欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2025, Vol. 51 ›› Issue (5): 1326-1337.doi: 10.3724/SP.J.1006.2025.44157

• 耕作栽培·生理生化 • 上一篇    下一篇

基于高光谱遥感的油菜叶片氮磷养分含量诊断

王清华(), 朱格格, 方雯, 刘诗诗*(), 鲁剑巍   

  1. 华中农业大学资源与环境学院 / 农业农村部长江中下游耕地保育重点实验室, 湖北武汉 430070
  • 收稿日期:2024-09-16 接受日期:2025-01-23 出版日期:2025-05-12 网络出版日期:2025-02-10
  • 通讯作者: *刘诗诗, E-mail: ssliu@mail.hzau.edu.cn
  • 作者简介:E-mail: wangqinghua@webmail.hzau.edu.cn
  • 基金资助:
    国家自然科学基金项目(42171350);国家重点研发计划项目(2021YFD1600503)

Diagnosis of nitrogen and phosphorus nutrient content in rapeseed leaves based on hyperspectral remote sensing

WANG Qing-Hua(), ZHU Ge-Ge, FANG Wen, LIU Shi-Shi*(), LU Jian-Wei   

  1. College of Resources and Environment, Huazhong Agricultural University / Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture and Rural Affairs, Wuhan 430070, Hubei, China
  • Received:2024-09-16 Accepted:2025-01-23 Published:2025-05-12 Published online:2025-02-10
  • Contact: *E-mail: ssliu@mail.hzau.edu.cn
  • Supported by:
    National Natural Science Foundation of China(42171350);National Key Research and Development Program of China(2021YFD1600503)

RichHTML

2

PDF

54

摘要：

利用高光谱遥感技术准确、无损地诊断油菜氮磷养分亏缺, 能够为精准施肥提供依据。本研究以多点、多年田间试验测定的越冬期冬油菜叶片氮含量(leaf nitrogen concentration, LNC)、叶片磷含量(leaf phosphorus concentration, LPC)、产量和冠层反射光谱为基础, 利用竞争性自适应重加权平均算法、无信息变量消除法、连续投影算法筛选对LNC、LPC敏感的特征波段, 基于筛选的波段利用偏最小二乘回归构建基于原初光谱和一阶微分光谱的LNC、LPC估测模型。根据养分含量估测结果结合研究区的氮营养指数(nitrogen nutrition index, NNI)和磷营养指数(phosphorous nutrition index, PNI)进行油菜养分亏缺诊断。结果表明, 筛选出的油菜越冬期LNC、LPC特征波段主要集中在400~460 nm、650~730 nm、1140~1210 nm、2240~2370 nm和650~730 nm、2100~2310 nm。基于一阶微分光谱和无信息变量消除法的模型其估测精度要优于其他模型, 在测试集上该模型也能准确估测油菜LNC (R2=0.773, RMSE=0.528%)和LPC (R2=0.785, RMSE=0.09%)。同时, 本研究利用田间试验产量数据确定了油菜越冬期NNI和PNI的阈值, 分别为1.20和0.75。基于高光谱遥感估测的LNC和LPC, 进一步计算NNI和PNC, 能够对油菜越冬期的养分亏缺进行诊断, 为油菜生产可持续发展提供新的技术。

关键词: 冬油菜, 高光谱遥感, 偏最小二乘, 波段选择, 叶片氮磷含量

Abstract:

Hyperspectral remote sensing technology provides an accurate and non-destructive method for diagnosing nitrogen (N) and phosphorus (P) deficiencies in rapeseed, laying the groundwork for precision fertilization. This study utilized multi-site, multi-year field trials to collect data on leaf nitrogen concentration (LNC), leaf phosphorus concentration (LPC), yield, and the canopy reflectance spectrum of winter rapeseed during the overwintering period. Feature bands sensitive to LNC and LPC were identified using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and the elimination of non-informative variables (UVE). Partial least squares regression (PLSR) models were constructed to estimate LNC and LPC based on both the original spectrum and the first-order derivative spectrum. Nutrient deficiency diagnosis was achieved by integrating the nitrogen nutrition index (NNI) and phosphorus nutrition index (PNI) derived from the estimated nutrient concentrations. The results revealed that the characteristic bands for LNC and LPC were primarily concentrated in the ranges of 400-460 nm, 650-730 nm, 1140-1210 nm, and 2240-2370 nm for LNC, and 650-730 nm, 2100-2310 nm for LPC. The model based on the first-order derivative spectrum and the UVE method demonstrated superior accuracy compared to other models. In the test set, the model achieved high estimation accuracy for LNC (R2=0.773, RMSE=0.528%) and LPC (R2=0.785, RMSE=0.09%). Threshold values for NNI and PNI during the overwintering period were established using yield data from field trials, which were 1.20 and 0.75, respectively. By employing hyperspectral remote sensing to estimate LNC and LPC, subsequent calculations of NNI and PNI can effectively diagnose nutrient deficiencies in rapeseed during the overwintering period. This approach provides a novel technological solution for the sustainable development of rapeseed production.

Key words: winter rape, hyperspectral remote sensing, partial least squares, band selection, leaf nitrogen and phosphorus concentration

表1

土壤基础理化性质"

地区
Area		 pH 有机质
Organic matter
(g kg-1)		 全氮
Total nitrogen
(g kg-1)		 速效磷
Rapid available phosphorus
(mg kg-1)		 速效钾
Rapidly available potassium
(mg kg-1)
武穴Wuxue 5.49 31.93 1.85 4.00 47.60
沙洋Shayang 5.76 19.09 0.98 5.65 172.00
建始Jianshi 5.35 34.21 2.25 25.17 204.27

图1

研究区试验田分布 该图基于自然资源部标准地图服务网站下载的审图号为GS (2016) 2923号的标准地图制作, 底图边界无修改。"

表2

不同试验施肥处理"

试验
Experiment		 年份
Year		 地点
Site		 施肥处理
Fertilization treatment (kg hm-2)		 小区面积
Area (m2)		 样本总数
Sample number
1 2020-2024 武穴Wuxue N0, N90, N120, N270, N360
P0, P45, P90, P135, P180		 20 120
2 2021-2023 沙洋Shayang N0, N90, N120, N270, N360
P0, P45, P90, P135, P180		 20 60
3 2022-2023 建始Jianshi CK, Y300, Y450, Y600, Y750, Y900, Y1050 20 21

图2

不同施肥用量下的油菜冠层光谱图 处理同表2。"

表3

不同施肥处理下的油菜LNC和LPC"

施肥水平
Fertilizer rate		 LNC (%) 施肥水平
Fertilizer rate		 LPC (%)
最小值
Min.		 最大值
Max.		 平均值
Mean		 标准差
SD		 最小值
Min.		 最大值
Max.		 平均值
Mean		 标准差
SD
N0 1.52 4.24 2.72 0.85 P0 0.06 0.37 0.21 0.12
N90 1.88 4.69 3.04 0.77 P45 0.11 0.42 0.23 0.12
N180 2.65 5.23 3.96 0.78 P90 0.17 0.43 0.32 0.09
N270 3.72 5.52 4.36 0.54 P135 0.26 0.67 0.40 0.11
N360 3.92 7.61 5.77 1.67 P180 0.32 0.85 0.59 0.20

表4

不同专用肥用量下的油菜LNC和LPC"

项目Item CK Y300 Y450 Y600 Y750 Y900 Y1050
叶片氮含量LNC (%) 3.33 3.59 3.66 3.94 4.06 4.39 4.51
叶片磷含量LPC (%) 0.35 0.36 0.37 0.39 0.41 0.42 0.50

图3

3种筛选算法下冬油菜特征光谱分布 LNC: 叶片氮含量; LPC: 叶片磷含量。CARS: 竞争性自适应重加权平均算法; UVE: 无信息变量消除法; SPA: 连续投影算法。R: 原初光谱; FDR: 一阶微分光谱。"

图4

不同筛选算法下的PLSR模型测试集R2和RMSE 缩写同图3。"

图5

油菜LNC和LPC预测值和真实值比较 缩写同图3。"

图6

不同氮肥用量下的油菜产量与氮营养指数(NNI) N0、N60、N120、N180、N240分别表示2023-2024年建始地区氮肥用量为0、60、120、180、240 kg hm-2。图中小写字母表示相同年份不同氮肥用量间产量差异达5%显著水平。"

图7

不同磷肥用量下的油菜产量与磷营养指数(PNI) P0、P45、P90、P135、P180分别表示2023-2024年建始地区磷肥用量为0、45、90、135、180 kg hm-2。图中小写字母表示相同年份不同磷肥用量间产量差异达5%显著水平。"

图8

独立验证数据集油菜LNC、LPC预测值和真实值比较 缩写同图3。"

图9

不同专用肥用量处理下的油菜氮营养指数(NNI)、磷营养指数(PNI) 处理同表2。"

[1] 张冉, 曹娟娟, 濮超, 李育, 金海刚. 中国油菜籽和菜籽油的生产、进出口及供需分析. 中国油脂, 2022, 47(6): 8-14.
Zhang R, Cao J J, Pu C, Li Y, Jin H G. Analysis on production, import, export, supply and demand of rapeseed and rapeseed oil in China. China Oils Fats, 2022, 47(6): 8-14 (in Chinese with English abstract).
[2] 孟孜贞, 杨佳群, 田贵生, 胡兵, 鲁君明, 任涛, 鲁剑巍. 追施氮磷钾肥对菜油两用油菜籽产量的影响. 长江蔬菜, 2024, (20): 17-20.
Meng Z Z, Yang J Q, Tian G S, Hu B, Lu Q M, Ren T, Lu J W. Effects of nitrogen, phosphorus and potassium fertilizer on rapeseed yield of vegetable and oilseed dual-used rape. J Changjiang Veget, 2024, (20): 17-20 (in Chinese).
[3] 武志杰, 张丽莉, 石元亮, 魏占波, 李东坡, 宫平, 李杰, 张蕾, 王玲莉, 武开阔, 等. 绿色肥料缘起、现状与发展趋势. 中国农业科学, 2023, 56: 2530-2546.
doi: 10.3864/j.issn.0578-1752.2023.13.008
Wu Z J, Zhang L L, Shi Y L, Wei Z B, Li D P, Gong P, Li J, Zhang L, Wang L L, Wu K K, et al. Origin, present situation and development trend of green fertilizer. Sci Agric Sin, 2023, 56: 2530-2546 (in Chinese with English abstract).
doi: 10.3864/j.issn.0578-1752.2023.13.008
[4] 鲁剑巍, 任涛, 李小坤, 丛日环, 陆志峰, 张洋洋, 刘诗诗, 廖世鹏, 朱俊. 我国冬油菜养分精准调控策略与高效施肥技术体系. 华中农业大学学报, 2023, 42(6): 18-25.
Lu J W, Ren T, Li X K, Cong R H, Lu Z F, Zhang Y Y, Liu S S, Liao S P, Zhu J. Strategy of precisely controlling nutrient and system of efficient fertilization technology for winter rapeseed in China. J Huazhong Agric Univ, 2023, 42(6): 18-25 (in Chinese with English abstract).
[5] Li L T, Wang S Q, Ren T, Wei Q Q, Ming J, Li J, Li X K, Cong R H, Lu J W. Ability of models with effective wavelengths to monitor nitrogen and phosphorus status of winter oilseed rape leaves using in situ canopy spectroscopy. Field Crops Res, 2018, 215: 173-186.
[6] 蒋雪松, 黄林峰, 贾志成, 戎子凡. 基于光谱遥感技术的作物营养诊断研究进展. 林业工程学报, 2023, 8(6): 13-23.
Jiang X S, Huang L F, Jia Z C, Rong Z F. Research and progress of crop nutrition diagnosis based on spectral remote sensing technology. J For Eng, 2023, 8(6): 13-23 (in Chinese with English abstract).
[7] 刘潜, 王梦迪, 郭龙, 王冉, 贾中甫, 胡献君, 唐乾坤, 石铁柱. 基于机载高光谱影像的农田尺度土壤有机碳密度制图. 遥感学报, 2024, 28: 293-305.
Liu Q, Wang M D, Guo L, Wang R, Jia Z F, Hu X J, Tang Q K, Shi T Z. Mapping of soil organic carbon density at farmland scale based on airborne hyperspectral images. Natl Remote Sens Bull, 2024, 28: 293-305 (in Chinese with English abstract).
[8] 史舟, 梁宗正, 杨媛媛, 郭燕. 农业遥感研究现状与展望. 农业机械学报, 2015, 46(2): 247-260.
Shi Z, Liang Z Z, Yang Y Y, Guo Y. Status and prospect of agricultural remote sensing. Trans CSAM, 2015, 46(2): 247-260 (in Chinese with English abstract).
[9] 张霞, 刘良云, 赵春江, 张兵. 利用高光谱遥感图像估算小麦氮含量. 遥感学报, 2003, 7(3): 176-181.
Zhang X, Liu L Y, Zhao C J, Zhang B. Estimating wheat nitrogen concentration with high spectral resolution image. J Remote Sens, 2003, 7(3): 176-181 (in Chinese with English abstract).
[10] 许童羽, 金忠煜, 郭忠辉, 杨柳, 白驹驰, 冯帅, 于丰华. 基于CARS-RUN-ELM算法的水稻叶片氮磷含量协同反演方法. 农业工程学报, 2022, 38(10): 148-155.
Xu T Y, Jin Z Y, Guo Z H, Yang L, Bai J C, Feng S, Yu F H. Simultaneous inversion method of nitrogen and phosphorus contents in rice leaves using CARS-RUN-ELM algorithm. Trans CSAE, 2022, 38(10): 148-155 (in Chinese with English abstract).
[11] 李媛媛, 常庆瑞, 刘秀英, 严林, 罗丹, 王烁. 基于高光谱和BP神经网络的玉米叶片SPAD值遥感估算. 农业工程学报, 2016, 32(16): 135-142.
Li Y Y, Chang Q R, Liu X Y, Yan L, Luo D, Wang S. Estimation of maize leaf SPAD value based on hyperspectrum and BP neural network. Trans CSAE, 2016, 32(16): 135-142 (in Chinese with English abstract).
[12] 唐雪海, 燕李鹏, 傅根深, 匡帆, 窦敏, 黄庆丰, 欧强新. 基于机器学习的油茶叶片钾含量估算模型构建. 中国油料作物学报, 网络首发[2024-09-18], https://link.cnki.net/urlid/42.1429.S.20240918.1015.002.
Tang X H, Yan L P, Fu G S, Kuang F, Dou M, Huang F Q, Ou Q X. Construction of potassium content estimation model for Camellia oleifera leaves based on machine learning. Chin J Oil Crop Sci, Published online [2024-09-18], https://link.cnki.net/urlid/42.1429.S.20240918.1015.002 (in Chinese with English abstract).
[13] 李岚涛, 盛开, 尹焕丽, 郭娅, 王丹丹, 王宜伦. 考虑植株氮垂直分布的夏玉米营养诊断敏感位点筛选. 农业工程学报, 2020, 36(6): 56-65.
Li L T, Sheng K, Yin H L, Guo Y, Wang D D, Wang Y L. Selecting the sensitive position of maize leaves for nitrogen status diagnosis of summer maize by considering vertical nitrogen distribution in plant. Trans CSAE, 2020, 36(6): 56-65 (in Chinese with English abstract).
[14] 裴浩杰, 冯海宽, 李长春, 金秀良, 李振海, 杨贵军. 基于综合指标的冬小麦长势无人机遥感监测. 农业工程学报, 2017, 33(20): 74-82.
Pei H J, Feng H K, Li C C, Jin X L, Li Z H, Yang G J. Remote sensing monitoring of winter wheat growth with UAV based on comprehensive index. Trans CSAE, 2017, 33(20): 74-82 (in Chinese with English abstract).
[15] Osborne S L, Schepers J S, Francis D D, Schlemmer M R. Detection of phosphorus and nitrogen deficiencies in corn using spectral radiance measurements. Agron J, 2002, 94: 1215-1221.
[16] 叶林蔚, 唐荣年, 李创. 基于AE-FFNN神经网络的橡胶树叶片磷含量定性研究. 中国农业科技导报, 2021, 23(7): 117-124.
Ye L W, Tang R N, Li C. Qualitative study on phosphorus content in rubber leaves based on AE-FFNN neural network. J Agric Sci Technol, 2021, 23(7): 117-124 (in Chinese with English abstract).
[17] 鲍士旦. 土壤农化分析(第3版). 北京: 中国农业出版社, 2000.
Bao S D. Soil and Agricultural Chemistry Analysis, 3rd edn. Beijing: China Agriculture Press, 2000 (in Chinese).
[18] 于雷, 朱亚星, 洪永胜, 夏天, 刘目兴, 周勇. 高光谱技术结合CARS算法预测土壤水分含量. 农业工程学报, 2016, 32(22): 138-145.
Yu L, Zhu Y X, Hong Y S, Xia T, Liu M X, Zhou Y. Determination of soil moisture content by hyperspectral technology with CARS algorithm. Trans CSAE, 2016, 32(22): 138-145 (in Chinese with English abstract).
[19] Centner V, Massart D L, de Noord O E, Jong S D, Vandeginste B M, Sterna C. Elimination of uninformative variables for multivariate calibration. Anal Chem, 1996, 68: 3851-3858.
doi: 10.1021/ac960321m pmid: 21619260
[20] Deng F, Ding Y, Chen Y J, Zhu S N, Chen F F. Quantitative analysis of the content of nitrogen and sulfur in coal based on laser-induced breakdown spectroscopy: effects of variable selection. Plasma Sci Technol, 2020, 22: 074005.
[21] 芮婷婷, 徐云飞, 程琦, 杨斌, 冯志军, 周涛, 张世文. 基于无人机多光谱遥感的冬小麦叶片含水量反演. 麦类作物学报, 2022, 42: 1291-1300.
Rui T T, Xu Y F, Cheng Q, Yang B, Feng Z J, Zhou T, Zhang S W. Water content retrieval of winter wheat leaves based on UAV multi-spectral remote sensing. J Triticeae Crops, 2022, 42: 1291-1300 (in Chinese with English abstract).
[22] Lemaire G, Gastal F, Salette J. Analysis of the effect of N nutrition on dry matter yield of a sward by reference to potential yield and optimum N content. Proceedings of the 16th International Grassland Congress, Nice, France, 1989. pp 179-180.
[23] 彭新新, 刘其, 王江丽, 刁明. 基于临界氮浓度的滴灌春小麦氮素营养诊断. 麦类作物学报, 2015, 35: 1009-1015.
Peng X X, Liu Q, Wang J L, Diao M. Nitrogen nutrition diagnosis based on the critical nitrogen concentration of drip irrigation spring wheat. J Triticeae Crops, 2015, 35: 1009-1015 (in Chinese with English abstract).
[24] 刘秋霞, 任涛, 张亚伟, 廖世鹏, 李小坤, 丛日环, 鲁剑巍. 华中区域直播冬油菜临界氮浓度稀释曲线的建立与应用. 中国农业科学, 2019, 52: 2835-2844.
doi: 10.3864/j.issn.0578-1752.2019.16.009
Liu Q X, Ren T, Zhang Y W, Liao S P, Li X K, Cong R H, Lu J W. Determination and application of a critical nitrogen dilution curve for direct-sowing winter oilseed rape in Central China. Sci Agric Sin, 2019, 52: 2835-2844 (in Chinese with English abstract).
doi: 10.3864/j.issn.0578-1752.2019.16.009
[25] Justes E, Mary B, Meynard J M, Machet J M, Thelier-Huche L. Determination of a critical nitrogen dilution curve for winter wheat crops. Ann Bot, 1994, 74: 397-407.
[26] 丁怡梦, 陈慕琪, 丁文锐, 任清铭, 沈文远, 刘大同, 陆成彬, 熊飞. 谷类作物碳氮代谢互作机制的研究进展. 植物生理学报, 2024, 60: 753-761.
Ding Y M, Chen M Q, Ding W R, Ren Q M, Shen W Y, Liu D T, Lu C B, Xiong F. Research advances on the carbon-nitrogen metabolic interaction mechanisms in cereal crops. Plant Physiol J, 2024, 60: 753-761 (in Chinese with English abstract).
[27] 王浩宇, 魏子渊, 杨永霞, 侯军英, 孙章彤, 胡瑾. 基于高光谱和卷积自编码网络的茄子叶片氮含量估测. 光谱学与光谱分析, 2024, 44: 2208-2215.
Wang H Y, Wei Z Y, Yang Y X, Hou J Y, Sun Z T, Hu J. Estimation of eggplant leaf nitrogen content based on hyperspectral imaging and convolutional auto-encoders networks. Spectrosc Spectr Anal, 2024, 44: 2208-2215 (in Chinese with English abstract).
[28] 车淼, 王海荣, 徐玺, 孙崇. PSO-DF: 基于高光谱的水稻叶片氮含量估测方法. 遥感技术与应用, 2024, 39(2): 280-289.
doi: 10.11873/j.issn.1004-0323.2024.2.0280
Che M, Wang H R, Xu X, Sun C. PSO-DF: a hyperspectral model for estimating nitrogen content in rice leaves. Remote Sens Technol Appl, 2024, 39(2): 280-289 (in Chinese with English abstract).
[29] 晁亚茹, 卢艳丽, 王磊, 白由路, 宋桂霈. 不同玉米品种氮胁迫响应及诊断参数确定. 玉米科学, 2022, 30(6): 59-66.
Chao Y R, Lu Y L, Wang L, Bai Y L, Song G P. Responses of different maize varieties to nitrogen stress and determination of diagnostic parameters. J Maize Sci, 2022, 30(6): 59-66 (in Chinese with English abstract).
[30] 栗方亮, 孔庆波, 张青. 基于高光谱的琯溪蜜柚叶片磷素含量估算模型研究. 中国农业科技导报, 2023, 25(1): 100-108.
doi: 10.13304/j.nykjdb.2021.1002
Li F L, Kong Q B, Zhang Q. Estimation models of phosphorus contents in Guanxi honey pomelo leaves based on hyperspectral data. J Agric Sci Technol, 2023, 25(1): 100-108 (in Chinese with English abstract).
[31] 李岚涛, 汪善勤, 任涛, 马驿, 魏全全, 高雯晗, 鲁剑巍. 基于高光谱的冬油菜叶片磷含量诊断模型. 农业工程学报, 2016, 32(14): 209-218.
Li L T, Wang S Q, Ren T, Ma Y, Wei Q Q, Gao W H, Lu J W. Evaluating models of leaf phosphorus content of winter oilseed rape based on hyperspectral data. Trans CSAE, 2016, 32(14): 209-218 (in Chinese with English abstract).
[32] 杨宝华, 陈建林, 陈林海, 曹卫星, 姚霞, 朱艳. 基于敏感波段的小麦冠层氮含量估测模型. 农业工程学报, 2015, 31(22): 176-182.
Yang B H, Chen J L, Chen L H, Cao W X, Yao X, Zhu Y. Estimation model of wheat canopy nitrogen content based on sensitive bands. Trans CSAE, 2015, 31(22): 176-182 (in Chinese with English abstract).
[33] 程立真, 朱西存, 高璐璐, 王凌, 赵庚星. 基于随机森林模型的苹果叶片磷素含量高光谱估测. 果树学报, 2016, 33: 1219-1229.
Cheng L Z, Zhu X C, Gao L L, Wang L, Zhao G X. Hyperspectral estimation of phosphorus content for apple leaves based on the random forest model. J Fruit Sci, 2016, 33: 1219-1229 (in Chinese with English abstract).
[34] Greenwood D J, Lemaire G, Gosse G, Cruz P, Draycott A, Neeteson J J. Decline in percentage N of C3 and C4 crops with increasing plant mass. Ann Bot, 1990, 66: 425-436.
[35] 曾丽丽, 江俊松. 油菜临界磷浓度稀释曲线模型的构建和验证. 河南农业科学, 2023, 52(4): 51-59.
Zeng L L, Jiang J S. Construction and validation of critical phosphorus concentration dilution curve model in rape. J Henan Agric Sci, 2023, 52(4): 51-59 (in Chinese with English abstract).
[1] 孟孜贞, 刘陈, 盛倩男, 熊志豪, 方娅婷, 赵剑, 余秋华, 王昆昆, 李小坤, 任涛, 鲁剑巍. 氮磷钾肥施用对冬油菜增产效果及因冻害减产程度的影响[J]. 作物学报, 2025, 51(4): 1037-1049.
[2] 谢雄泽, 谢捷, 褚乾梅, 尹羽丰, 余小红, 王盾, 冯鹏. 长江流域冬油菜需水量及水分盈亏特征分析[J]. 作物学报, 2024, 50(7): 1829-1840.
[3] 宋毅, 李静, 谷贺贺, 陆志峰, 廖世鹏, 李小坤, 丛日环, 任涛, 鲁剑巍. 氮肥用量对冬油菜籽粒产量和品质的影响[J]. 作物学报, 2023, 49(7): 2002-2011.
[4] 宁宁, 莫娇, 胡冰, 李大双, 娄洪祥, 王春云, 白晨阳, 蒯婕, 汪波, 王晶, 徐正华, 李晓华, 贾才华, 周广生. 长江流域不同生态区油菜籽关键品质比较研究[J]. 作物学报, 2023, 49(12): 3315-3327.
[5] 马骊, 白静, 赵玉红, 孙柏林, 侯献飞, 方彦, 王旺田, 蒲媛媛, 刘丽君, 徐佳, 陶肖蕾, 孙万仓, 武军艳. 冷胁迫下甘蓝型冬油菜表达蛋白及BnGSTs基因家族的鉴定与分析[J]. 作物学报, 2023, 49(1): 153-166.
[6] 冯子恒, 李晓, 段剑钊, 高飞, 贺利, 杨天聪, 戎亚思, 宋莉, 尹飞, 冯伟. 基于特征波段选择和机器学习的小麦白粉病高光谱遥感监测[J]. 作物学报, 2022, 48(9): 2300-2314.
[7] 李金敏, 陈秀青, 杨琦, 史良胜. 基于高光谱的水稻叶片氮含量估计的深度森林模型研究[J]. 作物学报, 2021, 47(7): 1342-1350.
[8] 易秋香,刘英,常存,钟瑞森. 基于光谱指数和偏最小二乘的棉花类胡萝卜素/叶绿素a比值估算[J]. 作物学报, 2020, 46(8): 1266-1274.
[9] 米文博, 方园, 刘自刚, 徐春梅, 刘高阳, 邹娅, 徐明霞, 郑国强, 曹小东, 方新玲. 白菜型冬油菜温敏不育系PK3-12S育性转换的差异蛋白质组学分析[J]. 作物学报, 2020, 46(10): 1507-1516.
[10] 吴亚鹏,贺利,王洋洋,刘北城,王永华,郭天财,冯伟. 冬小麦生物量及氮积累量的植被指数动态模型研究[J]. 作物学报, 2019, 45(8): 1238-1249.
[11] 李静,闫金垚,胡文诗,李小坤,丛日环,任涛,鲁剑巍. 氮钾配施对油菜产量及氮素利用的影响[J]. 作物学报, 2019, 45(6): 941-948.
[12] 米超,赵艳宁,刘自刚,陈其鲜,孙万仓,方彦,李学才,武军艳. 白菜型冬油菜RuBisCo蛋白亚基基因rbcL和rbcS的克隆及其在干旱胁迫下的表达[J]. 作物学报, 2018, 44(12): 1882-1890.
[13] 方彦，孙万仓，武军艳，刘自刚，董云，米超，马骊，陈奇，何辉立. 北方白菜型冬油菜的膜脂脂肪酸组分和ATPase活性对温度的响应[J]. 作物学报, 2018, 44(01): 95-104.
[14] 高林,杨贵军,李长春,冯海宽,徐波,王磊,董锦绘,付奎. 基于光谱特征与PLSR结合的叶面积指数拟合方法的无人机画幅高光谱遥感应用[J]. 作物学报, 2017, 43(04): 549-557.
[15] 马骊,袁金海,孙万仓,刘自刚,曾秀存,武军艳,方彦,李学才,陈奇,许耀照,蒲媛媛,刘海卿,杨刚,刘林波. 白菜型冬油菜类甜蛋白的筛选、克隆及其在低温胁迫下的表达[J]. 作物学报, 2017, 43(04): 620-628.
Viewed
Full text


Abstract

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