Deep learning-based phenology extraction and crop classification in arid oasis using Sentinel-2 time series

Chunli WANG; Jianan CHI; Xiao ZHANG; Nannan ZHANG

doi:10.1631/jzus.B2500403

Your Location：

Home >

Browse articles >

Deep learning-based phenology extraction and crop classification in arid oasis using Sentinel-2 time series

Scan QR Code

Alert me when the article has been cited

Submit

Research Article | Updated：2026-05-15

Deep learning-based phenology extraction and crop classification in arid oasis using Sentinel-2 time series
Deep learning-based phenology extraction and crop classification in arid oasis using Sentinel-2 time series
基于深度学习的干旱绿洲地区Sentinel-2时间序列物候提取与作物分类
Journal of Zhejiang University-SCIENCE B Vol. 27, Issue 5, Pages: 537-560(2026)
Affiliations：

1.College of Information Engineering, Tarim University, Alar 843300, China
2.Key Laboratory of Oasis Agriculture in Tarim, Tarim University, Alar 843300, China
Funds：
DOI：10.1631/jzus.B2500403
CLC：
CSTR：
Received：11 July 2025，

Revised：2025-12-03，

Online First：15 April 2026，

Published：15 May 2026
Accepted：

王春莉,池佳男,张晓等.基于深度学习的干旱绿洲地区Sentinel-2时间序列物候提取与作物分类[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2026,27(05):537-560.

Chunli WANG, Jianan CHI, Xiao ZHANG, et al. Deep learning-based phenology extraction and crop classification in arid oasis using Sentinel-2 time series[J]. Journal of Zhejiang University-SCIENCE B, 2026, 27(5): 537-560.
王春莉,池佳男,张晓等.基于深度学习的干旱绿洲地区Sentinel-2时间序列物候提取与作物分类[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2026,27(05):537-560. DOI： 10.1631/jzus.B2500403.

Chunli WANG, Jianan CHI, Xiao ZHANG, et al. Deep learning-based phenology extraction and crop classification in arid oasis using Sentinel-2 time series[J]. Journal of Zhejiang University-SCIENCE B, 2026, 27(5): 537-560. DOI： 10.1631/jzus.B2500403.

摘要

多时相遥感数据在大规模作物物候识别与分类中应用日益广泛，尤其适用于种植结构复杂的干旱绿洲农业区域的精细化管理。本研究构建了一个融合Sentinel-2多时相影像与归一化植被指数（NDVI）时间序列的深度学习框架，用于对中国新疆图木舒克市的棉花、冬枣和油莎豆进行分类制图。采用最小冗余最大相关（mRMR）算法进行光谱与植被指数特征选择，结合Savitzky-Golay（S-G）滤波与双逻辑函数拟合，自动提取关键物候参数（生长季开始（SOS）、生长季峰值（POS）和生长季结束（EOS）），显著提升了物候特征提取的精度。通过融合多时相Sentinel-2数据与多尺度特征融合策略，系统比较了五种分类模型（MLP、ResNet-18、ConvLSTM、Transformer和RFC），结果表明高分辨率空间细节能显著增强复杂环境下作物边界划定与分类一致性。通过多尺度窗口分析进一步优化Transformer的空间表征能力，发现1×1+3×3+5×5卷积窗口在精度与计算效率间达到最佳平衡。在未参与训练区域的独立验证表明模型具有良好的泛化能力，三种作物（冬枣、棉花和油莎豆）的F1分数分别达到94.37%、87.75%和86.35%。综上所述，本研究验证了Sentinel-2时间序列数据与深度神经网络在多作物环境中具有高精度识别潜力，且能够实现作物分布的精确空间制图，为干旱绿洲地区的智慧农业决策提供方法支持。

Abstract

Multi-temporal remote sensing data in large-scale crop phenology identification and classification have become increasingly utilized

particularly for precision management in arid oasis agricultural regions with complex cropping systems. In this study

we developed a deep learning framework integrating Sentinel-2 multi-temporal imagery and normalized difference vegetation index (NDVI) time series for mapping cotton

winter jujube

and tiger nut crops in Tumushuke City

Xinjiang Uygur Autonomous Region

China. We employed the minimum redundancy maximum relevance (mRMR) algorithm for spectral and vegetation index feature selection

followed by Savitzky-Golay (S-G) filtering and double logistic function fitting

to automatically extract the key phenological parameters (start of season (SOS)

peak of season (POS)

and end of season (EOS))

significantly improving phenological feature extraction accuracy. By incorporating multi-temporal Sentinel-2 data and a multi-scale feature fusion approach

we could systematically compare five classification models (multi-layer perceptron (MLP)

residual network-18 (ResNet-18)

convolutional long short-term memory (ConvLSTM)

Transformer

and random forest classifier (RFC))

demonstrating that high-resolution spatial details substantially enhance crop boundary delineation and classification consistency in complex environments. Further optimization of Transformer’s spatial representation through multi-scale window analysis revealed that the use of 1×1+3×3+5×5 convolutional windows achieves an optimal balance between accuracy and computational efficiency. Independent validation on unseen areas confirmed robust model transferability

with F1 scores of 94.37%

87.75%

and 86.35% for the three crops (winter jujube

cotton

and tiger nut)

respectively. This study validates the high-precision identification potential of Sentinel-2 temporal data and deep neural networks for multi-crop environments

enabling the precise spatial mapping of crop distributions and providing methodological support for smart agricultural decision-making in arid oasis regions.

关键词

Keywords

references

Cao J , Ma SP , Yuan WH , et al. , 2022 . Characteristics of diurnal variations of warm-season precipitation over Xinjiang Province in China . Atmos Oceanic Sci Lett , 15 ( 2 ): 100113 . https://doi.org/10.1016/j.aosl.2021.100113 https://doi.org/10.1016/j.aosl.2021.100113

Chaudhary A , Kolhe S , Kamal R , 2016 . An improved random forest classifier for multi-class classification . Inf Process Agric , 3 ( 4 ): 215 - 222 . https://doi.org/10.1016/j.inpa.2016.08.002 https://doi.org/10.1016/j.inpa.2016.08.002

Chen XM , Ding YL , Zheng XM , et al. , 2024 . Improved estimation of non-photosynthetic vegetation cover using a novel multispectral slope difference index with soil information, Sentinel-1 data, and machine learning . Ecol Inform , 84 : 102930 . https://doi.org/10.1016/j.ecoinf.2024.102930 https://doi.org/10.1016/j.ecoinf.2024.102930

Cheng JR , Li H , Li DB , et al. , 2022 . A survey on image semantic segmentation using deep learning techniques . Comput Mater Contin , 74 ( 1 ): 1941 - 1957 . https://doi.org/10.32604/cmc.2023.032757 https://doi.org/10.32604/cmc.2023.032757

de la Guardia L , de Miranda JH , dos Santos Luciano AC , 2024 . Assessment of irrigation water use for dry beans in center pivots using ERA5 Land climate variables and Sentinel 2 NDVI time series in the Brazilian Cerrado . Agric Water Manag , 305 : 109128 . https://doi.org/10.1016/j.agwat.2024.109128 https://doi.org/10.1016/j.agwat.2024.109128

Du RQ , Xiang YZ , Chen JY , et al. , 2024 . The daily soil water content monitoring of cropland in irrigation area using Sentinel-2/3 spatio-temporal fusion and machine learning . Int J Appl Earth Obs Geoinf , 132 : 104081 . https://doi.org/10.1016/j.jag.2024.104081 https://doi.org/10.1016/j.jag.2024.104081

Faqeerzada MA , Kim H , Kim MS , et al. , 2025 . Hyperspectral imaging VIS-NIR and SWIR fusion for improved drought-stress identification of strawberry plants . Comput Electron Agric , 237 : 110702 . https://doi.org/10.1016/j.compag.2025.110702 https://doi.org/10.1016/j.compag.2025.110702

Farbo A , Sarvia F , de Petris S , et al. , 2024 . Forecasting corn NDVI through AI-based approaches using Sentinel 2 image time series . ISPRS J Photogramm Remote Sens , 211 : 244 - 261 . https://doi.org/10.1016/j.isprsjprs.2024.04.011 https://doi.org/10.1016/j.isprsjprs.2024.04.011

Fern RR , Foxley EA , Bruno A , et al. , 2018 . Suitability of NDVI and OSAVI as estimators of green biomass and coverage in a semi-arid rangeland . Ecol Indic , 94 ( Pt 1 ): 16 - 21 . https://doi.org/10.1016/j.ecolind.2018.06.029 https://doi.org/10.1016/j.ecolind.2018.06.029

Gámez AL , Segarra J , Vatter T , et al. , 2025 . Alfalfa yield estimation using the combination of Sentinel-2 and meteorological data . Field Crops Res , 326 : 109857 . https://doi.org/10.1016/j.fcr.2025.109857 https://doi.org/10.1016/j.fcr.2025.109857

Gao XY , Chi H , Huang JL , et al. , 2024 . Comparison of cloud-mask algorithms and machine-learning methods using Sentinel-2 imagery for mapping paddy rice in Jianghan Plain . Remote Sens , 16 ( 7 ): 1305 . https://doi.org/10.3390/rs16071305 https://doi.org/10.3390/rs16071305

Gao Y , Sun ZZ , Hu D , et al. , 2025 . GMOPNet: a GAN-MLP two-stage network for optical properties measurement of kiwifruit and peaches with spatial frequency domain imaging . Food Chem , 465 ( Pt 1 ): 141944 . https://doi.org/10.1016/j.foodchem.2024.141944 https://doi.org/10.1016/j.foodchem.2024.141944

Ghilardi F , de Petris S , Torti V , et al. , 2025 . A possible role of NDVI time series from Landsat Mission to characterize lemurs habitats degradation in Madagascar . Sci Total Environ , 974 : 179243 . https://doi.org/10.1016/j.scitotenv.2025.179243 https://doi.org/10.1016/j.scitotenv.2025.179243

Guerri MF , Distante C , Spagnolo P , et al. , 2025 . Boosting hyperspectral image classification with Gate-Shift-Fuse mechanisms in a novel CNN-Transformer approach . Comput Electron Agric , 237 : 110489 . https://doi.org/10.1016/j.compag.2025.110489 https://doi.org/10.1016/j.compag.2025.110489

Han W , Chen SH , Xiao SL , et al. , 2025 . Large-scale tobacco identification via a very-high-resolution unmanned aerial vehicle benchmark and a ConvFlow Transformer . Int J Appl Earth Obs Geoinf , 139 : 104549 . https://doi.org/10.1016/j.jag.2025.104549 https://doi.org/10.1016/j.jag.2025.104549

Haseeb M , Tahir Z , Mahmood SA , et al. , 2025 . Winter wheat yield prediction using linear and nonlinear machine learning algorithms based on climatological and remote sensing data . Inf Process Agric , 12 ( 4 ): 431 - 444 . https://doi.org/10.1016/j.inpa.2025.02.004 https://doi.org/10.1016/j.inpa.2025.02.004

He JQ , Jia YL , Li Y , et al. , 2025 . Regional-scale precision mapping of cotton suitability using UAV and satellite data in arid environments . Agric Water Manag , 307 : 109215 . https://doi.org/10.1016/j.agwat.2024.109215 https://doi.org/10.1016/j.agwat.2024.109215

Huang XZ , Wang H , Li XB , 2024 . A multi-scale semantic feature fusion method for remote sensing crop classification . Comput Electron Agric , 224 : 109185 . https://doi.org/10.1016/j.compag.2024.109185 https://doi.org/10.1016/j.compag.2024.109185

Ismaili M , Krimissa S , Namous M , et al. , 2024 . Mapping soil suitability using phenological information derived from MODIS time series data in a semi-arid region: a case study of Khouribga, Morocco . Heliyon , 10 ( 2 ): e24101 . https://doi.org/10.1016/j.heliyon.2024.e24101 https://doi.org/10.1016/j.heliyon.2024.e24101

Ji YS , Liu ZH , Liu R , et al. , 2024 . High-throughput phenotypic traits estimation of faba bean based on machine learning and drone-based multimodal data . Comput Electron Agric , 227 ( Pt 2 ): 109584 . https://doi.org/10.1016/j.compag.2024.109584 https://doi.org/10.1016/j.compag.2024.109584

Jia TY , Shamseldin AY , Liu TX , et al. , 2025 . Soil moisture inversion method for semi-arid regions using multi-temporal Sentinel-1 and Sentinel-2 data . J Hydrol Reg , 661 : 133603 . https://doi.org/10.1016/j.jhydrol.2025.133603 https://doi.org/10.1016/j.jhydrol.2025.133603

Jiang DG , Shi XY , Liang Y , et al. , 2024 . Feature extraction technique based on Shapley value method and improved mRMR algorithm . Measurement , 237 : 115190 . https://doi.org/10.1016/j.measurement.2024.115190 https://doi.org/10.1016/j.measurement.2024.115190

Khankeshizadeh E , Tahermanesh S , Mohsenifar A , et al. , 2024 . FBA-DPAttResU-Net: forest burned area detection using a novel end-to-end dual-path attention residual-based U-Net from post-fire Sentinel-1 and Sentinel-2 images . Ecol Indic , 167 : 112589 . https://doi.org/10.1016/j.ecolind.2024.112589 https://doi.org/10.1016/j.ecolind.2024.112589

Kordi F , Yousefi H , 2022 . Crop classification based on phenology information by using time series of optical and synthetic-aperture radar images . Remote Sens Appl Soc Environ , 27 : 100812 . https://doi.org/10.1016/j.rsase.2022.100812 https://doi.org/10.1016/j.rsase.2022.100812

Li CL , Guo SL , Cui Z , et al. , 2025 . Flow simulation based on MISO and LSTM models in the Yangtze River-Dongting Lake System . J Hydrol Reg Stud , 60 : 102469 . https://doi.org/10.1016/j.ejrh.2025.102469 https://doi.org/10.1016/j.ejrh.2025.102469

Li GG , Zhang H , Lyu T , et al. , 2024 . Regional significant wave height forecast in the East China Sea based on the Self-Attention ConvLSTM with SWAN model . Ocean Eng , 312 ( Pt 1 ): 119064 . https://doi.org/10.1016/j.oceaneng.2024.119064 https://doi.org/10.1016/j.oceaneng.2024.119064

Li WC , Chen R , Ma D , et al. , 2023 . Tracking autumn photosynthetic phenology on Tibetan plateau grassland with the green–red vegetation index . Agric For Meteorol , 339 : 109573 . https://doi.org/10.1016/j.agrformet.2023.109573 https://doi.org/10.1016/j.agrformet.2023.109573

Liu Y , Yang FQ , Yue JB , et al. , 2024 . Crop canopy volume weighted by color parameters from UAV-based RGB imagery to estimate above-ground biomass of potatoes . Comput Electron Agric , 227 ( Pt 2 ): 109678 . https://doi.org/10.1016/j.compag.2024.109678 https://doi.org/10.1016/j.compag.2024.109678

Lu DN , Xu LL , Zhou J , et al. , 2025 . 3DLST: 3D Learnable Supertoken Transformer for LiDAR point cloud scene segmentation . Int J Appl Earth Obs Geoinf , 140 : 104572 . https://doi.org/10.1016/j.jag.2025.104572 https://doi.org/10.1016/j.jag.2025.104572

Marcone A , Impollonia G , Croci M , et al. , 2024 . Estimation of above ground biomass, biophysical and quality parameters of spinach ( Spinacia oleracea L.) using Sentinel-2 to support the supply chain . Sci Hortic , 325 : 112641 . https://doi.org/10.1016/j.scienta.2023.112641 https://doi.org/10.1016/j.scienta.2023.112641

Marin DB , Ferraz GAES , Santana LS , et al. , 2021 . Detecting coffee leaf rust with UAV-based vegetation indices and decision tree machine learning models . Comput Electron Agric , 190 : 106476 . https://doi.org/10.1016/j.compag.2021.106476 https://doi.org/10.1016/j.compag.2021.106476

Marino S , 2023 . Understanding the spatio-temporal behaviour of the sunflower crop for subfield areas delineation using Sentinel-2 NDVI time-series images in an organic farming system . Heliyon , 9 ( 9 ): e19507 . https://doi.org/10.1016/j.heliyon.2023.e19507 https://doi.org/10.1016/j.heliyon.2023.e19507

Martínez-Movilla A , Rodríguez-Somoza JL , Román M , et al. , 2024 . Rapid diagnosis of the geospatial distribution of intertidal macroalgae using large-scale UAVs . Ecol Inform , 83 : 102845 . https://doi.org/10.1016/j.ecoinf.2024.102845 https://doi.org/10.1016/j.ecoinf.2024.102845

Medelytė S , Rzhanov Y , Šiaulys A , et al. , 2025 . Evaluating textural descriptors for automated image classification of stony reefs in turbid temperate waters . Ecol Inform , 90 : 103236 . https://doi.org/10.1016/j.ecoinf.2025.103236 https://doi.org/10.1016/j.ecoinf.2025.103236

Mendes J , Lima J , Costa L , et al. , 2025 . Impact of hyper-parameter tuning on CNN accuracy in agricultural image classification . Smart Agric Technol , 11 : 101016 . https://doi.org/10.1016/j.atech.2025.101016 https://doi.org/10.1016/j.atech.2025.101016

Mishra K , Tiwari HL , Poonia V , 2025 . An integrated approach of machine learning methods coupled with cellular automaton for monitoring and forecasting of land use and land cover . J Arid Environ , 226 : 105293 . https://doi.org/10.1016/j.jaridenv.2024.105293 https://doi.org/10.1016/j.jaridenv.2024.105293

Naqvi SMZA , Awais M , Khan FS , et al. , 2021 . Unmanned air vehicle based high resolution imagery for chlorophyll estimation using spectrally modified vegetation indices in vertical hierarchy of citrus grove . Remote Sens Appl Soc Environ , 23 : 100596 . https://doi.org/10.1016/j.rsase.2021.100596 https://doi.org/10.1016/j.rsase.2021.100596

Newete SW , Abutaleb K , Chirima GJ , et al. , 2024 . Phenology-based winter wheat classification for crop growth monitoring using multi-temporal Sentinel-2 satellite data . Egypt J Remote Sens Space Sci , 27 ( 4 ): 695 - 704 . https://doi.org/10.1016/j.ejrs.2024.10.001 https://doi.org/10.1016/j.ejrs.2024.10.001

Nivedita V , Begum SS , Aldehim G , et al. , 2024 . Plastic debris detection along coastal waters using Sentinel-2 satellite data and machine learning techniques . Mar Pollut Bull , 209 : 117106 . https://doi.org/10.1016/j.marpolbul.2024.117106 https://doi.org/10.1016/j.marpolbul.2024.117106

Pandey GK , Mittal K , Bansal A , et al. , 2025 . Fire detection with ResNet 18: comparative analysis across different hyperparameters . Procedia Comput Sci , 260 : 708 - 716 . https://doi.org/10.1016/j.procs.2025.03.250 https://doi.org/10.1016/j.procs.2025.03.250

Peng KY , Liu YM , Zhang KY , et al. , 2025 . Regional NDVI reconstruction based on tree-ring width of Pinus massoniana Lamb. in the north-south transition zone of China . Dendrochronologia , 92 : 126373 . https://doi.org/10.1016/j.dendro.2025.126373 https://doi.org/10.1016/j.dendro.2025.126373

Qiu ZQ , Liu D , Yan NX , et al. , 2024 . Improving the observations of suspended sediment concentrations in rivers from Landsat to Sentinel-2 imagery . Int J Appl Earth Obs Geoinf , 134 : 104209 . https://doi.org/10.1016/j.jag.2024.104209 https://doi.org/10.1016/j.jag.2024.104209

Ren K , Zhang DW , Wan MJ , et al. , 2021 . An infrared and visible image fusion method based on improved DenseNet and mRMR-ZCA . Infrared Phys Technol , 115 : 103707 . https://doi.org/10.1016/j.infrared.2021.103707 https://doi.org/10.1016/j.infrared.2021.103707

Ren ZQ , Tian FM , Wang SQ , et al. , 2025 . Research on maize leaves surface action potential recognition method based on ResNet-18SE . Smart Agric Technol , 10 : 100819 . https://doi.org/10.1016/j.atech.2025.100819 https://doi.org/10.1016/j.atech.2025.100819

Sarkar A , Maity PP , Ray M , et al. , 2023 . Inclusion of fractal dimension in four machine learning algorithms improves the prediction accuracy of mean weight diameter of soil . Ecol Inform , 74 : 101959 . https://doi.org/10.1016/j.ecoinf.2022.101959 https://doi.org/10.1016/j.ecoinf.2022.101959

Shojaeezadeh SA , Elnashar A , David Weber TK , 2025 . A novel fusion of Sentinel-1 and Sentinel-2 with climate data for crop phenology estimation using machine learning . Sci Remote Sens , 11 : 100227 . https://doi.org/10.1016/j.srs.2025.100227 https://doi.org/10.1016/j.srs.2025.100227

Tesfaye AA , Osgood DE , Aweke BG , 2025 . Application of a novel vegetation condition index using MODIS EVI for structuring crop index insurance under a smallholder system . Environ Sustain Indic , 26 : 100696 . https://doi.org/10.1016/j.indic.2025.100696 https://doi.org/10.1016/j.indic.2025.100696

Testa S , Soudani K , Boschetti L , et al. , 2018 . MODIS-derived EVI, NDVI and WDRVI time series to estimate phenological metrics in French deciduous forests . Int J Appl Earth Obs Geoinf , 64 : 132 - 144 . https://doi.org/10.1016/j.jag.2017.08.006 https://doi.org/10.1016/j.jag.2017.08.006

Tran KH , Zhang XY , Zhang HK , et al. , 2025 . A Transformer-based model for detecting land surface phenology from the irregular harmonized Landsat and Sentinel-2 time series across the United States . Remote Sens Environ , 320 : 114656 . https://doi.org/10.1016/j.rse.2025.114656 https://doi.org/10.1016/j.rse.2025.114656

Tufail R , Tassinari P , Torreggiani D , 2025 . Assessing feature extraction, selection, and classification combinations for crop mapping using Sentinel-2 time series: a case study in northern Italy . Remote Sens Appl Soc Environ , 38 : 101525 . https://doi.org/10.1016/j.rsase.2025.101525 https://doi.org/10.1016/j.rsase.2025.101525

Wang C , Zhang XY , Wang WJ , et al. , 2024 . Understanding the potentials of early-season crop type mapping by using Landsat-8, Sentinel-1/2, and GF-1/6 data . Comput Electron Agric , 224 : 109239 . https://doi.org/10.1016/j.compag.2024.109239 https://doi.org/10.1016/j.compag.2024.109239

Wang LY , Dai LM , Sun L , 2025 . ConvLSTM-based spatiotemporal and temporal processing models for chaotic vibration prediction of a microbeam . Commun Nonlinear Sci Numer Simul , 140 ( Pt 2 ): 108411 . https://doi.org/10.1016/j.cnsns.2024.108411 https://doi.org/10.1016/j.cnsns.2024.108411

Xu LC , Su XY , Wang KT , et al. , 2025 . Enhancing canopy nitrogen estimation in Torreya grandis based on advanced SLIC-EVI and HMT-seCNN methods using hyperspectral UAV data . Comput Electron Agric , 231 : 109977 . https://doi.org/10.1016/j.compag.2025.109977 https://doi.org/10.1016/j.compag.2025.109977

Yu DF , Ren LR , Chen C , et al. , 2025 . An AttSDNet model for multi-scale feature perception enhanced remote sensing classification of coastal salt-marsh wetlands . Mar Environ Res , 204 : 106899 . https://doi.org/10.1016/j.marenvres.2024.106899 https://doi.org/10.1016/j.marenvres.2024.106899

Zhai LL , Zan M , Ye M , et al. , 2025 . Time-series forest age estimation in Xinjiang based on forest disturbance and recovery detection . Ecol Indic , 170 : 113043 . https://doi.org/10.1016/j.ecolind.2024.113043 https://doi.org/10.1016/j.ecolind.2024.113043

Zheng B , Liu X , Wu Y , 2022 . Evaluation of urban human settlement environment in corps based on the entropy method: a case study of Tumushuke City . Proceedings of the 3rd International Conference on Humanities , Arts , and Social Sciences (HASS 2022 ), New York , USA , p. 108 - 118 . https://doi.org/10.54691/bcpssh.v18i.943 https://doi.org/10.54691/bcpssh.v18i.943

Zhou JP , Gu XH , Liu CL , et al. , 2024 . A new approach to extract the upright maize straw from Sentinel-2 satellite imagery using new straw indices . Comput Electron Agric , 216 : 108506 . https://doi.org/10.1016/j.compag.2023.108506 https://doi.org/10.1016/j.compag.2023.108506

Zhou ZJ , Plauborg F , Thomsen AG , et al. , 2017 . A RVI/LAI-reference curve to detect N stress and guide N fertigation using combined information from spectral reflectance and leaf area measurements in potato . Eur J Agron , 87 : 1 - 7 . https://doi.org/10.1016/j.eja.2017.04.002 https://doi.org/10.1016/j.eja.2017.04.002

Views

Downloads

CSCD

Tools

Publicity Resources

Improved lightweight convolutional neural network models for the detection and evaluation of Fusarium head blight in wheat

RCTUnet: a deep learning model for crop-residue-soil image segmentation and crop residue cover extraction

Recent advances in antibody optimization based on deep learning methods

Esculetin attenuates migraine-like pain via CGRP suppression and meningeal mast cell modulation in rat models

Regenerative potential of Schneiderian membrane-derived mesenchymal stem cells in sinus floor elevation model and calvarial defect model

Related Author

Wang ZHANG

Yi REN

Zidi GUO

Han LI

Man ZHANG

Jie LIU

Ruicheng QIU

Yang LIU

Related Institution

Key Laboratory of Smart Agriculture System Integration Research, Ministry of Education, China Agricultural University

College of Information and Electrical Engineering, China Agricultural University

Yantai Academy of Agricultural Sciences

College of Information and Management Science, Henan Agricultural University

Institute of Quantitative Remote Sensing and Smart Agriculture, Henan Polytechnic University

Address：Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou, China Postal code：310027
Tel：+86-571-87952783 Email：cjzhang@zju.edu.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰