Scientia Agricultura Sinica ›› 2021, Vol. 54 ›› Issue (16): 3417-3427.doi: 10.3864/j.issn.0578-1752.2021.16.005

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Research on Winter Wheat Yield Estimation Based on Hyperspectral Remote Sensing and Ensemble Learning Method

FEI ShuaiPeng1,2(),YU XiaoLong2,LAN Ming2,LI Lei2,XIA XianChun2,HE ZhongHu2,3,XIAO YongGui2()   

  1. 1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, Henan
    2Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081
    3CIMMYT-China Office, c/o CAAS, Beijing 100081
  • Received:2020-11-18 Accepted:2021-04-08 Online:2021-08-16 Published:2021-08-24
  • Contact: YongGui XIAO E-mail:feishuaipeng@163.com;xiaoyonggui@caas.cn

RichHTML

5

PDF

725

Abstract:

【Objective】Using the hyperspectral data of winter wheat canopy at different development stages under two irrigation treatments, the estimation accuracy of wheat grain yield was studied by machine learning method, and the best yield estimation model was defined, which had the important application value for crop breeding. 【Method】 A total of 207 widely-grown wheat varieties in the Yellow and Huai Valleys Winter Wheat Zone (YHVWWZ) of China were planted under full irrigation and limited irrigation treatments in Xinxiang, Henan province during two consecutive growing seasons of 2018-2019 and 2019-2020, the canopy hyperspectral was investigated at three growth stages after flowering, and six machine learning methods and ensemble methods were adopted to establish yield prediction model by using spectral index as input features.【Result】 The spectral indices at each growth stage were significantly correlated with yield (P<0.0001) under both the two irrigation treatments, and also showed high heritability (0.61-0.85) across all the three growth stages under both the irrigation treatment, which were mainly controlled by genetic factors. Under the full irrigation treatment, compared with the model with the best performance of traditional machine learning methods, the average coefficient of determination (R2) of ensemble learning method in the three growth stages increased from 0.610, 0.611 and 0.640 to 0.649, 0.612 and 0.675, respectively, and the average root mean square error (RMSE) decreased to 0.607, 0.612 and 0.593 t·hm-2, respectively; Under the limited irrigation treatment, the average R 2 increased from 0.461, 0.408 and 0.452 to 0.467, 0.433 and 0.498, respectively, and the average RMSE decreased to 0.519, 0.559 and 0.504 t·hm -2, respectively.【Conclusion】Combining the prediction results of different models with the ensemble learning method could effectively improve the yield estimation accuracy, and the mid grain filling achieved the best prediction accuracy under both the two irrigation treatments. Overall, this study could provide the reference for yield estimation in winter wheat breeding.

Key words: winter wheat, grain yield, hyperspectral, ensemble method, machine learning

Table 1

Spectral indices used in this study"

光谱指数 Spectral index 名称 Name 公式 Formula
NDVI[23] 归一化光谱指数
Normalized difference vegetation index		 $\frac{R_{800}-R_{670}}{R_{800}+R_{670}}$
MCARI[24] 修正叶绿素吸收比指数
Modified chlorophyll absorption ratio index		 $\left[\left(R_{702}-R_{671}\right)-0.2\left(R_{702}-R_{549}\right)\right] \times \frac{R_{702}}{R_{671}}$
NDRE[25] 归一化红边光谱指数
Normalized difference red edge		 $\frac{R_{790}-R_{720}}{R_{790}+R_{720}}$
GNDVI[26] 绿色归一化光谱指数
Green normalized difference vegetation index		 $\frac{R_{750}-R_{550}}{R_{750}+R_{550}}$
MSR[23] 修正红边比值指数
Modified simple ratio index		 $\frac{R_{750} / R_{705}-1}{\sqrt{R_{750} / R_{705}+1}}$
NDRSR[27] 归一化红边简单比值指数
Normalized difference red-edge simple ratio		 $\frac{R_{872}-R_{712}}{R_{872}+R_{712}}$
MTVI[28] 修正三角光谱指数
Modified triangular vegetation index		 1.2[1.2(R800-R500)-2.6(R670-R550)]
MTCI2[29] MERIS陆地叶绿素指数2
MERIS terrestrial chlorophyll index 2		 $\frac{R_{754}-R_{709}}{R_{709}+R_{681}}$
MNDVI[30] 修正归一化光谱指数
Modified normalized difference vegetation index		 $\frac{R_{750}-R_{705}}{R_{750}+R_{705}-2 R_{445}}$
RDVI[31] 重归一化光谱指数
Renormalized difference vegetation index		 $\frac{R_{800}-R_{670}}{\sqrt{R_{800}+R_{670}}}$
VDI[32] 植被干指数
Vegetation dry index		 $\frac{R_{970}-R_{900}}{R_{970}+R_{900}}$
CI[33] 叶绿素指数
Chlorophyll index		 (R749-R720)-(R701-R672)
VREI[34] 沃格尔曼红边指数
Vogelmann red edge index		 $\frac{R_{742}}{R_{722}}$
ARVI[35] 大气抗性光谱指数
Atmospherically resistant vegetation index		 $\frac{R_{872}-\left[R_{661}-\left(R_{488}-R_{661}\right)\right]}{R_{872}+\left[R_{661}-\left(R_{488}-R_{661}\right)\right]}$
NDMI[36] 归一化物质指数
Normalized difference matter index		 $\frac{R_{1649}-R_{1792}}{R_{1649}+R_{1792}}$

Fig. 1

Flow chart for establishing grain yield estimation model based on Stacking method"

Table 2

Correlation analysis of spectral index and grain yield under full irrigation treatment, spectral index heritability"

光谱指数
Spectral index		 开花期Flowering 灌浆前期Early grain filling 灌浆中期Mid grain filling
|r| H2 |r| H2 |r| H2
NDVI 0.50*** 0.83 0.53*** 0.75 0.66*** 0.74
MCARI 0.61*** 0.85 0.65*** 0.85 0.69*** 0.82
NDRE 0.71*** 0.81 0.65*** 0.79 0.72*** 0.78
GNDVI 0.68*** 0.82 0.63*** 0.78 0.71*** 0.77
MSR 0.65*** 0.80 0.62*** 0.76 0.70*** 0.76
NDRSR 0.72*** 0.82 0.67*** 0.79 0.73*** 0.79
MTVI 0.59*** 0.77 0.60*** 0.73 0.63*** 0.75
MTCI2 0.63*** 0.83 0.59*** 0.80 0.68*** 0.80
MNDVI 0.62*** 0.83 0.63*** 0.76 0.69*** 0.83
RDVI 0.60*** 0.77 0.62*** 0.77 0.66*** 0.78
VDI 0.45*** 0.84 0.43*** 0.80 0.62*** 0.83
CI 0.61*** 0.82 0.59*** 0.79 0.64*** 0.81
VREI 0.69*** 0.81 0.65*** 0.75 0.72*** 0.76
ARVI 0.52*** 0.79 0.54*** 0.73 0.65*** 0.73
NDMI 0.53*** 0.80 0.56*** 0.63 0.61*** 0.77

Table 3

Correlation analysis of spectral index and grain yield under limited irrigation treatment, spectral index heritability"

光谱指数
Spectral index		 开花期 Flowering 灌浆前期 Early grain filling 灌浆中期 Mid grain filling
|r| H2 |r| H2 |r| H2
NDVI 0.48*** 0.63 0.42*** 0.69 0.53*** 0.65
MCARI 0.57*** 0.64 0.49*** 0.70 0.56*** 0.68
NDRE 0.56*** 0.67 0.48*** 0.78 0.55*** 0.73
GNDVI 0.54*** 0.66 0.41*** 0.73 0.54*** 0.71
MSR 0.55*** 0.65 0.43*** 0.74 0.52*** 0.69
NDRSR 0.57*** 0.68 0.49*** 0.79 0.55*** 0.73
MTVI 0.45*** 0.64 0.46*** 0.68 0.49*** 0.68
MTCI2 0.50*** 0.62 0.45*** 0.71 0.50*** 0.66
MNDVI 0.52*** 0.64 0.49*** 0.67 0.51*** 0.71
RDVI 0.49*** 0.66 0.47*** 0.65 0.50*** 0.67
VDI 0.59*** 0.69 0.61*** 0.68 0.58*** 0.73
CI 0.48*** 0.64 0.49*** 0.74 0.52*** 0.74
VREI 0.55*** 0.68 0.48*** 0.68 0.52*** 0.69
ARVI 0.49*** 0.61 0.42*** 0.71 0.51*** 0.68
NDMI 0.44*** 0.73 0.49*** 0.66 0.50*** 0.72

Fig. 2

The R2 (a) and RMSE (b) distribution on the test set during cross-validation of six primary models at flowering"

Fig. 3

The R2 (a) and RMSE (b) distribution on the test set during cross-validation of six primary models at early grain filling"

Fig. 4

The R2 (a) and RMSE (b) distribution on the test set during cross-validation of six primary models at mid grain filling"

Fig. 5

R2 (a) and RMSE (b) distribution on the test set during cross-validation of secondary model"

Table 4

The average coefficients of each primary learner in the modeling of the secondary learner"

模型
Model		 正常灌溉处理 Full irrigation treatment 节水处理 Limited irrigation treatment
开花期
Flowering		 灌浆前期
Early grain filling		 灌浆中期
Mid grain filling		 开花期
Flowering		 灌浆前期
Early grain filling		 灌浆中期
Mid grain filling
ANN -4.53 0.43 -0.59 -4.10 -2.81 0.93
GP 0.24 0.26 -1.85 2.10 0.77 0.86
MLR 1.91 -0.19 4.51 -2.14 -0.14 -2.02
RF 0.84 -0.30 -7.30 -0.47 0.02 -0.37
RR 6.13 0.61 4.32 3.20 4.06 3.28
SVM -3.41 0.42 2.34 2.43 -0.56 -1.59
[1] HERNANDEZ J, LOBOS G A, MATUS I, DEL POZO A, SILVA P, GALLEGUILLOS M. Using ridge regression models to estimate grain yield from field spectral data in bread wheat (Triticum aestivum L. ) grown under three water regimes. Remote Sensing, 2015, 7(2):2109-2126.
doi: 10.3390/rs70202109
[2] MONTESINOS-LÓPEZ O A, MONTESINOS-LÓPEZ A, CROSSA J, DELOS G, CAMPOS , ALVARADO G, SUCHISMITA M, RUTKOSKI J, GONZÁLEZ-PÉREZ L, BURGUEÑO J. Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data. Plant Methods, 2017, 13:4.
doi: 10.1186/s13007-016-0154-2
[3] HASSAN M A, YANG M, RASHEED A, YANG G, REYNOLDS M, XIA X, XIAO Y, HE Z. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Science, 2019, 282:95-103.
doi: 10.1016/j.plantsci.2018.10.022
[4] HASSAN M A, YANG M, RASHEED A, JIN X, XIA X, XIAO Y, HE Z. Time-series multispectral indices from unmanned aerial vehicle imagery reveal senescence rate in bread wheat. Remote Sensing, 2018, 10(6):809.
doi: 10.3390/rs10060809
[5] GITELSON A A, PENG Y, ARKEBAUER T J, SCHEPERS J. Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production. Remote Sensing of Environment, 2014, 144:65-72.
doi: 10.1016/j.rse.2014.01.004
[6] BOLTON D K, FRIEDL M A. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agricultural & Forest Meteorology, 2013, 173:74-84.
[7] 李岚涛, 李静, 明金, 汪善勤, 任涛, 鲁剑巍. 冬油菜叶面积指数高光谱监测最佳波宽与有效波段研究. 农业机械学报, 2018, 49(2):156-165.
LI L T, LI J, MING J, WANG S Q, REN T, LU J W. Selection optimization of hyperspectral bandwidth and effective wavelength for predicting leaf areaindex in winter oilseed rape. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(2):156-165. (in Chinese)
[8] SHAH S H, ANGEL Y, HOUBORG R, ALI S, MCCABE M F. A random forest machine learning approach for the retrieval of leaf chlorophyll content in wheat. Remote Sensing, 2019, 11:920.
doi: 10.3390/rs11080920
[9] BREIMANL. Random forests. Machine Learning, 2001, 45:5-32.
doi: 10.1023/A:1010933404324
[10] SAIN, STEPHAN R. The nature of statistical learning theory. Technometrics, 1996, 38:409.
[11] BRADLEY J B. Neural networks: A comprehensive foundation. Information Processing & Management, 1995, 31:786.
doi: 10.1016/0306-4573(95)90003-9
[12] WANG L, ZHOU X, ZHU X, DONG Z, GUO W. Estimation of biomass in wheat using random forest regression algorithm and remote sensing data. The Crop Journal, 2016, 4:212-219.
doi: 10.1016/j.cj.2016.01.008
[13] YUAN H, YANG G, LI C, WANG Y, LIU J, YU H, FENG H, XU B, ZHAO X, YANG X. Retrieving soybean leaf area index from unmanned aerial vehicle hyperspectral remote sensing: Analysis of RF, ANN, and SVM regression models. Remote Sensing, 2017, 9:309.
doi: 10.3390/rs9040309
[14] JIN X, XU X, SONG X, LI Z, WANG J, GUO W. Estimation of leaf water content in winter wheat using grey relational analysis-partial least squares modeling with hyperspectral data. Agronomy Journal, 2013, 105:1385-1392.
doi: 10.2134/agronj2013.0088
[15] FENG L, ZHANG Z, MA Y, DU Q, WILLIAMS P, DREWRY J, LUCK B. Alfalfa yield prediction using UAV-based hyperspectral imagery and ensemble learning. Remote Sensing, 2020, 12(12):2028.
doi: 10.3390/rs12122028
[16] WOLPERT D H. Stacked generalization. Neural Networks, 1992, 5:241-259.
doi: 10.1016/S0893-6080(05)80023-1
[17] TING K M, WITTEN I H. Issues in stacked generalization. Journal of Artificial Intelligence Research, 1999, 10:271-289.
doi: 10.1613/jair.594
[18] FU P, MEACHAM-HENSOLD K, GUAN K, BERNACCHI C J. Hyperspectral leaf reflectance as proxy for photosynthetic capacities: An ensemble approach based on multiple machine learning algorithms. Frontiers in Plant Science, 2019, 10.
[19] HEALEY S P, COHEN W B, YANG Z, KENNETH BREWER C, BROOKS E B, GORELICK N, HERNANDEZ A J, HUANG C, JOSEPH HUGHES M, KENNEDY R E, LOVELAND T R, MOISEN G G, SCHROEDER T A, STEHMAN S V, VOGELMANN J E, WOODCOCK C E, YANG L, ZHU Z. Mapping forest change using stacked generalization: An ensemble approach. Remote Sensing of Environment, 2018, 204:717-728.
doi: 10.1016/j.rse.2017.09.029
[20] WILLIAMSCK, RASMUSSENCE. Gaussian processes for machine learning. Cambridge, CA: MIT Press, 2006.
[21] MCDONALD G C. Ridge regression. Wiley Interdisciplinary Reviews Computational Statistics, 2009, 1:93-100.
doi: 10.1002/wics.v1:1
[22] LIANG L, DI L P, ZHANG L P, DENG M X, QIN Z H, ZHAO S H, LIN H. Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Remote Sensing of Environment, 2015, 165:123-134.
doi: 10.1016/j.rse.2015.04.032
[23] SIMS D A, GAMON J A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 2002, 81(2/3):337-354.
doi: 10.1016/S0034-4257(02)00010-X
[24] DAUGHTRY C S T, WALTHALL C L, KIM M S, DE COLSTOUN E B, MCMURTREY J E. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 2000, 74:229-239.
doi: 10.1016/S0034-4257(00)00113-9
[25] RODRIGUEZ D, FITZGERALD G J, BELFORD R, CHRISTENSEN L K. Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts. Australian Journal of Agricultural Research, 2006, 57:781-789.
doi: 10.1071/AR05361
[26] GITELSON A A, KAUFMAN Y J, MERZLYAK M N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 1996, 58:289-298.
doi: 10.1016/S0034-4257(96)00072-7
[27] GITELSON A A, VINA A, CIGANDA V, RUNDQUIST D C, ARKEBAUER T J. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 2005, 32:1-4.
[28] HABOUDANE D, MILLER J R, PATTEY E, ZARCO-TEJADA P J, STRACHAN I B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 2004, 90:337-352.
doi: 10.1016/j.rse.2003.12.013
[29] DASH J, CURRAN P J. Evaluation of the meris terrestrial chlorophyll index (MTCI). Advances in Space Research, 2007, 39:100-104.
doi: 10.1016/j.asr.2006.02.034
[30] SIMS D A, GAMON J A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 2002, 81:337-354.
doi: 10.1016/S0034-4257(02)00010-X
[31] ROUJEAN J L, BREON F M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 1995, 51:375-384.
doi: 10.1016/0034-4257(94)00114-3
[32] PENUELAS J, FILELLA I, BIEL C S, SERRANO L, SAVE R. The Reflectance at the 950-970 Nm region as an indicator of plant water status. International Journal of Remote Sensing, 1993, 14(10):1887-1905.
doi: 10.1080/01431169308954010
[33] GUPTA R K, VIJAYAN D, PRASAD T S. New hyperspectral vegetation characterization parameters. Advances in Space Research, 2001, 28(1):201-206.
doi: 10.1016/S0273-1177(01)00346-5
[34] VOGELMANN J, ROCK B, MOSS D. Red edge spectral measurements from sugar maple leaves. Remote Sensing. 1993, 14:1563-1575.
[35] KAUFMAN Y J, TANRE D. Atmospherically resistant vegetation index (ARVI) for eos-modis. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30:261-270.
doi: 10.1109/36.134076
[36] WANG L, HUNT E R, JR, QU J J, HAO X, DAUGHTRY C S T. Towards estimation of canopy foliar biomass with spectral reflectance measurements. Remote Sensing of Environment, 2011, 115(3):836-840.
doi: 10.1016/j.rse.2010.11.011
[37] 周志华. 机器学习.第一版. 北京: 清华大学出版社, 2016: 181-182.
ZHOU Z H. Machine Learning.1st edition. Beijing: Tsinghua University Press, 2016: 181-182. (in Chinese)
[38] 邓威, 郭钇秀, 李勇, 朱亮, 刘定国. 基于特征选择和Stacking集成学习的配电网网损估测. 电力系统保护与控制, 2020, 48:108-115.
DENG W, GUO Y X, LI Y, ZHU L, LIU D G. Power losses prediction based on feature selection and Stacking integrated learning. Power System Protection and Control, 2020, 48:108-115. (in Chinese)
[39] JULIANE B, ANDREAS B, SIMON B, JANIS B, SILAS E, GEORG B. Estimating biomass of barley using crop surface models (CSMs) derived from UAV-Based RGB imaging. Remote Sensing, 2014, 6(11):10395-10412.
doi: 10.3390/rs61110395
[40] ZOU X C, MOTTUS M. Sensitivity of common vegetation indices to the canopy structure of field crops. Remote Sensing, 2017, 9:994.
doi: 10.3390/rs9100994
[41] FENG L, LI Y, WANG Y, DU Q. Estimating hourly and continuous ground-level PM2.5 concentrations using an ensemble learning algorithm: The ST-Stacking model. Atmospheric Environment, 2020, 223:117242.
doi: 10.1016/j.atmosenv.2019.117242
[42] FRAME J, MERRILEES D W. The effect of tractor wheel passes on herbage production from diploid and tetraploid ryegrass swards. Grass and Forage Science, 1996, 51:13-20.
doi: 10.1111/gfs.1996.51.issue-1
[43] VAN D L, M J, POLLEY E C, HUBBARDAE. Super learner. Statistical Applications in Genetics & Molecular Biology, 2007, 6(1):25.
[44] 陈智芳, 宋妮, 王景雷, 孙景生. 基于高光谱遥感的冬小麦叶水势估测模型. 中国农业科学, 2017, 50(5):871-880.
CHEN Z F, SONG N, WANG J L, SUN J S. Leaf water potential estimating models of winter wheat based on hyperspectral remote sensing. Scientia Agricultura Sinica, 2017, 50(5):871-880. (in Chinese)
[1] ZHU Qi, JIA ZhenPeng, Tahir SHAH, XU ChenSheng, LI ZhiQi, LÜ HuiShuai, ZHU PengChao, WEI XiaoMin, HUANG DongLin, SUN YanNi, CAO WeiDong, GAO YaJun, WANG ZhaoHui, ZHANG DaBin. Green Manure Crops Combined with Enhanced-Efficiency Products Reduced Greenhouse Gas Emissions and Carbon Footprints in Dryland Wheat Fields [J]. Scientia Agricultura Sinica, 2026, 59(7): 1507-1522.
[2] WANG JiaNuo, CHEN GuiPing, LI Pan, WANG LiPing, NAN YunYou, HE Wei, FAN ZhiLong, HU FaLong, CHAI Qiang, YIN Wen, ZHAO LiaoHao. Photo-Physiological Mechanism at Grain Filling Stage of No-Tillage with Plastic Re-Mulching to Increase Maize Yield in Oasis Irrigation Areas [J]. Scientia Agricultura Sinica, 2026, 59(6): 1189-1202.
[3] QIAN Jin, LI YingXue, WU Fang, ZOU XiaoChen. Improved Leaf Phosphorus Content Estimation of Winter Wheat Using Ensemble Hyperspectral Dimensionality Reduction Method [J]. Scientia Agricultura Sinica, 2026, 59(4): 781-792.
[4] KONG Yuan, CUI ShaSha, LI Mei, LI Jian, YANG SiYu, FANG Feng, LIU ShuaiShuai, LIU MingPing, ZENG Yan, GAO XingXiang, BAI LianYang. Spatiotemporal Distribution Dynamics of Five Grass Weed Species Including Lolium multiflorum in Winter Wheat Fields of the Huang- Huai-Hai Region [J]. Scientia Agricultura Sinica, 2026, 59(4): 807-823.
[5] XIAN QingLin, XIAO JianKe, GAO AQing, GAO LiChuang, LIU Yang. Effects of Planting Patterns Combined with Soil Moisture Measurement and Supplementary Irrigation on the Yield and Water Use Efficiency of Winter Wheat [J]. Scientia Agricultura Sinica, 2026, 59(3): 589-601.
[6] LÜ XuDong, SUN ShiYuan, LI YaNan, LIU YuLong, WANG YanQun, FU Xin, ZHANG JiaYing, NING Peng, PENG ZhengPing. Effects of Intelligent Mechanized Layered Fertilization on Root-Soil Nutrient Distribution and Yield in Wheat Fields [J]. Scientia Agricultura Sinica, 2026, 59(1): 129-146.
[7] FEI YaoYing, WANG Di, TANG WeiJie, GUO CaiLi, ZHANG XiaoHu, QIU XiaoLei, CHENG Tao, YAO Xia, JIANG ChongYa, ZHU Yan, CAO WeiXing, ZHENG HengBiao. Estimation of Rice Grain Protein Content Using Fusion Imagery from UAV-based Multi-Sensors [J]. Scientia Agricultura Sinica, 2026, 59(1): 41-56.
[8] WANG AiDong, LI RuiJie, FENG XiangQian, HONG WeiYuan, LI ZiQiu, ZHANG XiaoGuo, WANG DanYing, CHEN Song. Multi-Angle Imaging and Machine Learning Approaches for Accurate Rice Leaf Area Estimation [J]. Scientia Agricultura Sinica, 2025, 58(9): 1719-1734.
[9] PU LiXia, ZHANG JiaRui, YE JianPing, HUANG XiuLan, FAN GaoQiong, YANG HongKun. The Combined Effects of 16, 17-Dihydro Gibberellin A5 and Straw Mulching on Tillering and Grain Yield of Dryland Wheat [J]. Scientia Agricultura Sinica, 2025, 58(9): 1735-1748.
[10] LIU JinSong, WU LongMei, BAO XiaoZhe, LIU ZhiXia, ZHANG Bin, YANG TaoTao. Effects of a Short-Term Reduction in Nitrogen Fertilizer Application Rates on the Grain Yield and Rice Quality of Early and Late-Season Dual-Use Rice in South China [J]. Scientia Agricultura Sinica, 2025, 58(8): 1508-1520.
[11] YIN Bo, YU AiZhong, WANG PengFei, YANG XueHui, WANG YuLong, SHANG YongPan, ZHANG DongLing, LIU YaLong, LI Yue, WANG Feng. Effects of Green Manure Returning Combined with Nitrogen Fertilizer Reduction on Hydrothermal Characteristics of Wheat Field and Grain Yield in Oasis Irrigation Area [J]. Scientia Agricultura Sinica, 2025, 58(7): 1366-1380.
[12] SHI Fan, LI WenGuang, YI ShuSheng, YANG Na, CHEN YuMeng, ZHENG Wei, ZHANG XueChen, LI ZiYan, ZHAI BingNian. The Variation Characteristics of Soil Organic Carbon Fractions Under the Combined Application of Organic and Inorganic Fertilizers [J]. Scientia Agricultura Sinica, 2025, 58(4): 719-732.
[13] QIU HaiLong, LI Pan, ZHANG DianKai, FAN ZhiLong, HU FaLong, CHEN GuiPing, FAN Hong, HE Wei, YIN Wen, ZHAO LianHao. Compensatory Effects of Multiple Cropping Green Manure on Growth and Yield Loss of Nitrogen-Reduced Spring Wheat in Oasis Irrigation Areas of Northwest China [J]. Scientia Agricultura Sinica, 2025, 58(3): 443-459.
[14] FANG KangRui, DING ShiJie, CHEN YuShan, YANG BingGeng, GUO TengFei, XU XinPeng, ZHAO ShiCheng, WANG XiuBin, HUANG ShaoMin, QIU ShaoJun, HE Ping, ZHOU Wei. In-Season Release Rate of Nitrogen and Phosphorus in Manure Fertilizers During the Wheat Season in Typical Fluvo-Aquic Soil Under the Combined Application of Chemical and Manure Fertilizers [J]. Scientia Agricultura Sinica, 2025, 58(24): 5234-5246.
[15] ZHANG Feng, XU JunFeng, GAO ZhiYuan, DANG HaiYan, GUO RongBo, SHE WenTing, LI WenHu, LIU JinShan, WANG ZhaoHui. Optimizing Wheat Nitrogen and Phosphorus Fertilizer Rates Based on Apparent Nitrogen and Phosphorus Balance in a Long-Term Location Fixed Field Experiment [J]. Scientia Agricultura Sinica, 2025, 58(22): 4688-4702.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备09089781号-30
Copyright 2018 © Editorial office of Scientia Agricultura Sinica
Tel: 86-010-82106279    E-mail: zgnykx@mail.caas.net.cn
Supported by Beijing Magtech Co.Ltd