Scientia Agricultura Sinica ›› 2025, Vol. 58 ›› Issue (19): 3857-3871.doi: 10.3864/j.issn.0578-1752.2025.19.005

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

Comprehensive Assessment of Wheat Seedling Growth Status Based on Multimodal Data

SHAO MingChao(), AN JingWei, LIU BoRui, WU JianShuang, ZHANG Qi, YAO Xia, CHENG Tao, JIANG ChongYa, CAO WeiXing, ZHENG HengBiao(), ZHU Yan()   

  1. College of Agriculture, Nanjing Agricultural University/National Engineering and Technology Center for Information Agriculture/ Engineering Research Center of Smart Agriculture, Ministry of Education/Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs/Jiangsu Key Laboratory for Information Agriculture, Nanjing 210095
  • Received:2025-03-25 Accepted:2025-07-23 Online:2025-10-01 Published:2025-10-10
  • Contact: ZHENG HengBiao, ZHU Yan

RichHTML

12

PDF

108

Abstract:

【Objective】The seedling condition of wheat reflects the growth status and health level of the seedlings, which is an important basis for yield prediction and field management. Traditional seedling condition evaluation methods relying on manual expertise have limitations in large-scale field applications, such as low efficiency, strong subjectivity, and difficulty in generalization. This study used UAVs equipped with RGB sensors, combined with ground-measured agronomic parameters, to explore a comprehensive wheat seedling condition assessment method that integrated multimodal remote sensing features, so as to provide a technical pathway for large-scale and cross-regional monitoring.【Method】Multi-site and multi-altitude UAV flight tests were designed in Jiangsu Province to capture UAV imagery and to simultaneously collect agronomic parameters, such as tiller number and canopy coverage. Based on vegetation indices and texture features, crop spectral and structural information was extracted. Feature selection was performed using Information Value (IV) and GINI coefficients. Various machine learning models, including random forest (RF), extreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT), were developed to assess the classification accuracy of seedling condition. The optimal image resolution was determined based on the local variance coefficient to enhance the stability and cross-regional adaptability of the application.【Result】The enhanced green-red difference index (EXGR) showed the best accuracy in identifying canopy coverage during the seedling stage (Pixel Accuracy (PA)=0.69, Specificity (S)=0.83). The green-red ratio index (GRRI) exhibited a significant correlation with tiller number (R2=0.58, relative root mean square error (rRMSE)=0.28). The Random Forest algorithm, which integrates agronomic parameters and remote sensing features, achieved the highest accuracy in seedling condition grade classification (PA=0.85, R=0.86). Tiller number and texture information (E_energy) contributed the most to the seedling condition grade classification (IV>0.70). A flight altitude of (35±5) m was found to be an important reference for obtaining high-quality UAV data (local variance=0.17).【Conclusion】This study constructed a comprehensive wheat seedling condition assessment framework integrating agronomic parameters and remote sensing features, which demonstrated the feasibility and efficiency of portable UAV RGB imagery combined with machine learning methods for cross-regional seedling condition grade monitoring. This method could provide data support and methodological references for regional-scale dynamic seedling condition assessment, crop management strategy formulation, and food security assurance.

Key words: seedling condition assessment, UAV RGB imagery, agronomic parameters, random forest, multimodal remote sensing features, wheat

Fig. 1

Location of the study area"

Table 1

Vegetable indexes used in this study"

植被指数 Vegetation index 公式 Formula
绿红比指数 Green-red ration index (GRRI) G/R
绿蓝比指数 Green-blue ration index (GBRI) G/B
蓝红比指数 Blue-red ration index (BRRI) B/R
过红指数 Excess red (EXR) 1.4×R-G
过绿指数 Excess green (EXG) 2×G-R-B
过绿-过红指数 Excess green minus excess red (EXGR) EXG-EXR
植被颜色指数 Color index of vegetation (CIVE) 0.441×r-0.881×g+0.385×b+18.7874
修正超绿指数 Modified excess green (MEXG) 1.262×g-0.884×r-0.311×b
红绿蓝植被指数 Red green blue vegetation Index (RGBVI) (G×G-B×R)/(G×G+B×R)
归一化红绿差异指数Normalized green-red difference index (NGBDI) (G-B)/(G+B)
可见光差异植被指数 Visible band difference vegetation index (VDVI) (2×G-R-B)/(2×G+R+B)

Table 2

Accuracy of canopy cover detection"

植被指数
Vegetation index		 像素精度
Pixel accuracy		 召回率
Recall		 F1分数
F1 score		 特异性
Specificity		 交并比
Intersection over union
EXGR 0.69 0.69 0.70 0.83 0.47
CIVE 0.59 0.68 0.56 0.76 0.41
EXG 0.68 0.66 0.61 0.86 0.46
EXR 0.50 0.71 0.55 0.65 0.39
MEXG 0.58 0.65 0.55 0.77 0.40

Fig. 2

Results of the seedling canopy cover detection"

Fig. 3

Estimation results of tiller number"

Fig. 4

The importance of features"

Table 3

The accuracy of seedling condition detection"

苗情等级 Class 精度指标 Metrics 随机森林 RF 极端梯度提升 XGBOOST 梯度提升决策树 GBDT 决策树 DT
1 像素精度 PA 0.90 1.00 0.95 0.88
召回率 R 1.00 0.32 0.95 0.79
2 像素精度 PA 0.83 0.57 0.86 0.70
召回率 R 0.94 0.64 0.86 0.83
3 像素精度 PA 0.83 0.57 0.80 0.73
召回率 R 0.77 0.18 0.84 0.55
4 像素精度 PA 0.82 0.40 0.92 0.65
召回率 R 0.79 0.81 0.81 0.79
5 像素精度 PA 0.89 0.82 0.79 0.83
召回率 R 0.81 0.67 0.90 0.71

Fig. 5

AUC curve of random forest classifier"

Fig. 6

Monitoring results at different heights"

Fig. 7

The result of seedling condition detecting"

Fig. 8

Wheat seedling condition change chart"

Fig. 9

EXGR image profile"

[1]
FAO. The State of Agricultural Commodity Markets 2022. Rome: FAO, 2022.
[2]
LI L C, WANG B, FENG P Y, DE LI LIU, HE Q S, ZHANG Y J, WANG Y K, LI S Y, LU X L, YUE C, LI Y, HE J Q, FENG H, YANG G J, YU Q. Developing machine learning models with multi-source environmental data to predict wheat yield in China. Computers and Electronics in Agriculture, 2022, 194: 106790.
[3]
IPCC Intergovernmental Panel on Climate Change. Climate Change 2022 - Mitigation of Climate Change: Working Group III Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, 2023.
[4]
BAI Y, NIE C W, WANG H W, CHENG M H, LIU S B, YU X, SHAO M C, WANG Z X, WANG S Y, TUOHUTI N, SHI L, MING B, JIN X L. A fast and robust method for plant count in sunflower and maize at different seedling stages using high-resolution UAV RGB imagery. Precision Agriculture, 2022, 23(5): 1720-1742.
[5]
WANG F H, HE Z H, SAYRE K, LI S D, SI J S, FENG B, KONG L G. Wheat cropping systems and technologies in China. Field Crops Research, 2009, 111(3): 181-188.
[6]
ZHU X J, LIU X, WU Q, LIU M S, HU X L, DENG H, ZHANG Y, QU Y F, WANG B Q, GOU X M, et al. Utilizing UAV-based high-throughput phenotyping and machine learning to evaluate drought resistance in wheat germplasm. Computers and Electronics in Agriculture, 2025, 237: 110602.
[7]
LI J M, INANAGA S, LI Z H, ENEJI A E. Optimizing irrigation scheduling for winter wheat in the North China Plain. Agricultural Water Management, 2005, 76(1): 8-23.
[8]
DING Y G, ZHANG X B, MA Q, LI F J, TAO R R, ZHU M, LI C Y, ZHU X K, GUO W S, DING J F. Tiller fertility is critical for improving grain yield, photosynthesis, and nitrogen efficiency in wheat. Journal of Integrative Agriculture, 2023, 22(7): 2054-2066.
[9]
TIMSINA J, CONNOR D J. Productivity and management of rice-wheat cropping systems: issues and challenges. Field Crops Research, 2001, 69(2): 93-132.
[10]
费帅鹏, 禹小龙, 兰铭, 李雷, 夏先春, 何中虎, 肖永贵. 基于高光谱遥感和集成学习方法的冬小麦产量估测研究. 中国农业科学, 2021, 54(16): 3417-3427. doi:10.3864/j.issn.0578-1752.2021.16.005.
FEI S P, YU X L, LAN M, LI L, XIA X C, HE Z H, XIAO Y G. Research on winter wheat yield estimation based on hyperspectral remote sensing and ensemble learning method. Scientia Agricultura Sinica, 2021, 54(16): 3417-3427. doi:10.3864/j.issn.0578-1752.2021.16.005. (in Chinese)
[11]
夏天, 吴文斌, 周清波, 周勇. 冬小麦叶面积指数高光谱遥感反演方法对比. 农业工程学报, 2013, 29(3): 139-147.
XIA T, WU W B, ZHOU Q B, ZHOU Y. Comparison of two inversion methods for winter wheat leaf area index based on hyperspectral remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(3): 139-147. (in Chinese)
[12]
曹中盛, 李艳大, 叶春, 舒时富, 孙滨峰, 黄俊宝, 吴罗发. 基于高光谱的双季稻分蘖数监测模型. 农业工程学报, 2020, 36(4): 185-192.
CAO Z S, LI Y D, YE C, SHU S F, SUN B F, HUANG J B, WU L F. Model for monitoring tiller number of double cropping rice based on hyperspectral reflectance. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(4): 185-192. (in Chinese)
[13]
FANG Y, QIU X L, GUO T, WANG Y Q, CHENG T, ZHU Y, CHEN Q, CAO W X, YAO X, NIU Q S, HU Y Q, GUI L J. An automatic method for counting wheat tiller number in the field with terrestrial LiDAR. Plant Methods, 2020, 16(1): 132.
[14]
王爱冬, 李瑞杰, 冯向前, 洪卫源, 李子秋, 张晓果, 王丹英, 陈松. 基于多角度成像与机器学习的水稻叶面积精确估算. 中国农业科学, 2025, 58(9): 1719-1734. doi:10.3864/j.issn.0578-1752.2025.09.004.
WANG A D, LI R J, FENG X Q, HONG W Y, LI Z Q, ZHANG X G, WANG D Y, CHEN S. Multi-angle imaging and machine learning approaches for accurate rice leaf area estimation. Scientia Agricultura Sinica, 2025, 58(9): 1719-1734. doi:10.3864/j.issn.0578-1752.2025.09.004. (in Chinese)
[15]
冯子恒, 宋莉, 张少华, 井宇航, 段剑钊, 贺利, 尹飞, 冯伟. 基于无人机多光谱和热红外影像信息融合的小麦白粉病监测. 中国农业科学, 2022, 55(5): 890-906. doi:10.3864/j.issn.0578-1752.2022.05.005.
FENG Z H, SONG L, ZHANG S H, JING Y H, DUAN J Z, HE L, YIN F, FENG W. Wheat powdery mildew monitoring based on information fusion of multi-spectral and thermal infrared images acquired with an unmanned aerial vehicle. Scientia Agricultura Sinica, 2022, 55(5): 890-906. doi:10.3864/j.issn.0578-1752.2022.05.005. (in Chinese)
[16]
ZHENG H B, MA J F, ZHOU M, LI D, YAO X, CAO W X, ZHU Y, CHENG T. Enhancing the nitrogen signals of rice canopies across critical growth stages through the integration of textural and spectral information from unmanned aerial vehicle (UAV) multispectral imagery. Remote Sensing, 2020, 12(6): 957.
[17]
CHEN B, HUANG B, XU B. Multi-source remotely sensed data fusion for improving land cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 124: 27-39.
[18]
LU J J, DAI E F, MIAO Y X, KUSNIEREK K. Improving active canopy sensor-based in-season rice nitrogen status diagnosis and recommendation using multi-source data fusion with machine learning. Journal of Cleaner Production, 2022, 380: 134926.
[19]
JEONG J H, RESOP J P, MUELLER N D, FLEISHER D H, YUN K, BUTLER E E, TIMLIN D J, SHIM K M, GERBER J S, REDDY V R, KIM S H. Random forests for global and regional crop yield predictions. PLoS ONE, 2016, 11(6): e0156571.
[20]
BARBOSA B D S, FERRAZ G A E S, COSTA L, AMPATZIDIS Y, VIJAYAKUMAR V, DOS SANTOS L M. UAV-based coffee yield prediction utilizing feature selection and deep learning. Smart Agricultural Technology, 2021, 1: 100010.
[21]
HOQUE N, BHATTACHARYYA D K, KALITA J K. MIFS-ND: A mutual information-based feature selection method. Expert Systems with Applications, 2014, 41(14): 6371-6385.
[22]
MARIAMMAL G, SURULIANDI A, RAJA S P, POONGOTHAI E. Prediction of land suitability for crop cultivation based on soil and environmental characteristics using modified recursive feature elimination technique with various classifiers. IEEE Transactions on Computational Social Systems, 2021, 8(5): 1132-1142.
[23]
MENZE B H, KELM B M, MASUCH R, HIMMELREICH U, BACHERT P, PETRICH W, HAMPRECHT F A. A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinformatics, 2009, 10: 213.

doi: 10.1186/1471-2105-10-213 pmid: 19591666
[24]
LIU T, ZHAO Y Y, WU F, WANG J C, CHEN C, ZHOU Y Z, JU C X, HUO Z Y, ZHONG X C, LIU S P, SUN C M. The estimation of wheat tiller number based on UAV images and gradual change features (GCFs). Precision Agriculture, 2023, 24(1): 353-374.
[25]
CAO C Y, LAM N S. Understanding the scale and resolution effects in remote sensing and GIS. Scale in Remote Sensing and GIS. New York: Routledge, 2023: 57-72.
[26]
WOODCOCK C E, STRAHLER A H. The factor of scale in remote sensing. Remote Sensing of Environment, 1987, 21(3): 311-332.
[27]
MEYER G E, NETO J C. Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics in Agriculture, 2008, 63(2): 282-293.
[28]
汪小钦, 王苗苗, 王绍强, 吴云东. 基于可见光波段无人机遥感的植被信息提取. 农业工程学报, 2015, 31(5): 152-159.
WANG X Q, WANG M M, WANG S Q, WU Y D. Extraction of vegetation information from visible unmanned aerial vehicle images. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(5): 152-159. (in Chinese)
[29]
CASTILLO-MARTÍNEZ M Á, GALLEGOS-FUNES F J, CARVAJAL- GÁMEZ B E, URRIOLAGOITIA-SOSA G, ROSALES-SILVA A J. Color index based thresholding method for background and foreground segmentation of plant images. Computers and Electronics in Agriculture, 2020, 178: 105783.
[30]
OTSU N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66.
[31]
LIU T Y. EasyEnsemble and feature selection for imbalance data sets. 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing. Shanghai, China. IEEE, 2009: 517-520.
[32]
WANG Y, WONG A K C. From association to classification: Inference using weight of evidence. IEEE Transactions on Knowledge and Data Engineering, 2003, 15(3): 764-767.
[33]
SOREL L, VIAUD V, DURAND P, WALTER C. Modeling spatio-temporal crop allocation patterns by a stochastic decision tree method, considering agronomic driving factors. Agricultural Systems, 2010, 103(9): 647-655.
[34]
CHEN T, GUESTRIN C. XGBoost: A scalable tree boosting system. ACM, 2016.
[35]
CHENG M H, PENUELAS J, MCCABE M F, ATZBERGER C, JIAO X Y, WU W B, JIN X L. Combining multi-indicators with machine-learning algorithms for maize yield early prediction at the county-level in China. Agricultural and Forest Meteorology, 2022, 323: 109057.
[36]
AHMED I, ERAMIAN M, OVSYANNIKOV I, VAN DER KAMP W, NIELSEN K, DUDDU H S, RUMALI A, SHIRTLIFFE S, BETT K. Automatic detection and segmentation of lentil crop breeding plots from multi-spectral images captured by UAV- mounted camera. // 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). Waikoloa Village, HI, USA. IEEE, 2019: 1673-1681.
[37]
HE K M, GKIOXARI G, DOLLAR P, GIRSHICK R. Mask R-CNN. // 2017 IEEE International Conference on Computer Vision (ICCV). Venice. IEEE, 2017: 2980-2988.
[38]
ZHANG X H, QIAO Y, MENG F F, FAN C G, ZHANG M M. Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access, 2018, 6: 30370-30377.
[39]
UPENDAR K, AGRAWAL K N, CHANDEL N S, SINGH K. Greenness identification using visible spectral colour indices for site specific weed management. Plant Physiology Reports, 2021, 26(1): 179-187.
[40]
YANG W Z, WANG S L, ZHAO X L, ZHANG J S, FENG J Q. Greenness identification based on HSV decision tree. Information Processing in Agriculture, 2015, 2(3/4): 149-160.
[41]
PHILLIPS S B, KEAHEY D A, WARREN J G, MULLINS G L. Estimating winter wheat tiller density using spectral reflectance sensors for early-spring, variable-rate nitrogen applications. Agronomy Journal, 2004, 96(3): 1.
[42]
ZENG Y L, HAO D L, HUETE A, DECHANT B, BERRY J, CHEN J M, JOINER J, FRANKENBERG C, BOND-LAMBERTY B, RYU Y, XIAO J F, ASRAR G R, CHEN M. Optical vegetation indices for monitoring terrestrial ecosystems globally. Nature Reviews Earth & Environment, 2022, 3(7): 477-493.
[43]
LI D, CHEN J M, YU W G, ZHENG H B, YAO X, CAO W X, WEI D D, XIAO C C, ZHU Y, CHENG T. Assessing a soil-removed semi-empirical model for estimating leaf chlorophyll content. Remote Sensing of Environment, 2022, 282: 113284.
[44]
TIAN L, WANG Z Y, XUE B W, LI D, ZHENG H B, YAO X, ZHU Y, CAO W X, CHENG T. A disease-specific spectral index tracks Magnaporthe oryzae infection in paddy rice from ground to space. Remote Sensing of Environment, 2023, 285: 113384.
[45]
郝琪, 陈天陆, 王富贵, 王振, 白岚方, 王永强, 王志刚. 基于无人机多光谱数据和氮素空间分异的玉米冠层氮浓度估算. 作物学报, 2025, 51(1): 189-206.

doi: 10.3724/SP.J.1006.2025.43015
HAO Q, CHEN T L, WANG F G, WANG Z, BAI L F, WANG Y Q, WANG Z G. Estimation of canopy nitrogen concentration in maize based on UAV multi-spectral data and spatial nitrogen heterogeneity. Acta Agronomica Sinica, 2025, 51(1): 189-206. (in Chinese)

doi: 10.3724/SP.J.1006.2025.43015
[46]
申哲, 张认连, 龙怀玉, 徐爱国. 基于机器学习方法的宁夏南部土壤质地空间分布研究. 中国农业科学, 2022, 55(15): 2961-2972. doi:10.3864/j.issn.0578-1752.2022.15.008.
SHEN Z, ZHANG R L, LONG H Y, XU A G. Research on spatial distribution of soil texture in southern Ningxia based on machine learning. Scientia Agricultura Sinica, 2022, 55(15): 2961-2972. doi:10.3864/j.issn.0578-1752.2022.15.008. (in Chinese)
[47]
WEISS M, JACOB F, DUVEILLER G. Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment, 2020, 236: 111402.
[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] LI WenHu, LI HaiFeng, DU YuPeng, DING YuLan, LUO YiNuo, LI YuKe, SHE WenTing, ZHANG Feng, TENG Yu, ZHANG SiQi, HUANG Cui, LI XiaoHan, LIU JinShan, WANG ZhaoHui. Regional Differences in Wheat Zinc Uptake and Translocation Responses to Soil Zinc Fertilization [J]. Scientia Agricultura Sinica, 2026, 59(5): 1034-1047.
[3] JIAO WenJuan, HE WanLong, GENG HongWei, BAI Bin, LI JianFeng, CHENG YuKun. Stripe Rust Resistance Evaluation and Molecular Characterization of Yr Genes for 155 Spring Wheat Varieties (Lines) [J]. Scientia Agricultura Sinica, 2026, 59(5): 937-950.
[4] CUI ShiYou, CHEN PengJun, MIAO YuanQing, HAN JiJun, SHEN JunMing. Development and Field Evaluation of Glyphosate-Resistant Wheat Germplasm Generated Through EMS Mutagenesis [J]. Scientia Agricultura Sinica, 2026, 59(4): 723-733.
[5] 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.
[6] 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.
[7] WANG YongSheng, NIU Li, WANG ChangJie, MA LiHua, LIAN XiaoXiao, MENG YaXiong, MA XiaoLe, YAO LiRong, ZHANG Hong, YANG Ke, LI BaoChun, WANG HuaJun, SI ErJing, WANG JunCheng. Genome-Wide Association Study and Candidate Gene Identification for Thousand Grain Weight in Winter Wheat [J]. Scientia Agricultura Sinica, 2026, 59(3): 499-514.
[8] LI XinYi, LI JiaNing, YANG WenPing, XIA Qing, HUO YingRui, HAO ShiHang, HUANG TingMiao, REN YongKang, CHEN Jie, GAO ZhiQiang, YANG ZhenPing. Effects of Post-Anthesis Foliar Zinc Application on Zinc Nutrition in Colored-Grain Wheat [J]. Scientia Agricultura Sinica, 2026, 59(3): 515-527.
[9] 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.
[10] ZHANG ZhiYong, TAN ShiChao, XIONG ShuPing, MA XinMing, WEI YiHao, WANG XiaoChun. Effects of Annual Water and Nitrogen Optimization on Yield and Nitrogen Migration of Wheat-Maize Rotation System in Irrigation Area of Northern Henan [J]. Scientia Agricultura Sinica, 2026, 59(2): 336-353.
[11] 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.
[12] LU Hao, ZHANG MingLong, HAN Mei, YAN QingBiao, LI ZhengPeng, YIN Wen, FAN ZhiLong, HU FaLong, CHAI Qiang. Green Manure Returning via Sheep Digest with Nitrogen Fertilizer Reduction are Beneficial to Improve Wheat Yield and Soil Quality at Qinghai-Tibet Plateau [J]. Scientia Agricultura Sinica, 2026, 59(1): 147-160.
[13] YE MeiJin, CHEN JiaTing, ZHOU JieGuang, YIN Li, HU XinRong, LAN YuXin, CHEN Bin, SU LongXing, LIU JiaJun, LIU TianChao, LI XiaoYu, MA Jian. Identification, Validation and Genetic Effect Analysis of Major QTL for Spike Density in Wheat [J]. Scientia Agricultura Sinica, 2026, 59(1): 17-28.
[14] LI YunLi, DIAO DengChao, LIU YaRui, SUN YuChen, MENG XiangYu, WU ChenFang, WANG Yu, WU JianHui, LI ChunLian, ZENG QingDong, HAN DeJun, ZHENG WeiJun. Genome-Wide Association Study of Heat Tolerance at Seedling Stage in A Wheat Natural Population [J]. Scientia Agricultura Sinica, 2025, 58(9): 1663-1683.
[15] 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.
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