{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T19:36:43Z","timestamp":1778355403111,"version":"3.51.4"},"reference-count":53,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T00:00:00Z","timestamp":1700524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research and Development Program of Hebei province","award":["20326406D"],"award-info":[{"award-number":["20326406D"]}]},{"name":"Research and Development Program of Hebei province","award":["32371998"],"award-info":[{"award-number":["32371998"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["20326406D"],"award-info":[{"award-number":["20326406D"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32371998"],"award-info":[{"award-number":["32371998"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Low-cost UAV RGB imagery combined with deep learning models has demonstrated the potential for the development of a feasible tool for field-scale yield prediction. However, collecting sufficient labeled training samples at the field scale remains a considerable challenge, significantly limiting the practical use. In this study, a split-merge framework was proposed to address the issue of limited training samples at the field scale. Based on the split-merge framework, a yield prediction method for winter wheat using the state-of-the-art Efficientnetv2_s (Efficientnetv2_s_spw) and UAV RGB imagery was presented. In order to demonstrate the effectiveness of the split-merge framework, in this study, Efficientnetv2_s_pw was built by directly feeding the plot images to Efficientnetv2_s. The results indicated that the proposed split-merge framework effectively enlarged the training samples, thus enabling improved yield prediction performance. Efficientnetv2_s_spw performed best at the grain-filling stage, with a coefficient of determination of 0.6341 and a mean absolute percentage error of 7.43%. The proposed split-merge framework improved the model ability to extract indicative image features, partially mitigating the saturation issues. Efficientnetv2_s_spw demonstrated excellent adaptability across the water treatments and was recommended at the grain-filling stage. Increasing the ground resolution of input images may further improve the estimation performance. Alternatively, improved performance may be achieved by incorporating additional data sources, such as the canopy height model (CHM). This study indicates that Efficientnetv2_s_spw is a promising tool for field-scale yield prediction of winter wheat, providing a practical solution to field-specific crop management.<\/jats:p>","DOI":"10.3390\/rs15235444","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T12:12:13Z","timestamp":1700568733000},"page":"5444","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Wheat Yield Prediction Using Unmanned Aerial Vehicle RGB-Imagery-Based Convolutional Neural Network and Limited Training Samples"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5750-9295","authenticated-orcid":false,"given":"Juncheng","family":"Ma","sequence":"first","affiliation":[{"name":"College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongfeng","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Binhui","family":"Liu","sequence":"additional","affiliation":[{"name":"Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui 053000, China"},{"name":"Key Laboratory of Crop Drouht Tolerance Research of Heibei Province, Hengshui 053000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenying","family":"Zhang","sequence":"additional","affiliation":[{"name":"Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui 053000, China"},{"name":"Key Laboratory of Crop Drouht Tolerance Research of Heibei Province, Hengshui 053000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bianyin","family":"Wang","sequence":"additional","affiliation":[{"name":"Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui 053000, China"},{"name":"Key Laboratory of Crop Drouht Tolerance Research of Heibei Province, Hengshui 053000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoyang","family":"Chen","sequence":"additional","affiliation":[{"name":"Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui 053000, China"},{"name":"Key Laboratory of Crop Drouht Tolerance Research of Heibei Province, Hengshui 053000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangcai","family":"Wang","sequence":"additional","affiliation":[{"name":"Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui 053000, China"},{"name":"Key Laboratory of Crop Drouht Tolerance Research of Heibei Province, Hengshui 053000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anqiang","family":"Guo","sequence":"additional","affiliation":[{"name":"Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui 053000, China"},{"name":"Key Laboratory of Crop Drouht Tolerance Research of Heibei Province, Hengshui 053000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"126569","DOI":"10.1016\/j.eja.2022.126569","article-title":"Mixing Process-Based and Data-Driven Approaches in Yield Prediction","volume":"139","author":"Maestrini","year":"2022","journal-title":"Eur. J. Agron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105197","DOI":"10.1016\/j.compag.2019.105197","article-title":"Modeling Yield Response to Crop Management Using Convolutional Neural Networks","volume":"170","author":"Barbosa","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"106632","DOI":"10.1016\/j.compag.2021.106632","article-title":"Identifying Causes of Crop Yield Variability with Interpretive Machine Learning","volume":"192","author":"Jones","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"126621","DOI":"10.1016\/j.eja.2022.126621","article-title":"Prediction of Field Winter Wheat Yield Using Fewer Parameters at Middle Growth Stage by Linear Regression and the BP Neural Network Method","volume":"141","author":"Tang","year":"2022","journal-title":"Eur. J. Agron."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"108773","DOI":"10.1016\/j.agrformet.2021.108773","article-title":"Bayesian Multi-Modeling of Deep Neural Nets for Probabilistic Crop Yield Prediction","volume":"314","author":"Abbaszadeh","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108786","DOI":"10.1016\/j.fcr.2022.108786","article-title":"Winter Wheat Yield Prediction Using Convolutional Neural Networks and UAV-Based Multispectral Imagery","volume":"291","author":"Tanabe","year":"2023","journal-title":"Field Crops Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"112938","DOI":"10.1016\/j.rse.2022.112938","article-title":"Subfield Maize Yield Prediction Improves When In-Season Crop Water Deficit Is Included in Remote Sensing Imagery-Based Models","volume":"272","author":"Shuai","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.biosystemseng.2021.01.017","article-title":"Yield Estimation of Soybean Breeding Lines under Drought Stress Using Unmanned Aerial Vehicle-Based Imagery and Convolutional Neural Network","volume":"204","author":"Zhou","year":"2021","journal-title":"Biosyst. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108698","DOI":"10.1016\/j.agrformet.2021.108698","article-title":"Machine Learning in Crop Yield Modelling: A Powerful Tool, but No Surrogate for Science","volume":"312","author":"Lischeid","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.agrformet.2016.01.009","article-title":"Identifying Indicators for Extreme Wheat and Maize Yield Losses","volume":"220","author":"Adrian","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105709","DOI":"10.1016\/j.compag.2020.105709","article-title":"Crop Yield Prediction Using Machine Learning: A Systematic Literature Review","volume":"177","author":"Kassahun","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"108736","DOI":"10.1016\/j.agrformet.2021.108736","article-title":"Early Season Prediction of within-Field Crop Yield Variability by Assimilating CubeSat Data into a Crop Model","volume":"313","author":"Ziliani","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"578","DOI":"10.2135\/cropsci2005.0059","article-title":"Spectral Reflectance Indices as a Potential Indirect Selection Criteria for Wheat Yield under Irrigation","volume":"46","author":"Babar","year":"2006","journal-title":"Crop Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.eja.2019.02.007","article-title":"Low-Cost Assessment of Grain Yield in Durum Wheat Using RGB Images","volume":"105","author":"Kefauver","year":"2019","journal-title":"Eur. J. Agron."},{"key":"ref_16","first-page":"103292","article-title":"Field-Scale Yield Prediction of Winter Wheat under Different Irrigation Regimes Based on Dynamic Fusion of Multimodal UAV Imagery","volume":"118","author":"Ma","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107721","DOI":"10.1016\/j.compag.2023.107721","article-title":"Machine Learning Technology for Early Prediction of Grain Yield at the Field Scale: A Systematic Review","volume":"207","author":"Leukel","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"109057","DOI":"10.1016\/j.agrformet.2022.109057","article-title":"Combining Multi-Indicators with Machine-Learning Algorithms for Maize Yield Early Prediction at the County-Level in China","volume":"323","author":"Cheng","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_19","first-page":"187","article-title":"UAV-based Multi-sensor Data Fusion and Machine Learning Algorithm for Yield Prediction in Wheat","volume":"27","author":"Fei","year":"2022","journal-title":"Precis. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2020.09.015","article-title":"Developing a Machine Learning Based Cotton Yield Estimation Framework Using Multi-Temporal UAS Data","volume":"169","author":"Ashapure","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108096","DOI":"10.1016\/j.agrformet.2020.108096","article-title":"Grain Yield Prediction of Rice Using Multi-Temporal UAV-Based RGB and Multispectral Images and Model Transfer\u2014A Case Study of Small Farmlands in the South of China","volume":"291","author":"Wan","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_22","first-page":"102397","article-title":"Combining Spectral and Textural Information in UAV Hyperspectral Images to Estimate Rice Grain Yield","volume":"102","author":"Wang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106036","DOI":"10.1016\/j.compag.2021.106036","article-title":"Sequential Forward Selection and Support Vector Regression in Comparison to LASSO Regression for Spring Wheat Yield Prediction Based on UAV Imagery","volume":"183","author":"Shafiee","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1186\/s13007-022-00949-0","article-title":"Combining Novel Feature Selection Strategy and Hyperspectral Vegetation Indices to Predict Crop Yield","volume":"18","author":"Fei","year":"2022","journal-title":"Plant Methods"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.eja.2011.06.006","article-title":"On Farm Assessment of Rice Yield Variability and Productivity Gaps between Organic and Conventional Cropping Systems under Mediterranean Climate","volume":"35","author":"Delmotte","year":"2011","journal-title":"Eur. J. Agron."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.isprsjprs.2021.02.008","article-title":"Field-Scale Crop Yield Prediction Using Multi-Temporal WorldView-3 and PlanetScope Satellite Data and Deep Learning","volume":"174","author":"Sagan","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106480","DOI":"10.1016\/j.compag.2021.106480","article-title":"Estimation of Leaf Area Index for Winter Wheat at Early Stages Based on Convolutional Neural Networks","volume":"190","author":"Li","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.eja.2018.12.004","article-title":"Estimating above Ground Biomass of Winter Wheat at Early Growth Stages Using Digital Images and Deep Convolutional Neural Network","volume":"103","author":"Ma","year":"2019","journal-title":"Eur. J. Agron."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"104859","DOI":"10.1016\/j.compag.2019.104859","article-title":"Crop Yield Prediction with Deep Convolutional Neural Networks","volume":"163","author":"Nevavuori","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.fcr.2019.02.022","article-title":"Deep Convolutional Neural Networks for Rice Grain Yield Estimation at the Ripening Stage Using UAV-Based Remotely Sensed Images","volume":"235","author":"Yang","year":"2019","journal-title":"Field Crops Res."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zeng, L., Peng, G., Meng, R., Man, J., Li, W., Xu, B., Lv, Z., and Sun, R. (2021). Wheat Yield Prediction Based on Unmanned Aerial Vehicles-Collected Red\u2013Green\u2013Blue Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13152937"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.1007\/s11119-022-09940-0","article-title":"Leaf Area Index Estimations by Deep Learning Models Using RGB Images and Data Fusion in Maize","volume":"23","author":"Egea","year":"2022","journal-title":"Precis. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"112724","DOI":"10.1016\/j.rse.2021.112724","article-title":"Transfer-Learning-Based Approach for Leaf Chlorophyll Content Estimation of Winter Wheat from Hyperspectral Data","volume":"267","author":"Zhang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105299","DOI":"10.1016\/j.compag.2020.105299","article-title":"Aerial Hyperspectral Imagery and Deep Neural Networks for High-Throughput Yield Phenotyping in Wheat","volume":"172","author":"Moghimi","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1111\/j.1365-3180.1974.tb01084.x","article-title":"A Decimal Code for the Growth Stages of Cereals","volume":"14","author":"Zadoks","year":"1974","journal-title":"Weed Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1111\/j.1744-7348.1995.tb05015.x","article-title":"Harvest Index: A Review of Its Use in Plant Breeding and Crop Physiology","volume":"126","author":"Hay","year":"1995","journal-title":"Ann. Appl. Biol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1093\/jxb\/47.5.623","article-title":"The Duration and Rate of Grain Growth, and Harvest Index, of Wheat (Triticum Aestivum L.) in Response to Temperature and CO2","volume":"47","author":"Wheeler","year":"1996","journal-title":"J. Exp. Bot."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.compag.2008.03.009","article-title":"Verification of Color Vegetation Indices for Automated Crop Imaging Applications","volume":"63","author":"Meyer","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_41","unstructured":"Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L.C., Tan, M., Chu, G., Vasudevan, V., Zhu, Y., and Pang, R. (November, January 27). Searching for mobileNetV3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_42","unstructured":"Tan, M., and Le, Q.V. (2021, January 18\u201324). EfficientNetV2: Smaller Models and Faster Training. Proceedings of the International Conference on Machine Learning, Virtual."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"1426","DOI":"10.2135\/cropsci2006.07.0492","article-title":"Potential Use of Spectral Reflectance Indices as a Selection Tool for Grain Yield in Winter Wheat under Great Plains Conditions","volume":"47","author":"Prasad","year":"2007","journal-title":"Crop Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.isprsjprs.2014.03.016","article-title":"Detection of Early Plant Stress Responses in Hyperspectral Images","volume":"93","author":"Behmann","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"116226","DOI":"10.1016\/j.eswa.2021.116226","article-title":"Towards Improved Accuracy of UAV-Based Wheat Ears Counting: A Transfer Learning Method of the Ground-Based Fully Convolutional Network","volume":"191","author":"Ma","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"108730","DOI":"10.1016\/j.fcr.2022.108730","article-title":"Application of Multi-Layer Neural Network and Hyperspectral Reflectance in Genome-Wide Association Study for Grain Yield in Bread Wheat","volume":"289","author":"Fei","year":"2022","journal-title":"Field Crops Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1186\/s13007-020-00648-8","article-title":"Wheat Ear Counting Using K-Means Clustering Segmentation and Convolutional Neural Network","volume":"16","author":"Xu","year":"2020","journal-title":"Plant Methods"},{"key":"ref_49","unstructured":"Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., and Brendel, W. (2018). ImageNet-Trained CNNs Are Biased towards Texture; Increasing Shape Bias Improves Accuracy and Robustness. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"105159","DOI":"10.1016\/j.compag.2019.105159","article-title":"Segmenting Ears of Winter Wheat at Flowering Stage Using Digital Images and Deep Learning","volume":"168","author":"Ma","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s13007-018-0289-4","article-title":"Wheat Ear Counting In-Field Conditions: High Throughput and Low-Cost Approach Using RGB Images","volume":"14","author":"Kefauver","year":"2018","journal-title":"Plant Methods"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"379","DOI":"10.3389\/fpls.2017.00379","article-title":"Evaluation of Yield and Drought Using Active and Passive Spectral Sensing Systems at the Reproductive Stage in Wheat","volume":"8","author":"Becker","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/23\/5444\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:26:26Z","timestamp":1760131586000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/23\/5444"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,21]]},"references-count":53,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["rs15235444"],"URL":"https:\/\/doi.org\/10.3390\/rs15235444","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,21]]}}}