{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T17:01:53Z","timestamp":1778864513470,"version":"3.51.4"},"reference-count":51,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T00:00:00Z","timestamp":1724803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2023YFD1900705"],"award-info":[{"award-number":["2023YFD1900705"]}]},{"name":"National Key R&amp;D Program of China","award":["IFI2024-01"],"award-info":[{"award-number":["IFI2024-01"]}]},{"name":"National Key R&amp;D Program of China","award":["242300420221"],"award-info":[{"award-number":["242300420221"]}]},{"name":"National Key R&amp;D Program of China","award":["NSFRF240101"],"award-info":[{"award-number":["NSFRF240101"]}]},{"name":"National Key R&amp;D Program of China","award":["221100110700"],"award-info":[{"award-number":["221100110700"]}]},{"name":"Central Public-Interest Scientific Institution Basal Research Fund","award":["2023YFD1900705"],"award-info":[{"award-number":["2023YFD1900705"]}]},{"name":"Central Public-Interest Scientific Institution Basal Research Fund","award":["IFI2024-01"],"award-info":[{"award-number":["IFI2024-01"]}]},{"name":"Central Public-Interest Scientific Institution Basal Research Fund","award":["242300420221"],"award-info":[{"award-number":["242300420221"]}]},{"name":"Central Public-Interest Scientific Institution Basal Research Fund","award":["NSFRF240101"],"award-info":[{"award-number":["NSFRF240101"]}]},{"name":"Central Public-Interest Scientific Institution Basal Research Fund","award":["221100110700"],"award-info":[{"award-number":["221100110700"]}]},{"name":"Fundamental Research Funds for the Universities of Henan Province","award":["2023YFD1900705"],"award-info":[{"award-number":["2023YFD1900705"]}]},{"name":"Fundamental Research Funds for the Universities of Henan Province","award":["IFI2024-01"],"award-info":[{"award-number":["IFI2024-01"]}]},{"name":"Fundamental Research Funds for the Universities of Henan Province","award":["242300420221"],"award-info":[{"award-number":["242300420221"]}]},{"name":"Fundamental Research Funds for the Universities of Henan Province","award":["NSFRF240101"],"award-info":[{"award-number":["NSFRF240101"]}]},{"name":"Fundamental Research Funds for the Universities of Henan Province","award":["221100110700"],"award-info":[{"award-number":["221100110700"]}]},{"name":"National Major Scientific Research Achievement Cultivation Fund of Henan Polytechnic University","award":["2023YFD1900705"],"award-info":[{"award-number":["2023YFD1900705"]}]},{"name":"National Major Scientific Research Achievement Cultivation Fund of Henan Polytechnic University","award":["IFI2024-01"],"award-info":[{"award-number":["IFI2024-01"]}]},{"name":"National Major Scientific Research Achievement Cultivation Fund of Henan Polytechnic University","award":["242300420221"],"award-info":[{"award-number":["242300420221"]}]},{"name":"National Major Scientific Research Achievement Cultivation Fund of Henan Polytechnic University","award":["NSFRF240101"],"award-info":[{"award-number":["NSFRF240101"]}]},{"name":"National Major Scientific Research Achievement Cultivation Fund of Henan Polytechnic University","award":["221100110700"],"award-info":[{"award-number":["221100110700"]}]},{"name":"Key Grant Technology Project of Henan","award":["2023YFD1900705"],"award-info":[{"award-number":["2023YFD1900705"]}]},{"name":"Key Grant Technology Project of Henan","award":["IFI2024-01"],"award-info":[{"award-number":["IFI2024-01"]}]},{"name":"Key Grant Technology Project of Henan","award":["242300420221"],"award-info":[{"award-number":["242300420221"]}]},{"name":"Key Grant Technology Project of Henan","award":["NSFRF240101"],"award-info":[{"award-number":["NSFRF240101"]}]},{"name":"Key Grant Technology Project of Henan","award":["221100110700"],"award-info":[{"award-number":["221100110700"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurately assessing maize crop height (CH) and aboveground biomass (AGB) is crucial for understanding crop growth and light-use efficiency. Unmanned aerial vehicle (UAV) remote sensing, with its flexibility and high spatiotemporal resolution, has been widely applied in crop phenotyping studies. Traditional canopy height models (CHMs) are significantly influenced by image resolution and meteorological factors. In contrast, the accumulated incremental height (AIH) extracted from point cloud data offers a more accurate estimation of CH. In this study, vegetation indices and structural features were extracted from optical imagery, nadir and oblique photography, and LiDAR point cloud data. Optuna-optimized models, including random forest regression (RFR), light gradient boosting machine (LightGBM), gradient boosting decision tree (GBDT), and support vector regression (SVR), were employed to estimate maize AGB. Results show that AIH99 has higher accuracy in estimating CH. LiDAR demonstrated the highest accuracy, while oblique photography and nadir photography point clouds were slightly less accurate. Fusion of multi-source data achieved higher estimation accuracy than single-sensor data. Embedding structural features can mitigate spectral saturation, with R2 ranging from 0.704 to 0.939 and RMSE ranging from 0.338 to 1.899 t\/hm2. During the entire growth cycle, the R2 for LightGBM and RFR were 0.887 and 0.878, with an RMSE of 1.75 and 1.76 t\/hm2. LightGBM and RFR also performed well across different growth stages, while SVR showed the poorest performance. As the amount of nitrogen application gradually decreases, the accumulation and accumulation rate of AGB also gradually decrease. This high-throughput crop-phenotyping analysis method offers advantages, such as speed and high accuracy, providing valuable references for precision agriculture management in maize fields.<\/jats:p>","DOI":"10.3390\/rs16173176","type":"journal-article","created":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T03:57:06Z","timestamp":1724817426000},"page":"3176","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Estimating Maize Crop Height and Aboveground Biomass Using Multi-Source Unmanned Aerial Vehicle Remote Sensing and Optuna-Optimized Ensemble Learning Algorithms"],"prefix":"10.3390","volume":"16","author":[{"given":"Yafeng","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China"},{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changchun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Cheng","sequence":"additional","affiliation":[{"name":"Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuyi","family":"Duan","sequence":"additional","affiliation":[{"name":"Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiguang","family":"Zhai","sequence":"additional","affiliation":[{"name":"Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongpeng","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bohan","family":"Mao","sequence":"additional","affiliation":[{"name":"Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Ding","sequence":"additional","affiliation":[{"name":"Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohui","family":"Kuang","sequence":"additional","affiliation":[{"name":"Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2847-0042","authenticated-orcid":false,"given":"Zhen","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Holman, F., Riche, A., Michalski, A., Castle, M., Wooster, M., and Hawkesford, M. (2016). High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing. Remote Sens., 8.","DOI":"10.3390\/rs8121031"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"108491","DOI":"10.1016\/j.fcr.2022.108491","article-title":"Estimating the Maize Above-Ground Biomass by Constructing the Tridimensional Concept Model Based on UAV-Based Digital and Multi-Spectral Images","volume":"282","author":"Shu","year":"2022","journal-title":"Field Crops Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"108144","DOI":"10.1016\/j.compag.2023.108144","article-title":"High-Quality Images and Data Augmentation Based on Inverse Projection Transformation Significantly Improve the Estimation Accuracy of Biomass and Leaf Area Index","volume":"212","author":"Che","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"108860","DOI":"10.1016\/j.fcr.2023.108860","article-title":"Combining UAV and Sentinel-2 Satellite Multi-Spectral Images to Diagnose Crop Growth and N Status in Winter Wheat at the County Scale","volume":"294","author":"Jiang","year":"2023","journal-title":"Field Crops Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.isprsjprs.2019.02.022","article-title":"Estimate of Winter-Wheat above-Ground Biomass Based on UAV Ultrahigh-Ground-Resolution Image Textures and Vegetation Indices","volume":"150","author":"Yue","year":"2019","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"112967","DOI":"10.1016\/j.rse.2022.112967","article-title":"Comparison and Transferability of Thermal, Temporal and Phenological-Based in-Season Predictions of above-Ground Biomass in Wheat Crops from Proximal Crop Reflectance Data","volume":"273","author":"Li","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"147335","DOI":"10.1016\/j.scitotenv.2021.147335","article-title":"Estimating the Aboveground Biomass of Coniferous Forest in Northeast China Using Spectral Variables, Land Surface Temperature and Soil Moisture","volume":"785","author":"Jiang","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_8","first-page":"313","article-title":"Estimating Tree Height and Biomass of a Poplar Plantation with Image-Based UAV Technology","volume":"3","author":"Pena","year":"2018","journal-title":"Aims Agric. Food"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote Sensing for Agricultural Applications: A Meta-Review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.isprsjprs.2019.03.003","article-title":"Vegetation Index Weighted Canopy Volume Model (CVMVI) for Soybean Biomass Estimation from Unmanned Aerial System-Based RGB Imagery","volume":"151","author":"Maimaitijiang","year":"2019","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Itakura, K., and Hosoi, F. (2019). Estimation of Leaf Inclination Angle in Three-Dimensional Plant Images Obtained from Lidar. Remote Sens., 11.","DOI":"10.3390\/rs11030344"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"113968","DOI":"10.1016\/j.rse.2023.113968","article-title":"Deep Point Cloud Regression for Above-Ground Forest Biomass Estimation from Airborne LiDAR","volume":"302","author":"Oehmcke","year":"2024","journal-title":"Remote Sens. Environ."},{"key":"ref_13","first-page":"166","article-title":"Comparative analysis of extraction algorithms for crown volume and surface area using UAV tilt photogrammetry","volume":"7","author":"Wang","year":"2022","journal-title":"J. For. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kachamba, D.J., Orka, H.O., Gobakken, T., Eid, T., and Mwase, W. (2016). Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland. Remote Sens., 8.","DOI":"10.3390\/rs8110968"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/s13007-019-0419-7","article-title":"Accuracy Assessment of Plant Height Using an Unmanned Aerial Vehicle for Quantitative Genomic Analysis in Bread Wheat","volume":"15","author":"Hassan","year":"2019","journal-title":"Plant Methods"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"108858","DOI":"10.1016\/j.compag.2024.108858","article-title":"Assessment of the Influence of UAV-Borne LiDAR Scan Angle and Flight Altitude on the Estimation of Wheat Structural Metrics with Different Leaf Angle Distributions","volume":"220","author":"Gu","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.isprsjprs.2020.02.013","article-title":"Above-Ground Biomass Estimation and Yield Prediction in Potato by Using UAV-Based RGB and Hyperspectral Imaging","volume":"162","author":"Li","year":"2020","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.compag.2017.07.008","article-title":"Crop Height Monitoring with Digital Imagery from Unmanned Aerial System (UAS)","volume":"141","author":"Chang","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, D., Li, R., Zhu, B., Liu, T., Sun, C., and Guo, W. (2023). Estimation of Wheat Plant Height and Biomass by Combining UAV Imagery and Elevation Data. Agriculture, 13.","DOI":"10.3390\/agriculture13010009"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.eja.2018.02.004","article-title":"Estimation of Plant Height Using a High Throughput Phenotyping Platform Based on Unmanned Aerial Vehicle and Self-Calibration: Example for Sorghum Breeding","volume":"95","author":"Hu","year":"2018","journal-title":"Eur. J. Agron."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.biosystemseng.2020.11.010","article-title":"Combining Plant Height, Canopy Coverage and Vegetation Index from UAV-Based RGB Images to Estimate Leaf Nitrogen Concentration of Summer Maize","volume":"202","author":"Lu","year":"2021","journal-title":"Biosyst. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jimenez-Berni, J.A., Deery, D.M., Rozas-Larraondo, P., Condon, A.T.G., Rebetzke, G.J., James, R.A., Bovill, W.D., Furbank, R.T., and Sirault, X.R. (2018). High Throughput Determination of Plant Height, Ground Cover, and Above-Ground Biomass in Wheat with LiDAR. Front. Plant Sci., 9.","DOI":"10.3389\/fpls.2018.00237"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sudu, B., Rong, G., Guga, S., Li, K., Zhi, F., Guo, Y., Zhang, J., and Bao, Y. (2022). Retrieving SPAD Values of Summer Maize Using UAV Hyperspectral Data Based on Multiple Machine Learning Algorithm. Remote Sens., 14.","DOI":"10.3390\/rs14215407"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"111564","DOI":"10.1016\/j.ecolind.2024.111564","article-title":"Mapping Aboveground Biomass of Moso Bamboo (Phyllostachys pubescens) Forests under Pantana phyllostachysae Chao-Induced Stress Using Sentinel-2 Imagery","volume":"158","author":"Chen","year":"2024","journal-title":"Ecol. Indic."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1177\/03091333221088018","article-title":"Prediction of Winter Wheat Yield at County Level in China Using Ensemble Learning","volume":"46","author":"Zhang","year":"2022","journal-title":"Prog. Phys. Geogr. -Earth Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s11119-023-10062-4","article-title":"Aboveground Wheat Biomass Estimation from a Low-Altitude UAV Platform Based on Multimodal Remote Sensing Data Fusion with the Introduction of Terrain Factors","volume":"25","author":"Zhang","year":"2024","journal-title":"Precis. Agric."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4471","DOI":"10.1080\/17538947.2023.2270459","article-title":"Evaluation of Machine Learning Methods and Multi-Source Remote Sensing Data Combinations to Construct Forest above-Ground Biomass Models","volume":"16","author":"Yan","year":"2023","journal-title":"Int. J. Digit. Earth"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"108666","DOI":"10.1016\/j.agrformet.2021.108666","article-title":"Integrating Satellite-Derived Climatic and Vegetation Indices to Predict Smallholder Maize Yield Using Deep Learning","volume":"311","author":"Zhang","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_29","first-page":"103528","article-title":"Comparison of Different Machine Learning Algorithms for Predicting Maize Grain Yield Using UAV-Based Hyperspectral Images","volume":"124","author":"Guo","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"eabc7447","DOI":"10.1126\/sciadv.abc7447","article-title":"A Unified Vegetation Index for Quantifying the Terrestrial Biosphere","volume":"7","author":"Walther","year":"2021","journal-title":"Sci. Adv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel Algorithms for Remote Estimation of Vegetation Fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1109\/JSTARS.2020.3038648","article-title":"A Simple Phenology-Based Vegetation Index for Mapping Invasive Spartina Alterniflora Using Google Earth Engine","volume":"14","author":"Xu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1002\/rse2.315","article-title":"Mapping Mangrove Leaf Area Index (LAI) by Combining Remote Sensing Images with PROSAIL-D and XGBoost Methods","volume":"9","author":"Zhao","year":"2023","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jelowicki, L., Sosnowicz, K., Ostrowski, W., Osinska-Skotak, K., and Bakula, K. (2020). Evaluation of Rapeseed Winter Crop Damage Using UAV-Based Multispectral Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12162618"},{"key":"ref_35","first-page":"215","article-title":"New Research Methods for Vegetation Information Extraction Based on Visible Light Remote Sensing Images from an Unmanned Aerial Vehicle (UAV)","volume":"78","author":"Zhang","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1078","DOI":"10.3390\/agriengineering6020062","article-title":"High-Throughput Phenotyping: Application in Maize Breeding","volume":"6","author":"Resende","year":"2024","journal-title":"AgriEngineering"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"111599","DOI":"10.1016\/j.rse.2019.111599","article-title":"Soybean Yield Prediction from UAV Using Multimodal Data Fusion and Deep Learning","volume":"237","author":"Maimaitijiang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Yang, S., Dong, S., Pang, Q., Smith, P., Abdalla, M., Zhang, J., Wang, G., and Xu, Y. (2023). Simulating Soil Salinity Dynamics, Cotton Yield and Evapotranspiration under Drip Irrigation by Ensemble Machine Learning. Front. Plant Sci., 14.","DOI":"10.3389\/fpls.2023.1143462"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_40","first-page":"1","article-title":"Research Status and Prospect on Height Estimation of Field Crop Using Near-Field Remote Sensing Technology","volume":"3","author":"Jian","year":"2021","journal-title":"Smart Agric."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"108685","DOI":"10.1016\/j.compag.2024.108685","article-title":"Maize Height Estimation Using Combined Unmanned Aerial Vehicle Oblique Photography and LIDAR Canopy Dynamic Characteristics","volume":"218","author":"Liu","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"170602","DOI":"10.1016\/j.scitotenv.2024.170602","article-title":"Estimation of Aboveground Biomass of Senescence Grassland in China\u2019s Arid Region Using Multi-Source Data","volume":"918","author":"Zhou","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zhu, X., Li, M., Xue, Y., Qin, A., Gao, G., Wang, M., and Jiang, Y. (2023). Utilization of the Fusion of Ground-Space Remote Sensing Data for Canopy Nitrogen Content Inversion in Apple Orchards. Horticulturae, 9.","DOI":"10.3390\/horticulturae9101085"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s11119-022-09938-8","article-title":"UAV-Based Multi-Sensor Data Fusion and Machine Learning Algorithm for Yield Prediction in Wheat","volume":"24","author":"Fei","year":"2023","journal-title":"Precis. Agric."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"108462","DOI":"10.1016\/j.compag.2023.108462","article-title":"Enhancing Leaf Area Index and Biomass Estimation in Maize with Feature Augmentation from Unmanned Aerial Vehicle-Based Nadir and Cross-Circling Oblique Photography","volume":"215","author":"Fei","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"108433","DOI":"10.1016\/j.agwat.2023.108433","article-title":"Water Use Efficiency and Its Drivers of Two Typical Cash Crops in an Arid Area of Northwest China","volume":"287","author":"Yu","year":"2023","journal-title":"Agric. Water Manag."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"108076","DOI":"10.1016\/j.compag.2023.108076","article-title":"Prediction of Sugar Beet Yield and Quality Parameters with Varying Nitrogen Fertilization Using Ensemble Decision Trees and Artificial Neural Networks","volume":"212","author":"Varga","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_48","first-page":"49","article-title":"Nitrogen-water Coupling Affects Nitrogen Utilization and Yield of Film-mulched Maize under Drip Irrigation","volume":"38","author":"Shang","year":"2019","journal-title":"J. Irrig. Drain."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s00271-022-00813-y","article-title":"Exploring Limiting Factors for Maize Growth in Northeast China and Potential Coping Strategies","volume":"41","author":"Yang","year":"2023","journal-title":"Irrig. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4229","DOI":"10.15666\/aeer\/1702_42294243","article-title":"Effects of nitrogen and three soil types on maize (Zea mays L.) Grain yield in northeast China","volume":"17","author":"Feng","year":"2019","journal-title":"Appl. Ecol. Environ. Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.3724\/SP.J.1006.2012.01080","article-title":"Characteristics of Accumulation, Transition and Distribution of Assimilate in Summer Maize Varieties with Different Plant Height","volume":"38","author":"Li","year":"2012","journal-title":"Acta Agron. Sin."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3176\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:43:59Z","timestamp":1760111039000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3176"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,28]]},"references-count":51,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16173176"],"URL":"https:\/\/doi.org\/10.3390\/rs16173176","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,28]]}}}