Teaching and Research CenterGembloux Agro-Bio TechUniversity of Liège5030 GemblouxBelgium Plant Sciences TERRA Teaching and Research CenterGembloux Agro-Bio TechUniversity of Liège5030 GemblouxBelgium Graduate School of Agricultural and Life Sciences The University of Tokyo1-1-1 Midori-choNishitokyo CityTokyoJapan Department of Computer Science University of SaskatchewanCanada Department of Plant Sciences University of SaskatchewanCanada College of Agriculture and Life Sciences University of ArizonaTucsonArizona

The Global Wheat Head Detection(GWHD)dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/*** an associated competition hosted in Kaggle,GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science *** this first experience,a few avenues for improvements have been identified regarding data size,head diversity,and label *** address these issues,the 2020 dataset has been reexamined,relabeled,and complemented by adding 1722 images from 5 additional countries,allowing for 81,553 additional wheat *** now release in 2021 a new version of the Global Wheat Head Detection dataset,which is bigger,more diverse,and less noisy than the GWHD_2020 version.

关键词： Wheat adding release

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