欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2019, Vol. 45 ›› Issue (1): 81-90.doi: 10.3724/SP.J.1006.2019.84058

• 耕作栽培·生理生化 • 上一篇    下一篇

基于随机森林法的棉花叶片叶绿素含量估算

依尔夏提•阿不来提1,2,买买提•沙吾提1,2,3,*,白灯莎•买买提艾力4,*(),安申群1,2,马春玥1,2   

  1. 1 新疆大学资源与环境科学学院, 新疆乌鲁木齐 830046
    2 新疆绿洲生态教育部重点实验室, 新疆乌鲁木齐 830046
    3 新疆智慧城市与环境建模普通高校重点实验室, 新疆乌鲁木齐 830046
    4 新疆农业科学院核技术生物技术研究所, 新疆乌鲁木齐 830046
  • 收稿日期:2018-04-22 接受日期:2018-08-20 出版日期:2018-09-20 网络出版日期:2018-09-20
  • 通讯作者: 买买提?沙吾提,白灯莎?买买提艾力
  • 基金资助:
    本研究由国家自然科学基金项目(41361016);本研究由国家自然科学基金项目(41461051);大学生创新训练计划项目资助(201710755058)

Estimation of leaf chlorophyll content in cotton based on the random forest approach

ABLET Ershat1,2,SAWUT Mamat1,2,3,*,MAIMAITIAILI Baidengsha4,*(),Shen-Qun AN1,2,Chun-Yue MA1,2   

  1. 1 College of Resources and Environmental Science, Xinjiang University, Urumqi 830064, Xinjiang, China
    2 Key Laboratory of Oasis Ecology of Ministry of Education, Urumqi 830064, Xinjiang, China
    3 Key Laboratory for Wisdom City and Environmental Modeling, Xinjiang University, Urumqi 830064, Xinjiang, China
    4 Institute of Nuclear and Biotechnologies, Xinjiang Academy of Agricultural Sciences, Urumqi 830064, Xinjiang, China
  • Received:2018-04-22 Accepted:2018-08-20 Published:2018-09-20 Published online:2018-09-20
  • Contact: SAWUT Mamat,MAIMAITIAILI Baidengsha
  • Supported by:
    This study was supported by the National Natural Science Foundation of China(41361016);This study was supported by the National Natural Science Foundation of China(41461051);the Student Innovation Training Program(201710755058)

RichHTML

12

PDF

456

摘要：

为了高效和无损地估算棉花叶片的叶绿素含量, 本研究测定了棉花光谱反射率及叶绿素含量(soil and plant analyzer development, SPAD)值, 对光谱数据进行包络线去除处理、立方根转换和倒数转换, 以SPAD值与反射光谱之间的相关性为基础, 通过随机森林法筛选出对棉花叶片SPAD值影响较大的特征波段, 构建估算棉花叶片SPAD值的BP神经网络(back propagation artificial neural networks, BP ANN)、偏最小二乘回归(partial least squares regression, PLSR)两个模型。结果表明, 在605~690 nm范围内的反射率与SPAD值相关性达0.01显著水平, 均呈负相关, 相关系数最高值为-0.619。与原始光谱相比, 经过变换后的棉花反射率与SPAD值相关性结果相差较大, 其中去除包络线光谱在550~750 nm波段范围有效提高了相关性, 相关性效果优于倒数转换数据和立方根转换数据。随机森林法能够有效评出对SPAD值影响较大的特征波段, 进而提高模型估算精度。在两种模型中, 基于去除包络线光谱建立的PLSR和BP神经网络模型的决定系数R 2分别为0.92、0.83, 说明这两种模型的估算能力较好; 两种模型RMSE分别为0.88、1.26, RE分别为1.30%、1.89%, 表明PLSR模型的估算精度比BP神经网络模型高。从模型的验证效果来看, PLSR模型在估算棉花SPAD值方面有一定的优势和参考价值。

关键词: SPAD值, 棉花, 随机森林法, 高光谱估算模型

Abstract:

The main objective of this study is the estimation of the leaf chlorophyll content efficiently and harmlessly. SPAD values and spectral data were collected from field observation. Original spectra processed to continuum-removal transformation, cube-root transformation and reciprocal transformation. Based on the correlation between SPAD values and canopy spectral reflectance, we selected characteristic bands by random forest approach to establish two kinds of estimating models, including back propagation artificial neural network (BP ANN) model and partial least squares regression (PLSR) model. The reflectivity in the range of 605-690 nm was negatively correlated with the SPAD value at P < 0.01, with the correlation coefficient of -0.619. After transformations, the spectral reflectance exhibited different correlations with SPAD value, continuum-removal spectra improved the correlation in the range of 550-750 nm, and had a better correlation with SPAD value than cube-root and reciprocal transformations. Random forest approach effectively evaluated the characteristic bands with large influence on SPAD value, which can help improve the estimation accuracy of the model. R 2 of the PLSR and BP neural network model based on continuum-removal spectra was 0.92 and 0.83 respectively, show the two models with good stability in estimation of cotton SPAD values. The RMSE of the two models was 0.88, 1.26, and RE was 1.30% and 1.89% respectively, which indicates that estimation accuracy of PLSR model is higher that of BP neural network model. From the validation of the model, PLSR model has certain advantages and reference value in estimating chlorophyll content of cotton.

Key words: SPAD value, cotton, random forest method, hyper-spectral estimation model

图1

研究区位置和采样点分布图"

图2

棉花叶片光谱"

图3

不同转换光谱曲线与叶SPAD值的相关性 R: 相关系数; RR: 原始光谱; Rcr: 包络线光谱; ?R: 立方根光谱; 1/R: 倒数光谱。"

图4

光谱自相关矩阵"

表1

特征波段的选取"

原始光谱RR 包络线去除光谱Rcr
变量名Variable
name		 特征波段Characteristic
band (nm)		 相关系数Correlation
coefficient		 VIM值
VIM value		 变量名Variable
name		 特征波段Characteristic
band (nm)		 相关系数Correlation
coefficient		 VIM值
VIM value
X1 614 -0.498 0.03420 X'1 407 -0.670 0.00729
X2 616 -0.520 0.00652 X'2 479 -0.681 0.00326
X3 653 -0.603 0.01130 X'3 488 -0.694 0.00326
X4 670 -0.633 0.01031 X'4 508 -0.699 0.00461
X5 689 -0.628 0.00461 X'5 577 -0.732 0.00326
X6 697 -0.544 0.01458 X'6 585 -0.750 0.00326
X7 700 -0.482 0.00652 X'7 612 -0.779 0
X8 759 0.513 0.00565 X'8 621 -0.799 0.00461
X9 786 0.516 0.00461 X'9 651 -0.793 0.00326
X10 826 0.519 0.00799 X'10 695 -0.792 0.00799
X11 905 0.529 0.00799 X'11 712 -0.760 0.00652
X12 941 0.509 0.05238 X'12 723 -0.697 0.00790

图5

变量重要性评估"

表2

建模结果比较"

模型
Model		 建模Calibration (RR) 建模集Calibration (Rcr) 验证集Validation (RR) 验证集Validation (Rcr)
R2 RMSE R2 RMSE R2 RMSE RE (%) R2 RMSE RE (%)
偏最小二乘模型PLSR
BP神经网络模型BP ANN		 0.68
0.69		 1.62
1.59		 0.63
0.90		 1.73
0.91		 0.64
0.78		 2.06
1.60		 3.01
2.27		 0.92
0.83		 0.88
1.26		 1.30
1.89

图6

PLSR和BP神经网络模型对实测值与预测值的拟合分析结果"

[1] 史典义, 刘忠香, 金危危 . 植物叶绿素合成、分解代谢及信号调控. 遗传, 2009,31:698-704.
doi: 10.3724/SP.J.1005.2009.00698
Shi D Y, Liu Z X, Jin W W . Biosynthesis, catabolism and related signal regulations of plant chlorophyll. Hereditas, 2009,31:698-704 (in Chinese with English abstract).
doi: 10.3724/SP.J.1005.2009.00698
[2] 刘燕婕, 李建设, 高艳明 . 可见光波段不同氮处理生菜叶片光谱反射率与叶片全氮、叶绿素的相关性研究. 北方园艺, 2015,39(22):12-16.
doi: 10.11937/bfyy.201522003
Liu Y J, Li J S, Gao Y M . Correlation between lettuce leaf spectral reflectance in visible light area and leaf nitrogen content and leaf chlorophyll content under different levels of nitrogen. Nor Hortic, 2015,39(22):12-16 (in Chinese with English abstract).
doi: 10.11937/bfyy.201522003
[3] 姜海玲, 杨杭, 陈小平, 王树东, 李雪轲, 刘凯 . 利用光谱指数反演植被叶绿素含量的精度及稳定性研究. 光谱学与光谱分析, 2015,35:975-981.
doi: 10.3964/j.issn.1000-0593(2015)04-0975-07
Jiang H L, Yang H, Chen X P, Wang S D, Li X K, Liu K . Research on accuracy and stability of inversing vegetation chlorophyll content by spectral index method. Spectrosc Spect Anal, 2015,35:975-981 (in Chinese with English abstract).
doi: 10.3964/j.issn.1000-0593(2015)04-0975-07
[4] Inoue Y, Guérif M, Baret F, Skidmore A, Gitelson A, Schlerf M . Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyper-spectral data for different types of vegetation. Plant Cell Environ, 2016,39:2609-2623.
doi: 10.1111/pce.12815 pmid: 27650474
[5] Vane G, Goetz A . Terrestrial imaging spectrometry: Current status, future trends. Remote Sense Environ, 1993,44:117-126.
doi: 10.1016/0034-4257(93)90011-L
[6] Curran P J . Remote sensing of foliar chemistry. Remote Sense Environ, 1989,30:271-278.
doi: 10.1016/0034-4257(89)90069-2
[7] Jacquemoud S, Baret F . PROSPECT: a model of leaf optical properties spectra. Remote Sense Environ, 1990,34:75-91.
doi: 10.1016/0034-4257(90)90100-Z
[8] Li D, Cheng T, Zhou K, Zheng H, Yao X, Tian Y . WREP: a wavelet-based technique for extracting the red edge position from reflectance spectra for estimating leaf and canopy chlorophyll contents of cereal crops. ISPRS J Photogramm Remote Sense, 2017: 103-117.
doi: 10.1016/j.isprsjprs.2017.04.024
[9] 毛博慧, 李民赞, 孙红, 刘豪杰, 张俊逸, Zhang Q . 冬小麦苗期叶绿素含量检测光谱学参数寻优. 农业工程学报, 2017,33(S1):164-169.
doi: 10.11975/j.issn.1002-6819.2017.z1.025
Mao B H, Li M Z, Sun H, Liu H J, Zhang J Y, Zhang Q . Optimization of spectroscopy parameters and prediction of chlorophyll content at seeding stage of winter wheat. Trans CSAE, 2017,33(S1):164-169 (in Chinese with English abstract).
doi: 10.11975/j.issn.1002-6819.2017.z1.025
[10] 丁永军, 张晶晶, 孙红, 李修华 . 玻璃温室环境下番茄叶绿素含量敏感光谱波段提取及估测模型. 光谱学与光谱分析, 2017,37:194-199.
Ding Y J, Zhang J J, Sun H, Li X H . Sensitive bands extraction and prediction model of tomato chlorophyll in glass green house. Spectrosc Spect Anal, 2017,37:194-199 (in Chinese with English abstract).
[11] 姚霞, 田永超, 刘小军, 曹卫星, 朱艳 . 不同算法红边位置监测小麦冠层氮素营养指标的比较. 中国农业科学, 2010,43:2661-2667.
doi: 10.3864/j.issn.0578-1752.2010.13.005
Yao X, Tian Y C, Liu X J, Cao W X, Zhu Y . Comparative study on monitoring canopy leaf nitrogen status on red edge position with different algorithms in wheat. Sci Agric Sin, 2010,43:2661-2667 (in Chinese with English abstract).
doi: 10.3864/j.issn.0578-1752.2010.13.005
[12] Yi Q X, Huang J F, Wang F M, Wang X Z . Evaluating the performance of PC-ANN for the estimation of rice nitrogen concentration from canopy hyperspectral reflectance. Int J Remote Sense, 2010,31:931-940.
doi: 10.1080/01431160902912061
[13] Curran P J, Dungan J L, Peterson D L . Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: testing the Kokaly and Clark methodologies. Remote Sense Environ, 2001,76:349-359.
doi: 10.1016/S0034-4257(01)00182-1
[14] 郭超凡, 郭逍宇 . 基于可见光波段包络线去除的湿地植物叶片叶绿素估算. 生态学报, 2016,36:6538-6546.
doi: 10.5846/stxb201507091460
Guo C F, Guo X Y . Estimation of wetland plant leaf chlorophyll content based on continuum removal on visible domain. Acta Ecol Sin, 2016,36:6538-6546 (in Chinese with English abstract).
doi: 10.5846/stxb201507091460
[15] Mielke C, Boesche N K, Rogass C, Kaufmann H, Gauert C . New geometric hull continuum removal algorithm for automatic absorption band detection from spectroscopic data. Remote Sense Lett, 2015,6:97-105.
doi: 10.1080/2150704X.2015.1007246
[16] Breiman L . Random forests. Mach Learn, 2001,45:5-32.
doi: 10.1023/A:1010933404324
[17] 李振国, 杨德森 . 生活质量与临床医学. 中国社会医学, 1994, ( 3):34-35.
Li Z G, Yang D S . Quality of life and clinical medicine. Chin J Soc Med, 1994, ( 3):34-35 (in Chinese).
[18] Donnelly S, Walsh D . Quality of life assessment in advanced cancer. Palliat Med, 2000,2:338-342.
doi: 10.1007/s11912-000-0027-7 pmid: 8931062
[19] Grömping U . Variable importance assessment in regression: linear regression versus random forest. Am Stat, 2009,63:308-319.
doi: 10.1198/tast.2009.08199
[20] 梁智, 孙国强, 卫志农, 臧海祥 . 基于变量选择与高斯过程回归的短期负荷预测. 电力建设, 2017,38(2):122-128.
doi: 10.3969/j.issn.1000-7229.2017.01.017
Liang Z, Sun G Q, Wei Z N, Zang H X , Short-term load forecasting based on variable selection and gaussian process regression. Electric Power Const, 2017,38(2):122-128 (in Chinese with English abstract).
doi: 10.3969/j.issn.1000-7229.2017.01.017
[21] 尼加提·卡斯木, 师庆东, 王敬哲, 茹克亚·萨吾提, 依力亚斯江·努尔麦麦提, 古丽努尔·依沙克 . 基于高光谱特征和偏最小二乘法的春小麦叶绿素含量估算. 农业工程学报, 2017,33(22):208-216.
doi: 10.11975/j.issn.1002-6819.2017.22.027
Nijat K, Shi Q D, Wang J Z, Rukeya S, Ilyas N, Gulnur I . Estimation of spring wheat chlorophyll content based on hyper-spectral features and PLSR model. Trans CSAE, 2017,33(22):208-216 (in Chinese with English abstract).
doi: 10.11975/j.issn.1002-6819.2017.22.027
[22] 翁永玲, 戚浩平, 方洪宾, 赵福岳, 路云阁 . 基于PLSR方法的青海茶卡-共和盆地土壤盐分高光谱遥感反演. 土壤学报, 2010,47:1255-1263.
doi: 10.11766/trxb200907100306
Weng Y L, Qi H P, Fang H B, Zhao F Y, Lu Y G . PLSR-Based hyper-spectral remote sensing retrieval of soil salinity of Chaka-gonghe basin in Qinghai province. Acta Pedol Sin, 2010,47:1255-1263(in Chinese with English abstract).
doi: 10.11766/trxb200907100306
[23] 刘全明, 成秋明, 王学, 李相君 . 河套灌区土壤盐渍化微波雷达反演. 农业工程学报, 2016,32(16):109-114.
doi: 10.11975/j.issn.1002-6819.2016.16.016
Liu Q M, Cheng Q M, Wang X, Li X J . Soil salinity inversion in Hetao Irrigation district using microwave radar. Trans CSAE, 2016,32(16):109-114 (in Chinese with English abstract).
doi: 10.11975/j.issn.1002-6819.2016.16.016
[24] 王静, 刘湘南, 黄方, 唐吉龙, 赵冷冰 . 基于ANN技术和高光谱遥感的盐渍土盐分预测. 农业工程学报, 2009,25(12):161-166.
doi: 10.3969/j.issn.1002-6819.2009.12.029
Wang J, Liu X N, Huang F, Tang J L, Zhao L B . Salinity forecasting of saline soil based on ANN and hyper-spectral remote sensing. Trans CSAE, 2009,25(12):161-166 (in Chinese with English abstract).
doi: 10.3969/j.issn.1002-6819.2009.12.029
[25] Johnson L F, Hlavka C A, Peterson D L . Multivariate analysis of AVIRIS data for canopy biochemical estimation along the oregon transect. Remote Sense Environ, 1994,47:216-230.
doi: 10.1016/0034-4257(94)90157-0
[26] Matson P, Johnson L, Billow C, Miller J, Pu R . Seasonal patterns and remote spectral estimation of canopy chemistry across the oregon transect. Ecol Appl, 1994,4:280-298.
doi: 10.2307/1941934
[27] Curran P J, Kupiec J A, Smith G M . Remote sensing the biochemical composition of a slash pine canopy. IEEE Trans Geosci Remote Sense, 1997,35:415-420.
doi: 10.1109/36.563280
[28] Peterson D L, Aber J D, Matson P A, Card D H, Swanberg N, Wessman C . Remote sensing of forest canopy and leaf biochemical contents. Remote Sense Environ, 1988,24:85-108.
doi: 10.1016/0034-4257(88)90007-7
[29] Yoder B J, Pettigrew-Crosby R E . Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400-2500 nm) at leaf and canopy scales. Remote Sense Environ, 1995,53:199-211.
doi: 10.1016/0034-4257(95)00135-N
[30] Chen L, Huang J F, Wang F M . Comparison between back propagation neural network and regression models for the estimation of pigment content in rice leaves and panicles using hyper-spectral data. Int J Remote Sense, 2007,28:3457-3478.
doi: 10.1080/01431160601024242
[31] 刘平, 马美湖 . 基于高光谱技术检测全蛋粉掺假的研究. 光谱学与光谱分析, 2018,38:246-252.
doi: 10.3964/j.issn.1000-0593(2018)01-0246-07
Liu P, Ma M F . Application of hyper-spectral technology for detecting adulterated whole egg powder. Spectrosc Spect Anal, 2018,38:246-252 (in Chinese with English abstract).
doi: 10.3964/j.issn.1000-0593(2018)01-0246-07
[32] 贾学勤, 冯美臣, 杨武德, 王超, 肖璐洁, 孙慧, 武改红, 张松 . 基于多植被指数组合的冬小麦地上干生物量高光谱估测. 生态学杂志, 2018,37:424-429.
Jia X Q, Feng M C, Yang W D, Wang C, Xiao L J, Sun H, Wu G H, Zhang S . Hyper-spectral estimation of aboveground dry biomass of winter wheat based on the combination of vegetation indices. Chin J Ecol, 2018,37:424-429 (in Chinese with English abstract).
[33] 孙红, 郑涛, 刘宁, 程萌, 李民赞, Zhang Q . 高光谱图像检测马铃薯植株叶绿素含量垂直分布. 农业工程学报, 2018,34(1):149-156.
Sun H, Zheng T, Liu N, Cheng M, Li M Z, Zhang Q . Vertical distribution of chlorophyll in potato plants based on hyper-spectral imaging. Trans CSAE, 2018,34(1):149-156 (in Chinese with English abstract).
[34] 郭云开, 刘宁, 刘磊, 李丹娜, 朱善宽 . 土壤Cu含量高光谱反演的BP神经网络模型. 测绘科学, 2018,43(1):135-139.
Guo Y K, Liu N, Liu L, Li D N, Zhu S K . Hyper-spectral inversion of soil Cu content based on BP neural network model. Sci Survey Map, 2018,43(1):135-139 (in Chinese with English abstract).
[35] 余蛟洋, 常庆瑞, 由明明, 张卓然, 罗丹 . 基于高光谱和BP神经网络模型苹果叶片SPAD值遥感估算. 西北林学院学报, 2018,33(2):156-165.
Yu J Y, Chang Q R, You M M, Zhang Z R, Luo D . Estimation of apple leaf SPAD value based on hyperspectrum and BP Neural Network. J Northwest For Univ, 2018,33(2):156-165 (in Chinese with English abstract).
[36] Zagolski F, Pinel V, Romier J, Alcayde D, Fontanari J, Gastellu-Etchegorry J P . Forest canopy chemistry with high spectral resolution remote sensing. Int J Remote Sense, 1996,17:1107-1128.
doi: 10.1080/01431169608949073
[37] Pal M . Random forest classifier for remote sensing classification. Int J Remote Sense, 2005,26:217-222.
doi: 10.1080/01431160412331269698
[38] Deschamps B, Mcnairn H, Shang J, Jiao X . Towards operational radar-only crop type classification: comparison of a traditional decision tree with a random forest classifier. Can J Remote Sense, 2012,38:60-68.
doi: 10.5589/m12-012
[39] 黄健熙, 侯矞焯, 苏伟, 刘峻明, 朱德海 . 基于GF-1 WFV数据的玉米与大豆种植面积提取方法. 农业工程学报, 2017,33(7):164-170.
doi: 10.11975/j.issn.1002-6819.2017.07.021
Huang J X, Hou Y Z, Su W, Liu J M, Zhu D H . Mapping corn and soybean cropped area with GF-1 WFV data. Trans CSAE, 2017,33(7):164-170 (in Chinese with English abstract).
doi: 10.11975/j.issn.1002-6819.2017.07.021
[40] 陈纪波, 胡慧, 陈克垚, 王桂芝 . 基于非线性PLSR模型的气候变化对粮食产量的影响分析. 中国农业气象, 2016,37:674-681.
doi: 10.3969/j.issn.1000-6362.2016.06.007
Chen J B, Hu H, Chen K Y, Wang G Z . Effects of climate change on the grain yield based on nonlinear PLSR model. Chin J Agrometeorol, 2016,37:674-681 (in Chinese with English abstract).
doi: 10.3969/j.issn.1000-6362.2016.06.007
[41] 于雷, 洪永胜, 耿雷, 周勇, 朱强, 曹隽隽, 聂艳 . 基于偏最小二乘回归的土壤有机质含量高光谱估算, 农业工程学报, 2015,31(14):103-109.
doi: 10.11975/j.issn.1002-6819.2015.14.015
Yu L, Hong Y S, Geng L, Zhou Y, Zhu Q, Cao J J, Nie Y . Hyperspectral estimation of soil organic matter content based on partial least squares regression. Trans CSAE, 2015,31(14):103-109 (in Chinese with English abstract).
doi: 10.11975/j.issn.1002-6819.2015.14.015
[42] Gomez C, Lagacherie P, Coulouma G . Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements. Geoderma, 2008,148:141-148.
doi: 10.1016/j.geoderma.2008.09.016
[43] 刘晓莉, 杨灵娥, 宋春玲 . 提高多目标输出神经网络模型泛化能力和预测精度的方法. 佛山科学技术学院学报(自然科学版), 2008,26(1):31-33.
doi: 10.3969/j.issn.1008-0171.2008.01.009
Liu X L, Yang L E, Song C L . Improvement of the genera and the learn enlcienin BP network models. J Foshan Univ(Nat Sci Edn), 2008,26(1):31-33 (in Chinese with English abstract).
doi: 10.3969/j.issn.1008-0171.2008.01.009
[1] 周静远, 孔祥强, 张艳军, 李雪源, 张冬梅, 董合忠. 基于种子萌发出苗过程中弯钩建成和下胚轴生长的棉花出苗壮苗机制与技术[J]. 作物学报, 2022, 48(5): 1051-1058.
[2] 孙思敏, 韩贝, 陈林, 孙伟男, 张献龙, 杨细燕. 棉花苗期根系分型及根系性状的关联分析[J]. 作物学报, 2022, 48(5): 1081-1090.
[3] 闫晓宇, 郭文君, 秦都林, 王双磊, 聂军军, 赵娜, 祁杰, 宋宪亮, 毛丽丽, 孙学振. 滨海盐碱地棉花秸秆还田和深松对棉花干物质积累、养分吸收及产量的影响[J]. 作物学报, 2022, 48(5): 1235-1247.
[4] 郑曙峰, 刘小玲, 王维, 徐道青, 阚画春, 陈敏, 李淑英. 论两熟制棉花绿色化轻简化机械化栽培[J]. 作物学报, 2022, 48(3): 541-552.
[5] 张艳波, 王袁, 冯甘雨, 段慧蓉, 刘海英. 棉籽油分和3种主要脂肪酸含量QTL分析[J]. 作物学报, 2022, 48(2): 380-395.
[6] 张特, 王蜜蜂, 赵强. 滴施缩节胺与氮肥对棉花生长发育及产量的影响[J]. 作物学报, 2022, 48(2): 396-409.
[7] 赵文青, 徐文正, 杨锍琰, 刘玉, 周治国, 王友华. 棉花叶片响应高温的差异与夜间淀粉降解密切相关[J]. 作物学报, 2021, 47(9): 1680-1689.
[8] 岳丹丹, 韩贝, Abid Ullah, 张献龙, 杨细燕. 干旱条件下棉花根际真菌多样性分析[J]. 作物学报, 2021, 47(9): 1806-1815.
[9] 曾紫君, 曾钰, 闫磊, 程锦, 姜存仓. 低硼及高硼胁迫对棉花幼苗生长与脯氨酸代谢的影响[J]. 作物学报, 2021, 47(8): 1616-1623.
[10] 马欢欢, 方启迪, 丁元昊, 池华斌, 张献龙, 闵玲. 棉花GhMADS7基因正调控棉花花瓣发育[J]. 作物学报, 2021, 47(5): 814-826.
[11] 许乃银, 赵素琴, 张芳, 付小琼, 杨晓妮, 乔银桃, 孙世贤. 基于GYT双标图对西北内陆棉区国审棉花品种的分类评价[J]. 作物学报, 2021, 47(4): 660-671.
[12] 周冠彤, 雷建峰, 代培红, 刘超, 李月, 刘晓东. 棉花CRISPR/Cas9基因编辑有效sgRNA高效筛选体系的研究[J]. 作物学报, 2021, 47(3): 427-437.
[13] 卢合全, 唐薇, 罗振, 孔祥强, 李振怀, 徐士振, 辛承松. 商品有机肥替代部分化肥对连作棉田土壤养分、棉花生长发育及产量的影响[J]. 作物学报, 2021, 47(12): 2511-2521.
[14] 王晔, 刘钊, 肖爽, 李芳军, 吴霞, 王保民, 田晓莉. 转PSAG12-IPT基因对棉花叶片衰老及产量和纤维品质的影响[J]. 作物学报, 2021, 47(11): 2111-2120.
[15] 杨琴莉, 杨多凤, 丁林云, 赵汀, 张军, 梅欢, 黄楚珺, 高阳, 叶莉, 高梦涛, 严孙艺, 张天真, 胡艳. 棉花花器官突变体的鉴定及候选基因的克隆[J]. 作物学报, 2021, 47(10): 1854-1862.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备15008789号-2