|本期目录/Table of Contents|

[1]李远鲲,郭新宇,张颖,等.棉花表型技术研究进展[J].江苏农业科学,2023,51(11):27-36.
 Li Yuankun,et al.Research progress on cotton phenotypic techniques[J].Jiangsu Agricultural Sciences,2023,51(11):27-36.
点击复制

棉花表型技术研究进展(PDF)
分享到:

《江苏农业科学》[ISSN:1002-1302/CN:32-1214/S]

卷:
第51卷
期数:
2023年第11期
页码:
27-36
栏目:
专论与综述
出版日期:
2023-06-05

文章信息/Info

Title:
Research progress on cotton phenotypic techniques
作者:
李远鲲123郭新宇12张颖12顾生浩12张永江3吴升12
1.北京市农林科学院信息技术研究中心数字植物北京市重点实验室,北京 100097; 2.国家农业信息化工程技术研究中心,北京 100097;3.河北农业大学农学院/华北作物改良与调控国家重点实验室/河北省作物生长调控重点实验室,河北保定 071000
Author(s):
Li Yuankunet al
关键词:
棉花表型技术表型平台高通量多尺度表型组学
Keywords:
-
分类号:
S562.01
DOI:
-
文献标志码:
A
摘要:
棉花是重要的纺织原料,表型技术的应用对棉花智慧栽培管理和数字化育种具有重要意义。随着农业监测传感器及表型平台的发展,棉花表型技术研究进入重要阶段。概述了棉花的表型构成和主要表型指标;从图像类表型平台、点云类表型平台2个方面综述了棉花表型获取相关传感器及高通量系统平台的发展现状,总结其适用场景、通量、效率及精度;详细综述了棉花多源表型数据处理技术体系,包括图像、三维建模、机器学习、深度学习以及表型大数据建模等技术方法;总结讨论了当前表型技术在棉花精准栽培管理和数字育种方面的应用进展;展望了棉花表型技术的未来发展趋势。
Abstract:
-

参考文献/References:

[1]Tester M,Langridge P. Breeding technologies to increase crop production in a changing world[J]. Science,2010,327(5967):818-822.
[2]Johannsen W. The genotype conception of heredity[J]. International Journal of Epidemiology,2014,43(4):989-1000.
[3]Zhao C J,Zhang Y,Du J J,et al. Crop phenomics:current status and perspectives[J]. Frontiers in Plant Science,2019,10:714.
[4]赵春江. 植物表型组学大数据及其研究进展[J]. 农业大数据学报,2019,1(2):5-18.
[5]周济,Tardieu F,Pridmore T,等. 植物表型组学:发展、现状与挑战[J]. 南京农业大学学报,2018,41(4):580-588.
[6]Yang W N,Feng H,Zhang X H,et al. Crop phenomics and high-throughput phenotyping:past decades,current challenges,and future perspectives[J]. Molecular Plant,2020,13(2):187-214.
[7]Yeom J,Jung J,Chang A J,et al. Automated open cotton boll detection for yield estimation using unmanned aircraft vehicle (UAV) data[J]. Remote Sensing,2018,10(12):1895.
[8]王莉,杨维才. 陆地棉基因组测序—开辟棉花育种新篇章[J]. 中国科学:生命科学,2015,45(5):517-518.
[9]Cobb J N,DeClerck G,Greenberg A,et al. Next-generation phenotyping:requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement[J]. Theoretical and Applied Genetics,2013,126(4):867-887.
[10]Pabuayon I L B,Sun Y Z,Guo W X,et al. High-throughput phenotyping in cotton:a review[J]. Journal of Cotton Research,2019,2(1):1-9.
[11]臧新山,耿延会,裴文锋,等. 棉花形态性状质量遗传分析与基因定位研究进展[J]. 棉花学报,2018,30(6):473-485.
[12]顾生浩. 棉花功能结构模型建立与新疆棉花产量预测[D]. 北京:中国农业大学,2018:13-14.
[13]Sun S P,Li C Y,Chee P W,et al. Three-dimensional photogrammetric mapping of cotton bolls in situ based on point cloud segmentation and clustering[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2020,160:195-207.
[14]付远志,薛惠云,胡根海,等. 我国棉花株型性状遗传育种研究进展[J]. 江苏农业科学,2019,47(5):16-19.
[15]李建峰,王聪,梁福斌,等. 新疆机采模式下棉花株行距配置对冠层结构指标及产量的影响[J]. 棉花学报,2017,29(2):157-165.
[16]Suo X M,Jiang Y T,Yang M,et al. Artificial neural network to predict leaf population chlorophyll content from cotton plant images[J]. Agricultural Sciences in China,2010,9(1):38-45.
[17]Ballester C,Hornbuckle J,Brinkhoff J,et al. Assessment of in-season cotton nitrogen status and lint yield prediction from unmanned aerial system imagery[J]. Remote Sensing,2017,9(11):1149.
[18]Dong Z Q,Liu Y,Ci B X,et al. Estimation of nitrate nitrogen content in cotton petioles under drip irrigation based on wavelet neural network approach using spectral indices[J]. Plant Methods,2021,17(1):90.
[19]Xu R,Li C Y,Paterson A H. Multispectral imaging and unmanned aerial systems for cotton plant phenotyping[J]. PLoS One,2019,14(2):e0205083.
[20]Nagasubramanian K,Jones S,Singh A K,et al. Plant disease identification using explainable 3D deep learning on hyperspectral images[J]. Plant Methods,2019,15:98.
[21]de Oliveira R H,Dias Milanez C R,Moraes-Dallaqua M A,et al. Boron deficiency inhibits petiole and peduncle cell development and reduces growth of cotton[J]. Journal of Plant Nutrition,2006,29(11):2035-2048.
[22]Dong L H,Guo Q G,Wang P P,et al. Qualitative and quantitative analyses of the colonization characteristics of Bacillus subtilis strain NCD-2 on cotton root[J]. Current Microbiology,2020,77(8):1600-1609.
[23]Yu Y Y,Yuan J G,Wang Q,et al. A study of surface morphology and structure of cotton fibres with soluble immobilized-cellulase treatment[J]. Fibers and Polymers,2014,15(8):1609-1615.
[24]Zhao H B,Kwak J H,Conrad Zhang Z,et al. Studying cellulose fiber structure by SEM,XRD,NMR and acid hydrolysis[J]. Carbohydrate Polymers,2007,68(2):235-241.
[25]van Nhan L,Ma C X,Rui Y K,et al. Phytotoxic mechanism of nanoparticles:destruction of chloroplasts and vascular bundles and alteration of nutrient absorption[J]. Scientific Reports,2015,5:11618.
[26]Zhang M,Han L B,Wang W Y,et al. Overexpression of GhFIM2 propels cotton fiber development by enhancing actin bundle formation[J]. Journal of Integrative Plant Biology,2017,59(8):531-534.
[27]Haleem N,Liu X,Hurren C,et al. Investigating the cotton ring spun yarn structure using micro computerized tomography and digital image processing techniques[J]. Textile Research Journal,2019,89(15):3007-3023.
[28]Adke S,Li C Y,Rasheed K M,et al. Supervised and weakly supervised deep learning for segmentation and counting of cotton bolls using proximal imagery[J]. Sensors,2022,22(10):3688.
[29]陈俊英,陈硕博,张智韬,等. 无人机多光谱遥感反演花蕾期棉花光合参数研究[J]. 农业机械学报,2018,49(10):230-239.
[30]Shah N,Jain S. Detection of disease in cotton leaf using artificial neural network[C]//2019 Amity International Conference on Artificial Intelligence (AICAI).Dubai,United Arab Emirates,2019:473-476.
[31]Jenifa A,Ramalakshmi R,Ramachandran V. Classification of cotton leaf disease using multi-support vector machine[C]//2019 IEEE International Conference on Intelligent Techniques in Control,Optimization and Signal Processing (INCOS).Tamilnadu,India,2020:1-4.
[32]Jiang Y,Li C Y,Paterson A H.High throughput phenotyping of cotton plant height using depth images under field conditions[J]. Computers and Electronics in Agriculture,2016,130:57-68.
[33]阳旭,胡松涛,王应华,等. 利用多时序激光点云数据提取棉花表型参数方法[J]. 智慧农业(中英文),2021,3(1):51-62.
[34]Paproki A,Sirault X,Berry S,et al. A novel mesh processing based technique for 3D plant analysis[J]. BMC Plant Biology,2012,12:63.
[35]黄成龙,李曜辰,骆树康,等. 基于结构光三维点云的棉花幼苗叶片性状解析方法[J]. 农业机械学报,2019,50(8):243-248,288.
[36]Wu J,Wu Q,Pagès L,et al. RhizoChamber-Monitor:a robotic platform and software enabling characterization of root growth[J]. Plant Methods,2018,14:44.
[37]Sun S P,Li C Y,Paterson A. In-field high-throughput phenotyping of cotton plant height using LiDAR[J]. Remote Sensing,2017,9(4):377.
[38]颜安,郭涛,陈全家,等. 基于无人机影像的棉花株高预测[J]. 新疆农业科学,2020,57(8):1493-1502.
[39]Huang Y,Brand H J,Sui R,et al. Cotton yield estimation using very high-resolution digital images acquired with a low-cost small unmanned aerial vehicle[J]. Transactions of the ASABE,2016,59(6):1563-1574.
[40]Thompson A L,Thorp K R,Conley M M,et al. Comparing nadir and multi-angle view sensor technologies for measuring in-field plant height of upland cotton[J]. Remote Sensing,2019,11(6):700.
[41]Sun S P,Li C Y,Chee P W,et al. High resolution 3D terrestrial LiDAR for cotton plant main stalk and node detection[J]. Computers and Electronics in Agriculture,2021,187:106276.
[42]雷亚平,韩迎春,杨北方,等. 利用无人机数字图像监测不同棉花品种叶面积指数[J]. 中国棉花,2018,45(12):9-15.
[43]田明璐,班松涛,常庆瑞,等. 基于低空无人机成像光谱仪影像估算棉花叶面积指数[J]. 农业工程学报,2016,32(21):102-108.
[44]马怡茹,吕新,易翔,等. 基于机器学习的棉花叶面积指数监测[J]. 农业工程学报,2021,37(13):152-162.
[45]Xu W C,Yang W G,Chen S D,et al. Establishing a model to predict the single boll weight of cotton in northern Xinjiang by using high resolution UAV remote sensing data[J]. Computers and Electronics in Agriculture,2020,179:105762.
[46]Dube N,Bryant B,Sari-Sarraf H,et al. In situ cotton leaf area index by height using three-dimensional point clouds[J]. Agronomy Journal,2019,111(6):2999-3007.
[47]Xavier T W F,Souto R N V,Statella T,et al. Identification of Ramularia leaf blight cotton disease infection levels by multispectral,multiscale UAV imagery[J]. Drones,2019,3(2):33.
[48]Ashapure A,Jung J,Chang A J,et al. A comparative study of RGB and multispectral sensor-based cotton canopy cover modelling using multi-temporal UAS data[J]. Remote Sensing,2019,11(23):2757.
[49]Li Y N,Cao Z G,Lu H,et al. In-field cotton detection via region-based semantic image segmentation[J]. Computers and Electronics in Agriculture,2016,127:475-486.
[50]Jiang Y,Li C Y,Xu R,et al. DeepFlower:a deep learning-based approach to characterize flowering patterns of cotton plants in the field[J]. Plant Methods,2020,16(1):156.
[51]Feng A J,Zhang M N,Sudduth K A,et al. Cotton yield estimation from UAV-based plant height[J]. Transactions of the ASABE,2019,62(2):393-404.
[52]王刚,王静,陈兵,等. 基于不同配置棉花化学控顶的光谱特征和光合特征响应研究[J]. 西北农业学报,2021,30(1):83-92.
[53]刘焕军,康苒,Ustin S,等. 基于时间序列高光谱遥感影像的田块尺度作物产量预测[J]. 光谱学与光谱分析,2016,36(8):2585-2589.
[54]Feng A J,Zhou J F,Vories E D,et al. Yield estimation in cotton using UAV-based multi-sensor imagery[J]. Biosystems Engineering,2020,193:101-114.
[55]Mustafic A,Roberts E E,Toews M D,et al. LED-Induced fluorescence and image analysis to detect stink bug damage in cotton bolls[J]. Journal of Biological Engineering,2013,7(1):5.
[56]杨贵军,李长春,于海洋,等. 农用无人机多传感器遥感辅助小麦育种信息获取[J]. 农业工程学报,2015,31(21):184-190.
[57]Fue K,Porter W,Barnes E,et al. Evaluation of a stereo vision system for cotton row detection and boll location estimation in direct sunlight[J]. Agronomy,2020,10(8):1137.
[58]王康丽. 基于无人机可见光和热红外图像的棉花冠层信息识别[D]. 北京:中国农业科学院,2019:5-37.
[59]Chen P F,Wang F Y. New textural indicators for assessing above-ground cotton biomass extracted from optical imagery obtained via unmanned aerial vehicle[J]. Remote Sensing,2020,12(24):4170.
[60]Yang W G,Xu W C,Wu C S,et al. Cotton hail disaster classification based on drone multispectral images at the flowering and boll stage[J]. Computers and Electronics in Agriculture,2021,180:105866.
[61]郭晓飞. 基于光合特征参数的棉花水分状况高光谱遥感监测研究[D]. 石河子:石河子大学,2017:14-41.
[62]Feng A J,Zhou J F,Vories E,et al. Evaluation of cotton emergence using UAV-based narrow-band spectral imagery with customized image alignment and stitching algorithms[J]. Remote Sensing,2020,12(11):1764.
[63]Sun L,Zhu Z S. Using spectral vegetation index to estimate continuous cotton and rice-cotton rotation effects on cotton yield[C]//2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics).Istanbul,Turkey:IEEE,2019:1-4.
[64]Paulus S. Measuring crops in 3D:using geometry for plant phenotyping[J]. Plant Methods,2019,15(1):103.
[65]Dube N,Bryant B,Sari-Sarraf H,et al. Cotton boll distribution and yield estimation using three-dimensional point cloud data[J]. Agronomy Journal,2020,112(6):4976-4989.
[66]Wang T Y,Thomasson J A,Isakeit T,et al. A plant-by-plant method to identify and treat cotton root rot based on UAV remote sensing[J]. Remote Sensing,2020,12(15):2453.
[67]李凯,张建华,冯全,等. 复杂背景与天气条件下的棉花叶片图像分割方法[J]. 中国农业大学学报,2018,23(2):88-98.
[68]Mao H H,Meng J H,Ji F J,et al. Comparison of machine learning regression algorithms for cotton leaf area index retrieval using sentinel-2 spectral bands[J]. Applied Sciences,2019,9(7):1459.
[69]Zhu S S,Zhou L,Gao P,et al. Near-infrared hyperspectral imaging combined with deep learning to identify cotton seed varieties[J]. Molecules,2019,24(18):3268.
[70]Chen P F.Cotton leaf area index estimation using unmanned aerial vehicle multi-spectral images[C]//2019 IEEE International Geoscience and Remote Sensing Symposium.Yokohama,Japan,2019:6251-6254.
[71]Erpelding J E. Genetic characterization of the brown lint phenotype for desi cotton (Gossypium arboreum) accession PI 408765 (cv.‘Sanguineum-1’)[J]. Plant Breeding,2021,140(6):1115-1122.
[72]Spiliotopoulos M,Loukas A. Hybrid methodology for the estimation of crop coefficients based on satellite imagery and ground-based measurements[J]. Water,2019,11(7):1364.
[73]戴建国,张国顺,郭鹏,等. 基于无人机遥感多光谱影像的棉花倒伏信息提取[J]. 农业工程学报,2019,35(2):63-70.
[74]肖爽,刘连涛,张永江,等. 植物微根系原位观测方法研究进展[J]. 植物营养与肥料学报,2020,26(2):370-385.
[75]陈鹏飞,梁飞. 基于低空无人机影像光谱和纹理特征的棉花氮素营养诊断研究[J]. 中国农业科学,2019,52(13):2220-2229.
[76]冯璐,邢芳芳,杨北方,等. 基于红外热成像的棉花叶片温度分布量化方法研究[J]. 棉花学报,2020,32(6):569-576.
[77]Meng L H,Liu H J,Zhang X L,et al. Assessment of the effectiveness of spatiotemporal fusion of multi-source satellite images for cotton yield estimation[J]. Computers and Electronics in Agriculture,2019,162:44-52.
[78]He L M,Mostovoy G. Cotton yield estimate using sentinel-2 data and an ecosystem model over the southern US[J]. Remote Sensing,2019,11(17):2000.
[79]Aires P,Gambarra-Neto F F,Coutinho W M,et al. Near infrared hyperspectral images and pattern recognition techniques used to identify etiological agents of cottonanthracnose and ramulosis[J]. Journal of Spectral Imaging,2018,7(1):a8.
[80]朱乾浩,丹尼·卢埃林,印·威尔逊.高通量测序技术在多倍体作物基因组学研究中的应用[J]. 浙江大学学报(农业与生命科学版),2014,40(4):355-369.
[81]李伟. 高通量作物表型检测关键技术研究与应用[D]. 合肥:中国科学技术大学,2017:1-6.
[82]Li H M,Liu S D,Ge C W,et al. Analysis of drought tolerance and associated traits in upland cotton at the seedling stage[J]. International Journal of Molecular Sciences,2019,20(16):3888.
[83]宿俊吉. 陆地棉早熟与产量纤维品质性状的全基因组关联分析及候选基因筛选[D]. 杨凌:西北农林科技大学,2017:25-67.
[84]王晓阳. 亚洲棉短绒的遗传研究和候选基因鉴定[D]. 武汉:华中农业大学,2020:38-67.
[85]Li B Q,Chen L,Sun W N,et al. Phenomics-based GWAS analysis reveals the genetic architecture for drought resistance in cotton[J]. Plant Biotechnology Journal,2020,18(12):2533-2544.
[86]马建江. 棉花纤维长度、油份和株高性状QTL定位及候选基因鉴定[D]. 杨凌:西北农林科技大学,2019:79-90.
[87]Li Y,Chen H,Li S T,et al. GhWRKY46 from upland cotton positively regulates the drought and salt stress responses in plant[J]. Environmental and Experimental Botany,2021,186:104438.
[88]应继锋,刘定富,赵健. 第5代(5G)作物育种技术体系[J]. 中国种业,2020(10):1-3.

相似文献/References:

[1]沙向红,严建萍.低温胁迫对幼苗期棉花根系ADHa与BADH表达的影响[J].江苏农业科学,2013,41(08):37.
 Sha Xianghong,et al.Effect of low temperature stress on expression of ADHa and BADH gene in root of cotton seedlings[J].Jiangsu Agricultural Sciences,2013,41(11):37.
[2]池文泽,周斌,盛玮,等.保水剂在棉花生产上的应用[J].江苏农业科学,2013,41(05):73.
 Chi Wenze,et al.Study on application of water retention agent in cotton production[J].Jiangsu Agricultural Sciences,2013,41(11):73.
[3]杨富强,杨长琴,刘瑞显,等.不同生育期渍水对棉花恢复生长及产量的影响[J].江苏农业科学,2014,42(12):108.
 Yang Fuqiang,et al.Effects of waterlogging in different growth stages on recovery growth and yield of cotton[J].Jiangsu Agricultural Sciences,2014,42(11):108.
[4]李玉侠,李家运,李长敏,等.江苏丰县棉花生产变化及植棉技术优化[J].江苏农业科学,2014,42(12):111.
 Li Yuxia,et al.Changes of cotton production and optimization of cotton planting technology in Fengxian County,Jiangsu Province[J].Jiangsu Agricultural Sciences,2014,42(11):111.
[5]马晓梅,孙杰,李保成,等.新陆早51号棉花器官同伸关系及棉铃空间分布[J].江苏农业科学,2014,42(11):123.
 Ma Xiaomei,et al().Together growth of organs and cotton bolls spatial distribution of cotton cultivar “Xinluzao No.51”[J].Jiangsu Agricultural Sciences,2014,42(11):123.
[6]丁锦平.棉花病毒诱导基因沉默体系构建[J].江苏农业科学,2013,41(06):38.
 Ding Jinping.Establishment of virus induced gene silencing system in cotton[J].Jiangsu Agricultural Sciences,2013,41(11):38.
[7]王义霞,刘春生,苏彦华.新疆棉花品种新陆中51号在山东棉区的需钾特性[J].江苏农业科学,2014,42(10):80.
 Wang Yixia,et al.Potassium requirement characteristics of Xinjiang cotton cultivar “Xinluzhong No.51” in Shandong cotton region[J].Jiangsu Agricultural Sciences,2014,42(11):80.
[8]刘瑞显,张国伟,杨长琴,等.转变现代棉花生产技术研究理念的新视角——基于复杂性科学的思考[J].江苏农业科学,2014,42(10):1.
 Liu Ruixian,et al.A new perspective on ideological changes of cotton productive technological innovation-Based on reflections on complexity science[J].Jiangsu Agricultural Sciences,2014,42(11):1.
[9]陈向阳,陈丽萍,王思乐,等.粗糙集在棉花异性纤维图像去噪中的应用[J].江苏农业科学,2016,44(03):446.
 Chen Xiangyang,et al.Application of rough set in denoising of cotton fiber image[J].Jiangsu Agricultural Sciences,2016,44(11):446.
[10]姚琛,华春,周峰,等.盐碱滩涂植物资源筛选与利用[J].江苏农业科学,2013,41(10):357.
 Yao Chen,et al.Screening and utilization of plant resources cultivated on saline tidal flats[J].Jiangsu Agricultural Sciences,2013,41(11):357.

备注/Memo

备注/Memo:
收稿日期:2022-08-30
基金项目:北京市农林科学院协同创新中心建设专项(编号:KJCX201917)。
作者简介:李远鲲(1998—),女,辽宁朝阳人,硕士研究生,主要从事棉花表型研究。E-mail:crispyl@qq.com。
通信作者:张永江,博士,教授,主要从事棉花栽培生理与信息技术研究,E-mail:yongjiangzh@sina.com;吴升,博士,高级农艺师,主要从事数字植物应用技术研究,E-mail:wus@nercita.org.cn。
更新日期/Last Update: 2023-06-05