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Remote sensing of quality traits in cereal and arable production systems:A review

The Crop Journal 2024年第1期12卷 45-57页

作者： Zhenhai Li Chengzhi Fan Yu Zhao xiuliang jin Raffaele Casa Wenjiang Huang Xiaoyu Song Gerald Blasch Guijun Yang James Taylor Zhenhong Li College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdao 266590ShandongChina Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs Information Technology Research CenterBeijing Academy of Agriculture and Forestry SciencesBeijing 100097China Institute of Crop Sciences Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and EcologyMinistry of Agriculture and Rural AffairsBeijing 100081China DAFNE Universitàdella TusciaVia San Camillo de Lellis01100 ViterboItaly Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China International Maize and Wheat Improvement Center(CIMMYT) PO Box 5689Addis AbabaEthiopia ITAP Univ.MontpellierINRAEInstitut AgroMontpellier 34000France College of Geological Engineering and Geomatics Chang’an UniversityXi’an 710054ShaanxiChina

Cereal is an essential source of calories and protein for the global population.Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers,grading harvest and categorised storage for enterprises,future trading prices,and policy planning.The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits.Many studies have also proposed models and methods for predicting such traits based on multiplatform remote sensing data.In this paper,the key quality traits that are of interest to producers and consumers are introduced.The literature related to grain quality prediction was analyzed in detail,and a review was conducted on remote sensing platforms,commonly used methods,potential gaps,and future trends in crop quality prediction.This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.

关键词： Remote sensing Quality traits Grain protein Cereal

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High-throughput phenotyping of plant leaf morphological, physiological,and biochemical traits on multiple scales using optical sensing

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作物学报:英文版 2023年第5期11卷 1303-1318页

作者： Huichun Zhang Lu Wang xiuliang jin Liming Bian Yufeng Ge College of Mechanical and Electronic Engineering Nanjing Forestry UniversityNanjing 210037JiangsuChina Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources Nanjing Forestry UniversityNanjing 210037JiangsuChina Institute of Crop Sciences Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and EcologyMinistry of AgricultureBeijing 100081China College of Forestry Nanjing Forestry UniversityNanjing 210037JiangsuChina Co-Innovation Center for Sustainable Forestry in Southern China and Key Laboratory of Forest Genetics&Biotechnology of the Ministry of Education Nanjing Forestry UniversityNanjing 210037JiangsuChina Department of Biological Systems Engineering University of Nebraska-LincolnNE 68583USA

Acquisition of plant phenotypic information facilitates plant breeding, sheds light on gene action, and can be applied to optimize the quality of agricultural and forestry products. Because leaves often show the fastest responses to external environmental stimuli, leaf phenotypic traits are indicators of plant growth,health, and stress levels. Combination of new imaging sensors, image processing, and data analytics permits measurement over the full life span of plants at high temporal resolution and at several organizational levels from organs to individual plants to field populations of plants. We review the optical sensors and associated data analytics used for measuring morphological, physiological, and biochemical traits of plant leaves on multiple scales. We summarize the characteristics, advantages and limitations of optical sensing and data-processing methods applied in various plant phenotyping scenarios. Finally, we discuss the future prospects of plant leaf phenotyping research. This review aims to help researchers choose appropriate optical sensors and data processing methods to acquire plant leaf phenotypes rapidly,accurately, and cost-effectively.

关键词： Leaf traits Optical sensing Image processing Machine learning Artificial intelligence

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Research on the Generation Mechanism and Suppression Method of Aerodynamic Noise in Expansion Cavity Based on Hybrid Method

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 2747-2772页

作者： Haitao Liu Jiaming Wang xiuliang Zhang Yanji Jiang Qian Xiao School of Mechanotronics and Vehicle Engineering East China Jiaotong UniversityNanchang330013China Suzhou Automotive Research Institue Tsinghua UniversitySuzhou215131China

The expansion chamber serves as the primary silencing structure within the exhaust pipeline.However,it can also act as a sound-emitting structure when subjected to airflow.This article presents a hybrid method for numerically simulating and analyzing the unsteady flow and aerodynamic noise in an expansion chamber under the influence of airflow.A fluid simulation model is established,utilizing the Large Eddy Simulation(LES)method to calculate the unsteady flow within the expansion chamber.The simulation results effectively capture the development and changes of the unsteady flow and vorticity inside the cavity,exhibiting a high level of consistency with experimental observations.To calculate the aerodynamic noise sources within the cavity,the flow field results are integrated using the method of integral interpolation and inserted into the acoustic grid.The acoustic analogy method is then employed to determine the aerodynamic noise sources.An acoustic simulation model is established,and the flow noise source is imported into the sound field grid to calculate the sound pressure at the far-field response point.The calculated sound pressure levels and resonance frequencies show good agreement with the experimental results.To address the issue of airflow regeneration noise within the cavity,perforated tubes are selected as a means of noise suppression.An experimental platformfor airflow regeneration noise is constructed,and experimental samples are processed to analyze and verify the noise suppression effect of perforated tube expansion cavities under different airflow velocities.The research findings indicate that the perforated tube expansion cavity can effectively suppress low-frequency aerodynamic noise within the cavity by impeding the formation of strong shear layers.Moreover,the semi-perforated tube expansion cavity demonstrates the most effective suppression of aerodynamic noise.

关键词： Expansion cavity aerodynamic noise numerical simulation hybrid method perforated tube

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Crop phenotyping studies with application to crop monitoring

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The Crop Journal 2022年第5期10卷 1221-1223页

作者： xiuliang jin Wanneng Yang John H.Doonan Clement Atzberger Institute of Crop Sciences Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and EcologyMinistry of AgricultureBeijing 100081China National Key Laboratory of Crop Genetic Improvement National Center of Plant Gene ResearchHuazhong Agricultural UniversityWuhan 430070HubeiChina The National Plant Phenomics Centre Institute of BiologicalRural and Environmental Sciences(IBERS)Aberystwyth UniversityAberystwythCeredigion SY233FLUK Institute of Geomatics University of Natural Resources and Life Sciences(BOKU)Peter Jordan Straße 821190 ViennaAustria

1.Introduction Crop yield must urgently be sustainably increased to accommodate a rising global population and anticipated climate change in the coming decades,in the face of plant stresses and limited resources[1].Co... 详细信息

关键词： breeding Crop efficiency.

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TaRLK-6A promotes Fusarium crown rot resistance in wheat

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Journal of Integrative Plant Biology 2024年第1期66卷 12-16页

作者： Haijun Qi xiuliang Zhu Wenbiao Shen Xia Yang Chaozhong Zhang Genying Li Feng Chen Xuening Wei Zengyan Zhang Institute of Special Animal and Plant Sciences Chinese Academy of Agricultural SciencesChangchun 130000China The National Key Facility for Crop Gene Resources and Genetic Improvement Institute of Crop SciencesChinese Academy of Agricultural SciencesBeijing 100081China College of Life Science Nanjing Agricultural UniversityNanjing 210095China Agronomy College/National Key Laboratory of Wheat and Maize Crop Science/CIMMYT‐China(Henan)Joint Center of Wheat and Maize Henan Agricultural UniversityZhengzhou 450002China Department of Plant Sciences University of CaliforniaDavisCA 95616USA Crop Research Institute Shandong Academy of Agricultural SciencesJinan 250100China

Fusariumcrown rot(FCR),mainly caused by the soil‐borne fungus Fusarium pseudograminearum,is a devastatingdisease of wheat(Triticum aestivum(Ta)).Fusariumcrown rotcauses substantial yield losses and generates mycotoxins inwheat grains that can cause serious health problems in hu-mans and livestock(Powell et al.,2017).Identifying genes thatprovide resistance toF.pseudograminearumand character-izing the molecular mechanisms underlying such resistance arekey to breeding wheat varieties that are resistant to this dev-astating fungus.

关键词： aestivum breeding soil

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Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index

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The Crop Journal 2020年第1期8卷 87-97页

作者： xiuliang jin Zhenhai Li Haikuan Feng Zhibin Ren Shaokun Li Institute of Crop Sciences Chinese Academy of Agricultural SciencesKey Laboratory of Crop Physiology and EcologyMinistry of AgricultureBeijing 100081China Beijing Research Center for Information Technology in Agriculture Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China Northeast Institute of Geography and Agroecology Chinese Academy of SciencesChangchun 130102JilinChina

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.

关键词： Biomass estimation Maize Vegetation indices Deep neural network algorithm LAI

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Time series canopy phenotyping enables the identification of genetic variants controlling dynamic phenotypes in soybean

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Journal of Integrative Plant Biology 2023年第1期65卷 117-132页

作者： Delin Li Dong Bai Yu Tian Ying-Hui Li Chaosen Zhao Qi Wang Shiyu Guo Yongzhe Gu Xiaoyan Luan Ruizhen Wang jinliang Yang Malcolm J.Hawkesford James C.Schnable xiuliang jin Li-Juan Qiu The National Key Facility for Crop Gene Resources and Genetic Improvement(NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement(MOA)/Key Laboratory of Soybean Biology(Beijing)(MOA) Institute of Crop Science Chinese Academy of Agricultural SciencesBeijing 100081 China Crops Research Institute of Jiangxi Academy of Agricultural Sciences Nanchang 330200 China College of Agriculture Northeast Agricultural University Harbin 150030 China Soybean Research Institute Heilongjiang Academy of Agricultural Sciences Harbin 150086 China Department of Agronomy and Horticulture University of Nebraska-Lincoln Lincoln Nebraska 68583 USA Plant Sciences Department Rothamsted ResearchWest CommonHarpendenHertfordshire AL52JQ UK

Advances in plant phenotyping technologies are dramatically reducing the marginal costs of collecting multiple phenotypic measurements across several time points.Yet,most current approaches and best statistical practices implemented to link genetic and phenotypic variation in plants have been developed in an era of single-time-point data.Here,we used time-series phenotypic data collected with an unmanned aircraft system for a large panel of soybean(Glycine max(L.)Merr.)varieties to identify previously uncharacterized loci.Specifically,we focused on the dissection of canopy coverage(CC)variation from this rich data set.We also inferred the speed of canopy closure,an additional dimension of CC,from the time-series data,as it may represent an important trait for weed control.Genome-wide association studies(GWASs)identified 35 loci exhibiting dynamic associations with CC across developmental stages.The time-series data enabled the identification of 10 known flowering time and plant height quantitative trait loci(QTLs)detected in previous studies of adult plants and the identification of novel QTLs influencing CC.These novel QTLs were disproportionately likely to act earlier in development,which may explain why they were missed in previous single-time-point studies.Moreover,this time-series data set contributed to the high accuracy of the GWASs,which we evaluated by permutation tests,as evidenced by the repeated identification of loci across multiple time points.Two novel loci showed evidence of adaptive selection during domestication,with different genotypes/haplotypes favored in different geographic regions.In summary,the time-series data,with soybean CC as an example,improved the accuracy and statistical power to dissect the genetic basis of traits and offered a promising opportunity for crop breeding with quantitative growth curves.

关键词： canopy coverage dynamic regulation GWAS soybean time series unmanned aircraft system

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面向平缝工艺信息融合的知识图谱构建方法

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纺织学报 2024年第4期45卷 195-203页

作者：郑小虎刘正好刘冰张洁徐修亮刘希东华大学人工智能研究院上海201620 纺织工业人工智能技术教育部工程研究中心上海201620 上海工业大数据与智能系统工程技术研究中心上海201620 东华大学机械工程学院上海201620 杭州中服科创研究院有限公司浙江杭州311199 上海富山精密机械科技有限公司上海201599 东华大学信息科学与技术学院上海201620

针对缝纫工艺流程链路长,生产要素多样,工艺知识分散化的特点,首先,提出缝纫工艺信息组织建模方法,面向多源异构平缝工艺信息构建本体模型;其次,研究基于面料力学性能的平缝工艺推荐方法,构建面料基础性能、缝制性能知识体系,结合面料... 详细信息

针对缝纫工艺流程链路长,生产要素多样,工艺知识分散化的特点,首先,提出缝纫工艺信息组织建模方法,面向多源异构平缝工艺信息构建本体模型;其次,研究基于面料力学性能的平缝工艺推荐方法,构建面料基础性能、缝制性能知识体系,结合面料层次分类方法和实验参数分析,形成力学性能参数与缝制参数之间的数学模型;在此基础上,以专家经验文本、实验测量数据为原始数据,构建面向平缝工艺信息融合的知识图谱,开发WEB系统展开应用验证。结果表明:所构建的工艺推荐方法实现了对面料平整度和最大缝缩率的预测,知识融合系统实现了缝纫工艺知识的智能搜索和缝制参数的智能推荐,为工艺数据集成、装备故障运维、工艺路线设计、产品质量控制提供决策参考。

关键词：服装缝纫知识图谱工艺知识管理平缝工艺知识推荐缝制参数

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电化学渗氮对不锈钢表面结构的影响

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材料研究学报 2024年第3期38卷 161-167页

作者：陈真勇魏欣欣徐妍婷张波马秀良中国科学技术大学材料科学与工程学院沈阳110016 中国科学院金属研究所沈阳材料科学国家研究中心沈阳110016 松山湖材料实验室大湾区显微科学与技术研究中心东莞523830 中国科学院物理研究所北京100190

使用原子力显微技术和透射电子显微技术等手段,研究了电化学渗氮的304L不锈钢多尺度下表面结构的演变。结果表明,在电化学渗氮电位下304L不锈钢表面发生了钝化膜的局部阴极还原、金属基体的微区阳极溶解以及溶解的金属阳离子再沉积,从... 详细信息

使用原子力显微技术和透射电子显微技术等手段,研究了电化学渗氮的304L不锈钢多尺度下表面结构的演变。结果表明,在电化学渗氮电位下304L不锈钢表面发生了钝化膜的局部阴极还原、金属基体的微区阳极溶解以及溶解的金属阳离子再沉积,从而形成了起伏幅度为几十纳米的微观粗糙表面。在扫描透射成像模式下进行的超级能谱分析结果表明,表面沉积物的主要组成是Fe的氧化物,进一步佐证了在渗氮过程中发生了Fe的微区阳极溶解和Fe阳离子的再沉积过程。

关键词：材料表面与界面表面结构演变电化学渗氮透射电子显微技术原子力显微技术 304L不锈钢

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基于无人机多源影像数据的灌浆期人工合成小麦抗旱性评价

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中国农业科学 2024年第9期57卷 1674-1686页

作者：燕雯金秀良李龙徐子涵苏悦张跃强景蕊莲毛新国孙黛珍山西农业大学农学院中国农业科学院作物科学研究所/作物基因资源与育种全国重点实验室新疆农业科学院核技术生物技术研究所/农业农村部荒漠绿洲区作物生理生态与耕作重点实验室/新疆作物生物技术重点实验室

【目的】基于无人机多源影像及产量数据评价人工合成小麦种质的抗旱性，优选高通量抗旱性鉴定指标，发掘抗旱人工合成小麦种质资源，为加快拓展小麦抗旱遗传资源、提升旱地小麦育种水平提供技术支撑和种质材料。【方法】以80份人工合成... 详细信息

【目的】基于无人机多源影像及产量数据评价人工合成小麦种质的抗旱性，优选高通量抗旱性鉴定指标，发掘抗旱人工合成小麦种质资源，为加快拓展小麦抗旱遗传资源、提升旱地小麦育种水平提供技术支撑和种质材料。【方法】以80份人工合成小麦种质及对照小麦品种新春37为试验材料，在田间进行小区播种，设置干旱和灌溉2种水分处理；利用无人机搭载多光谱及热红外相机采集试验材料灌浆期多源影像进行拼接处理，通过阈值分割等方法提取各试验材料的光谱指数；利用相关性分析和主成分分析鉴选抗旱相关光谱指标，结合单指标及综合评价方法鉴定人工合成小麦种质的抗旱性。【结果】基于无人机多源影像数据提取了80份人工合成小麦种质的19种光谱指数。不同光谱指数抗旱系数与小区产量抗旱指数的相关性分析结果表明，OSAVI的抗旱系数与抗旱指数的关联度最高，NDVI、CIre和NDRE的抗旱系数与抗旱指数的关联度较高。部分光谱指数的抗旱系数间相关性较高，存在冗余信息，通过主成分分析，将19个光谱指数的抗旱系数转换为3个相互独立的综合指标，3个综合指标的贡献度分别为59.6%、12.0%和9.6%。利用加权隶属函数法聚合综合指标，通过公式计算获得各人工合成小麦种质的综合抗旱性度量值。基于抗旱指数鉴定出6份强抗旱人工合成小麦种质，基于综合抗旱性度量值鉴定出5份强抗旱种质，其中，SW004和SW009在2种方法的评价结果中均被评为强抗旱种质。基于OSAVI的抗旱系数对80份人工合成小麦种质进行抗旱性分级，分级结果与基于综合抗旱性度量值的分级结果基本一致。根据OSAVI的抗旱系数鉴定出的6份强抗旱种质中，有5份在基于综合抗旱性度量值分级中也被鉴定为强抗旱种质。【结论】基于无人机多源影像提取的光谱指数NDVI、OSAVI、CIre和NDRE，以及基于光谱指数的综合抗旱性度量值均可用于辅助鉴定小麦种质抗旱性。

关键词：多源影像光谱指数人工合成小麦抗旱性灌浆期

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