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

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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 wheat.Use 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 process.Because of the easy availability of red–green–blue images,machine learning approaches have been applied to replacing manual counting.However,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 counting.In this paper,we provide a comprehensive pipeline from data collection to model deployment in deep-learning-assisted panicle yield estimation for sorghum.This pipeline provides a basis from data collection and model training,to model validation and model deployment in commercial fields.Accurate model training is the foundation of the pipeline.However,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 solution.Although we demonstrate our pipeline in a sorghum field,the pipeline can be generalized to other grain species.Our 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

Self-Supervised Plant Phenotyping by Combining Domain Adaptation with 3D Plant Model Simulations: Application to Wheat Leaf Counting at Seedling Stage

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Plant Phenomics 2023年第2期5卷 226-238页

作者： Yinglun Li Xiaohai Zhan Shouyang Liu Hao Lu Ruibo Jiang Wei Guo scott chapman Yufeng Ge Benoit de Solan Yanfeng Ding Frédéric Baret Plant Phenomics Research Centre Academy for Advanced Interdisciplinary StudiesJiangsu Collaborative Innovation Center for Modern Crop ProductionNanjing Agricultural UniversityNanjingChina Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina Graduate School of Agricultural and Life Sciences The University of Tokyo1-1-1 Midori-choNishitokyo CityTokyoJapan School of Agriculture and Food Sciences The University of QueenslandSt.LuciaQueensland 4072Australia Department of Biological Systems Engineering University of Nebraska-LincolnLincolnNebraska 68583United States INRAE Avignon UniversitéUMR EMMAHUMT CAPTE228route de l’aérodrome-CS 4050984914 Avignon Cedex 9France ARVALIS Institut du Végétal 3 Rue Joseph et Marie Hackin75116 ParisFrance

The number of leaves at a given time is important to characterize plant growth and development.In this work,we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB images.The digital plant phenotyping platform was used to simulate a large and diverse dataset of RGB images and corresponding leaf tip labels of wheat plants at seedling stages(150,000 images with over 2 million labels).The realism of the images was then improved using domain adaptation methods before training deep learning models.The results demonstrate the efficiency of the proposed method evaluated on a diverse test dataset,collecting measurements from 5 countries obtained under different environments,growth stages,and lighting conditions with different cameras(450 images with over 2,162 labels).Among the 6 combinations of deep learning models and domain adaptation techniques,the Faster-RCNN model with cycle-consistent generative adversarial network adaptation technique provided the best performance(R^(2)=0.94,root mean square error=8.7).Complementary studies show that it is essential to simulate images with sufficient realism(background,leaf texture,and lighting conditions)before applying domain adaptation techniques.Furthermore,the spatial resolution should be better than 0.6 mm per pixel to identify leaf tips.The method is claimed to be self-supervised since no manual labeling is required for model training.The self-supervised phenotyping approach developed here offers great potential for addressing a wide range of plant phenotyping problems.The trained networks are available at https://***/YinglunLi/Wheat-leaf-tip-detection.

关键词： Wheat seedling Plant

Estimating Photosynthetic Attributes from High-Throughput Canopy Hyperspectral Sensing in Sorghum

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Plant Phenomics 2022年第1期4卷 324-341页

作者： Xiaoyu Zhi Sean Reynolds Massey-Reed Alex Wu Andries Potgieter Andrew Borrell Colleen Hunt David Jordan Yan Zhao scott chapman Graeme Hammer Barbara George-Jaeggli The University of Queensland Queensland Alliance for Agriculture and Food Innovation(QAAFI)Hermitage Research FacilityWarwickQLDAustralia The University of Queensland Queensland Alliance for Agriculture and Food Innovation(QAAFI)St LuciaQLDAustralia The University of Queensland Queensland Alliance for Agriculture and Food Innovation(QAAFI)GattonQLDAustralia Agri-Science Queensland Department of Agriculture and Fisheries(DAF)Hermitage Research FacilityWarwickQLDAustralia School of Agriculture and Food Sciences The University of QueenslandGattonQLDAustralia

Sorghum,a genetically diverse C_(4) cereal,is an ideal model to study natural variation in photosynthetic capacity.Specific leaf nitrogen(SLN)and leaf mass per leaf area(LMA),as well as,maximal rates of Rubisco carboxylation(V cmax),phosphoenolpyruvate(PEP)carboxylation(V pmax),and electron transport(J max),quantified using a C_(4) photosynthesis model,were evaluated in two field-grown training sets(n=169 plots including 124 genotypes)in 2019 and 2020.

关键词： capacity maximal Sorghum

A Generic Model to Estimate Wheat LAI over Growing Season Regardless of the Soil-Type Background

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Plant Phenomics 2023年第2期5卷 333-352页

作者： Qiaomin Chen Bangyou Zheng Karine Chenu scott C.chapman School of Agriculture and Food Sciences The University of QueenslandSt LuciaQLDAustralia Agriculture and Food CSIROQueensland Bioscience PrecinctSt LuciaQLDAustralia The University of Queensland Queensland Alliance for Agriculture and Food InnovationToowoombaQLDAustralia

It is valuable to develop a generic model that can accurately estimate the leaf area index(LAI)of wheat from unmanned aerial vehicle-based multispectral data for diverse soil backgrounds without any ground calibration.To achieve this objective,2 strategies were investigated to improve our existing random forest regression(RFR)model,which was trained with simulations from a radiative transfer model(PROSAIL).The 2 strategies consisted of(a)broadening the reflectance domain of soil background to generate training data and(b)finding an appropriate set of indicators(band reflectance and/or vegetation indices)as inputs of the RFR model.The RFR models were tested in diverse soils representing varying soil types in Australia.Simulation analysis indicated that adopting both strategies resulted in a generic model that can provide accurate estimation for wheat LAI and is resistant to changes in soil background.From validation on 2 years of field trials,this model achieved high prediction accuracy for LAI over the entire crop cycle(LAI up to 7 m^(2)m^(−2))(root mean square error(RMSE):0.23 to 0.89 m^(2)m^(−2)),including for sparse canopy(LAI less than 0.3 m^(2)m^(−2))grown on different soil types(RMSE:0.02 to 0.25 m^(2)m^(−2)).The model reliably captured the seasonal pattern of LAI dynamics for different treatments in terms of genotypes,plant densities,and water–nitrogen managements(correlation coefficient:0.82 to 0.98).With appropriate adaptations,this framework can be adjusted to any type of sensors to estimate various traits for various species(including but not limited to LAI of wheat)in associated disciplines,e.g.,crop breeding,precision agriculture,etc.

关键词： soil Wheat breeding

Unsupervised Plot-Scale LAI Phenotyping via UAV-Based Imaging, Modelling, and Machine Learning

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Plant Phenomics 2022年第1期4卷 196-214页

作者： Qiaomin Chen Bangyou Zheng Karine Chenu Pengcheng Hu scott C.chapman School of Agriculture and Food Sciences The University of QueenslandSt LuciaQLDAustralia Agriculture and Food CSIROQueensland Bioscience PrecinctSt LuciaQLDAustralia The University of Queensland Queensland Alliance for Agriculture and Food InnovationToowoombaQLDAustralia

High-throughput phenotyping has become the frontier to accelerate breeding through linking genetics to crop growth estimation,which requires accurate estimation of leaf area index(LAI).This study developed a hybrid method to train the random forest regression(RFR)models with synthetic datasets generated by a radiative transfer model to estimate LAI from UAV-based multispectral images.

关键词： breeding forest estimation

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/institutions.With an associated competition hosted in Kaggle,GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities.From this first experience,a few avenues for improvements have been identified regarding data size,head diversity,and label reliability.To 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 heads.We 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

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 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://***/and aimed at developing and benchmarking methods for wheat head detection.

关键词： Wheat wheat maturity

我喜欢π：在《美国数学月刊》的一段简史和一个文选（I）

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数学译林 2017年第3期36卷 207-218页

作者： JonathanM.Borwein scott T.chapman 李灵芝澳大利亚纽卡索(Newcastle)大学数学与物理科学学院北得克萨斯大学不详

2015年3月，为了庆祝一个特殊的“大”日子π日（3／14／15）和2015年的美国数学协会（the Mathematical Association of America，简称MAA）成立100周年，我们在《美国数学月刊（theAmerican Mathematical Monthly）》上花了一些篇幅... 详细信息

2015年3月，为了庆祝一个特殊的“大”日子π日（3／14／15）和2015年的美国数学协会（the Mathematical Association of America，简称MAA）成立100周年，我们在《美国数学月刊（theAmerican Mathematical Monthly）》上花了一些篇幅回顾了常数π辉煌的历史．

关键词：数学美国数π 月刊简史

我喜欢π：在《美国数学月刊》的一段简史和一个文选（II）

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数学译林 2017年第4期36卷 311-319,373页

作者： Jonathan M. Borwein scott T. chapman 李灵芝陆柱家澳大利亚纽卡索(Newcastle)大学数学与物理科学学院不详

3．在1894—1944年《月刊》上的π这时期产生了文选中的20篇论文,1894年7月的《月刊》在它优雅的页面上包含了关于π的最尴尬的文章.只有“应作者要求出版”的标记，作者表明在1889年它是受版权保护的，它起源于著名的通常容易令人误解... 详细信息

3．在1894—1944年《月刊》上的π这时期产生了文选中的20篇论文,1894年7月的《月刊》在它优雅的页面上包含了关于π的最尴尬的文章.只有“应作者要求出版”的标记，作者表明在1889年它是受版权保护的，它起源于著名的通常容易令人误解的故事：

关键词：月刊简史数学美国 π 版权保护作者