Welcome to Acta Agronomica Sinica，

Acta Agronomica Sinica ›› 2025, Vol. 51 ›› Issue (5): 1326-1337.doi: 10.3724/SP.J.1006.2025.44157

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

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 Online:2025-05-12 Published: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)

RichHTML437

2

PDF

53

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

Table 1

Physical and chemical properties of soil"

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

Fig. 1

Locations of experiment fields This map is based on the standard map with the approval number GS (2016) 2923 downloaded from the Ministry of Natural Resources Standard Map Service website, and the boundary of the base map has not been modified."

Table 2

Different fertilization treatments"

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

Fig. 2

Canopy spectra of rapeseed under different fertilizer application rates Treatments are the same as those given in Table 2."

Table 3

LNC and LPC of rapeseed under different fertilization treatments"

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

Table 4

Rapeseed LNC and LPC under different application rates of specialized fertilizer"

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

Fig. 3

Spectral distribution of winter rapeseed characteristics under three screening algorithms LNC: leaf nitrogen concentration; LPC: leaf phosphorus concentration. CARS: competitive adaptive reweighted sampling; UVE: uninformative variable elimination; SPA: successive projections algorithm. R: raw hyperspectral reflectance; FDR: first derivative reflectance."

Fig. 4

R2 and RMSE of PLSR model test set under different screening algorithms Abbreviations are the same as those given in Fig. 3."

Fig. 5

Comparison between predicted values and true values of oilseed rape LNC and LPC Abbreviations are the same as those given in Fig. 3."

Fig. 6

Yield and NNI of oilseed rape under different nitrogen fertilizer application rates N0, N60, N120, N180, and N240 represent nitrogen fertilizer application rates of 0, 60, 120, 180, and 240 kghm-2 in Jianshi area from 2023 to 2024, respectively. The lowercase letters in the figure indicate significant differences among nitrogen fertilizer application treatments within the same year at the 5% significance level. NNI: nitrogen nutrition index."

Fig. 7

Yield and PNI of oilseed rape under different phosphorus fertilizer application P0, P45, P90, P135, and P180 indicate phosphorus fertilizer application rates of 0, 45, 90, 135, and 180 kg hm-2 in Jianshi area from 2023 to 2024, respectively. The lowercase letters in the figure indicate significant differences among phosphorus fertilizer application treatments within the same year at the 5% significance level. PNI: phosphorous nutrition index."

Fig. 8

Comparison of independent validation dataset for rapeseed LNC, LPC predicted values and true values Abbreviations are the same as those given in Fig. 3."

Fig. 9

NNI and PNI values of rapeseed treated with different amounts of specialized fertilizers NNI: nitrogen nutrition index; PNI: phosphorous nutrition index. Treatments are the same as those given in Table 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] XIE Xiong-Ze, XIE Jie, CHU Qian-Mei, YIN Yu-Feng, YU Xiao-Hong, WANG Dun, FENG Peng. Analysis of water requirement and water surplus/deficit characteristics of winter rapeseed in Yangtze River Basin [J]. Acta Agronomica Sinica, 2024, 50(7): 1829-1840.
[2] NING Ning, MO Jiao, HU Bing, LI Da-Shuang, LOU Hong-Xiang, WANG Chun-Yun, BAI Chen-Yang, KUAI Jie, WANG Bo, WANG Jing, XU Zheng-Hua, LI Xiao-Hua, JIA Cai-Hua, ZHOU Guang-Sheng. Comparative study on the processing quality of winter rape in different ecological zones of the Yangtze River valley [J]. Acta Agronomica Sinica, 2023, 49(12): 3315-3327.
[3] FENG Zi-Heng, LI Xiao, DUAN Jian-Zhao, GAO Fei, HE Li, YANG Tian-Chong, RONG Ya-Si, SONG Li, YIN Fei, FENG Wei. Hyperspectral remote sensing monitoring of wheat powdery mildew based on feature band selection and machine learning [J]. Acta Agronomica Sinica, 2022, 48(9): 2300-2314.
[4] LI Jin-Min, CHEN Xiu-Qing, YANG Qi, SHI Liang-Sheng. Deep learning models for estimation of paddy rice leaf nitrogen concentration based on canopy hyperspectral data [J]. Acta Agronomica Sinica, 2021, 47(7): 1342-1350.
[5] WU Ya-Peng,HE Li,WANG Yang-Yang,LIU Bei-Cheng,WANG Yong-Hua,GUO Tian-Cai,FENG Wei. Dynamic model of vegetation indices for biomass and nitrogen accumulation in winter wheat [J]. Acta Agronomica Sinica, 2019, 45(8): 1238-1249.
[6] Chao MI,Yan-Ning ZHAO,Zi-Gang LIU,Qi-Xian CHEN,Wan-Cang SUN,Yan FANG,Xue-Cai LI,Jun-Yan WU. Cloning of RuBisCo Subunits Genes rbcL and rbcS from Winter Rapeseed (Brassica rapa) and Their Expression under Drought Stress [J]. Acta Agronomica Sinica, 2018, 44(12): 1882-1890.
[7] FANG Yan,SUN Wan-Cang,WU Jun-Yan，LIU Zi-Gang,DONG Yun,MI Chao,MA Li,CHEN Qi,HE Hui-Li. Response of Membrane Fatty Acid Composition and ATPase Activity in Brassica rapa L. to Temperature in North China [J]. Acta Agron Sin, 2018, 44(01): 95-104.
[8] GAO Lin,YANG Gui-Jun,LI Chang-Chun,FENG Hai-Kuan,XU Bo,WANG Lei,DONG Jin-Hui,FU Kui. Application of an Improved Method in Retrieving Leaf Area Index Combined Spectral Index with PLSR in Hyperspectral Data Generated by Unmanned Aerial Vehicle Snapshot Camera [J]. Acta Agron Sin, 2017, 43(04): 549-557.
[9] SUN Wan-Cang1,**,LIU Hai-Qing1,**,LIU Zi-Gang1,*,WU Jun-Yan,LI Xue-Cai,FANG Yan,ZENG Xiu-Cun,XU Yao-Zhao,ZHANG Ya-Hong,DONG Yun. Critical Index Analysisof Safe Over-wintering Rate ofWinter Rapeseed (Brassica rapa) in Cold and Arid Region in North China [J]. Acta Agron Sin, 2016, 42(04): 609-618.
[10] ZUO Qing-Song,KUAI Jie,YANG Shi-Fen,CAO Shi,YANG Yang,WU Lian-Rong,SUN Ying-Ying,ZHOU Guang-Sheng,WU Jiang-Sheng. Effects of Nitrogen Fertilizer and Planting Density on Canopy Structure and Population Characteristic of Rapeseed with Direct Seeding Treatment [J]. Acta Agron Sin, 2015, 41(05): 758-765.
[11] ZENG Xiu-Cun,LIU Zi-Gang,SHI Peng-Hui,XU Yao-Zhao,SUN Jia,FANG Yan,YANG Gang,WU Jun-Yan,KONG De-Jing,SUN Wan-Cang. Cloning and Expression Analysis of Copper and Zinc Superoxide Dismutase (Cu/Zn-SOD) Gene from Brassica campestris L. [J]. Acta Agron Sin, 2014, 40(04): 636-643.
[12] LIU Zi-Gang,ZHANG Chang-Sheng,SUN Wan-Cang,YANG Ning-Ning,WANG Yue,HE Li,ZHAO Cai-Xia,WU Jun-Yan,FANG Yan,ZENG Xiu-Cun. Comparison of Winter Rapeseed Varieties (Lines) Different with Cold-Resistance Planted in the Northern-Extending Regions in China under Low Temperature before Winter [J]. Acta Agron Sin, 2014, 40(02): 346-354.
[13] ZENG Xiu-Cun1,2,SUN Wan-Cang1,*,FANG Yan1,LIU Zi-Gang1,DONG Yun3,SUN Jia4,WU Jun-Yan1,ZHANG Peng-Fei1,SHI Peng-Hui1,KONG De-Jing1,ZHANG Teng-Guo5,HE Li1,ZHAO Cai-Xia1. Cloning, Expression, and Activity Analysis of Ascorbate Peroxidase (APX) Gene from Winter Turnip Rape (Brassica campestris L.) [J]. Acta Agron Sin, 2013, 39(08): 1400-1408.
[14] FENG Wei,WANG Xiao-Yu,SONG Xiao,HE Li,WANG Yong-Hua,GUO Tian-Cai. Estimation of Severity Level of Wheat Powdery Mildew Based on Canopy Spectral Reflectance [J]. Acta Agron Sin, 2013, 39(08): 1469-1477.
[15] WU Qiong,QI Bo,ZHAO Tuan-Jie,YAO Xin-Feng,ZHU Yan,GAI Jun-Yi. A Tentative Study on Utilization of Canopy Hyperspectral Reflectance to Esti-mate Canopy Growth and Seed Yield in Soybean [J]. Acta Agron Sin, 2013, 39(02): 309-318.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Add: No. 80 Xueyuan South Rd, Beijing 100081, P. R. China E-mail: zwxb301@caas.cn
Supported by Beijing Magtech Co.Ltd