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

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

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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

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Assessment of the influence of global dimming on the photosynthetic production of rice based on three-dimensional modeling

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Science China Earth Sciences 2011年第2期54卷 290-297页

作者： zheng bangyou MA YunTao LI BaoGuo GUO Yan DENG QiYun Key Laboratory of Plant-Soil Interactions Ministry of Education College of Resources and Environment China Agricultural University Beijing 100193 China China National Hybrid Rice R & D Center Changsha 410125 China

Observations indicate that global radiation reaching the Earth's surface has gradually decreased over the past several decades(i.e.,the "global dimming" phenomenon),while the fraction of diffuse radiation in global radiation has increased.It has been found that the increase in diffuse fraction significantly increases photosynthetic production in the canopy,but the mechanism has not been clarified.A three-dimensional light distribution model considering the geometry of incident radiation was employed in this study.This model was used to simulate the light distribution and potential photosynthetic production of rice canopy.The results indicate that the potential photosynthetic production of rice significantly increases in response to an increase in the diffuse fraction as long as global radiation does not decline greatly.The "fertilization effect" of diffuse radiation results from the reduction of leaf area with photosynthetic photon flux density(PPFD) below the light compensation point in response to an increasing diffuse fraction,and an increase in the leaf area with PPFD on the linear part of the light response curve.The increase in the diffuse fraction results in a significant increase in the photosynthetic rate of the upper canopy.High-performance computation is an easy-to-use and economic approach to overcome the computational constraints of the model.

关键词：光合生产潜力三维建模光合作用水稻评估分布模型群体光合速率地球表面

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Detecting Sorghum Plant and Head Features from Multispectral UAV Imagery

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Plant Phenomics 2021年第1期3卷 337-350页

作者： Yan Zhao Bangyou Zheng Scott CChapman Kenneth Laws Barbara George-Jaeggli Graeme LHammer David RJordan Andries B.Potgieter The University of Queensland Queensland Alliance for Agriculture and Food InnovationCentre for Crop ScienceGattonQueensland 4343Australia CSIRO Agriculture and Food St.LuciaQueensland 4072Australia The University of Queensland School of Agriculture and Food SciencesSt.LuciaQueensland 4072Australia Department of Agriculture and Fisheries Agri-Science QueenslandWarwickQueensland 4370Australia

In plant breeding,unmanned aerial vehicles(UAVs)carrying multispectral cameras have demonstrated increasing utility for high-throughput phenotyping(HTP)to aid the interpretation of genotype and environment effects on morphological,biochemical,and physiological traits.A key constraint remains the reduced resolution and quality extracted from“stitched”mosaics generated from UAV missions across large areas.This can be addressed by generating high-quality reflectance data from a single nadir image per plot.In this study,a pipeline was developed to derive reflectance data from raw multispectral UAV images that preserve the original high spatial and spectral resolutions and to use these for phenotyping applications.Sequential steps involved(i)imagery calibration,(ii)spectral band alignment,(iii)backward calculation,(iv)plot segmentation,and(v)application.Each step was designed and optimised to estimate the number of plants and count sorghum heads within each breeding plot.Using a derived nadir image of each plot,the coefficients of determination were 0.90 and 0.86 for estimates of the number of sorghum plants and heads,respectively.Furthermore,the reflectance information acquired from the different spectral bands showed appreciably high discriminative ability for sorghum head colours(i.e.,red and white).Deployment of this pipeline allowed accurate segmentation of crop organs at the canopy level across many diverse field plots with minimal training needed from machine learning approaches.

关键词： breeding backward Plant

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Characterization of peach tree crown by using high-resolution images from an unmanned aerial vehicle

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Horticulture Research 2018年第1期5卷 22-31页

作者： Yue Mu Yuichiro Fujii Daisuke Takata Bangyou Zheng Koji Noshita Kiyoshi Honda Seishi Ninomiya Wei Guo International Field Phenomics Research Laboratory Institute for Sustainable Agro-ecosystem ServicesThe University of Tokyo1-1-1 Midori-choNishi-TokyoTokyo188-0002Japan Research Institute for Agriculture Okayama Prefectural Technology Center for AgricultureForestry and Fisheries1174-1 KodaokiAkaiwaOkayama709-0801Japan Fukushima University 1 KanayagawaFukushimaFukushima960-1248Japan CSIRO Agriculture and Food Queensland Bioscience Precinct306 Carmody RoadSt.LuciaQLD4067Australia JST PRESTO Kawaguchi Center Building4-1-8 HonchoKawaguchiSaitama332-0012Japan Graduate School of Agricultural and Life Sciences The University of Tokyo1-1-1 YayoiBunkyoTokyo113-8657Japan International Digital Earth Applied Science Research Center Chubu University1200 MatsumotochoKasugaiAichi-ken487-8501Japan

In orchards, measuring crown characteristics is essential for monitoring the dynamics of tree growth and optimizing farm management. However, it lacks a rapid and reliable method of extracting the features of trees with an irregular crown shape such as trained peach trees. Here, we propose an efficient method of segmenting the individual trees and measuring the crown width and crown projection area (CPA) of peach trees with time-series information, based on gathered images. The images of peach trees were collected by unmanned aerial vehicles in an orchard in Okayama, Japan, and then the digital surface model was generated by using a Structure from Motion (SfM) and Multi-View Stereo (MVS) based software. After individual trees were identified through the use of an adaptive threshold and marker-controlled watershed segmentation in the digital surface model, the crown widths and CPA were calculated, and the accuracy was evaluated against manual delineation and field measurement, respectively. Taking manual delineation of 12 trees as reference, the root-mean-square errors of the proposed method were 0.08 m (R^(2) = 0.99) and 0.15 m (R^(2) = 0.93) for the two orthogonal crown widths, and 3.87 m2 for CPA (R^(2) = 0.89), while those taking field measurement of 44 trees as reference were 0.47 m (R^(2) = 0.91), 0.51 m (R^(2) = 0.74), and 4.96 m2 (R^(2) = 0.88). The change of growth rate of CPA showed that the peach trees grew faster from May to July than from July to September, with a wide variation in relative growth rates among trees. Not only can this method save labour by replacing field measurement, but also it can allow farmers to monitor the growth of orchard trees dynamically.

关键词： crown tree watershed

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A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting

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Plant Phenomics 2019年第1期1卷 1-14页

作者： 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 Department of Mechanical Engineering Iowa State UniversityAmesIAUSA 2Department of Computer ScienceIowa State UniversityAmesIAUSA 3CSIRO Agriculture and FoodSt.LuciaQLDAustralia 4School of Agriculture and Food SciencesThe University of QueenslandGattonQLD 4343Australia 5Queensland Alliance for Agriculture and Food Innovation(QAAFI)The University of QueenslandGattonQLDAustralia 6Queensland Alliance for Agriculture and Food Innovation(QAAFI)The University of QueenslandWarwickQLDAustralia 7Department of AgronomyIowa State UniversityAmesIAUSA 8International Field Phenomics Research LaboratoryInstitute for Sustainable Agro-Ecosystem Services

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(𝑅2 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.

关键词： Deep breeding network

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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 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

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复杂关联路口交通组织与管控优化

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天津科技 2021年第1期48卷 27-30页

作者：廖邦友郑才林安康市市政园林处陕西安康725000

山区城市受地形地貌、历史、文化等诸多因素影响,道路标准总体偏低、异形交叉路口相对较多,交通出行结构非机动车占比较大,交通管理水平相对较低,针对这一系列现状导致的交通组织及管控难的问题,以安康中心城市汉江一桥关联路口为例,在... 详细信息

山区城市受地形地貌、历史、文化等诸多因素影响,道路标准总体偏低、异形交叉路口相对较多,交通出行结构非机动车占比较大,交通管理水平相对较低,针对这一系列现状导致的交通组织及管控难的问题,以安康中心城市汉江一桥关联路口为例,在对4个典型交叉路口交通现状进行调研、分析的基础上,提出了“以交通信号系统平台为支撑,信号协调优化与交叉口细节优化”为主要手段的交通组织及管控优化方案,并将优化前后运行情况进行对比评估,结果表明,优化后的车辆排队长度与延误时间明显降低,方案切实可行。

关键词：城市道路关联路口交通优化

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城市隧道渗水原因分析及治理措施

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市政设施管理 2018年第4期 8-11页

作者：郑才林廖邦友西安市政道桥建设有限公司

本文以安康市城市隧道-香溪隧道的渗漏水和隧道洞内装饰污染病害为研究对象,通过有针对性的调查和分析,得出病害形成的原因,再通过查阅国内外相关文献资料、现行相关规范及国内类似工程实施的成功经验,结合香溪隧道渗水及洞内装饰污染... 详细信息

本文以安康市城市隧道-香溪隧道的渗漏水和隧道洞内装饰污染病害为研究对象,通过有针对性的调查和分析,得出病害形成的原因,再通过查阅国内外相关文献资料、现行相关规范及国内类似工程实施的成功经验,结合香溪隧道渗水及洞内装饰污染的实际情况,提出了较详细的隧道渗水治理及洞内装饰污染治理方案。

关键词：隧道渗水污染病害治理

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基于非线性支持向量机的隧道煤与瓦斯突出危险性预测

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现代隧道技术 2020年第2期57卷 20-25页

作者：朱宝合郑邦友戴亦军刘灿中建隧道建设有限公司重庆401320

为了有效地预测桃子垭隧道揭煤段是否存在煤与瓦斯突出危险性,文章根据煤与瓦斯突出综合假说及《防治煤与瓦斯突出规定》,确定了影响煤与瓦斯突出的9个关键因素。由于评价因子与突出危险程度之间存在着复杂的非线性映射关系,因此选择了... 详细信息

为了有效地预测桃子垭隧道揭煤段是否存在煤与瓦斯突出危险性,文章根据煤与瓦斯突出综合假说及《防治煤与瓦斯突出规定》,确定了影响煤与瓦斯突出的9个关键因素。由于评价因子与突出危险程度之间存在着复杂的非线性映射关系,因此选择了非线性支持向量机(SVM)方法对隧道煤与瓦斯突出危险性进行预测研究。结合项目实际情况确定了各训练样本的具体参数,采用单项指标法、最优分类决策函数及MATLAB SVM Toolbox软件对选定的训练样本进行了煤与瓦斯突出危险性预测对比。通过N7和N8两个测点的预测结果表明,桃子垭隧道揭煤段存在煤与瓦斯突出危险性,必须做好相应的揭煤防突工作。

关键词：瓦斯隧道煤与瓦斯突出小样本评估支持向量机(SVM) 单项指标法

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