{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:00:25Z","timestamp":1776441625989,"version":"3.51.2"},"reference-count":16,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T00:00:00Z","timestamp":1734393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Major Project","award":["2021ZD0110901"],"award-info":[{"award-number":["2021ZD0110901"]}]},{"name":"National Science and Technology Major Project","award":["221100110700"],"award-info":[{"award-number":["221100110700"]}]},{"name":"National Science and Technology Major Project","award":["2022YFD1900404"],"award-info":[{"award-number":["2022YFD1900404"]}]},{"name":"Key Grant Technology Project of Henan","award":["2021ZD0110901"],"award-info":[{"award-number":["2021ZD0110901"]}]},{"name":"Key Grant Technology Project of Henan","award":["221100110700"],"award-info":[{"award-number":["221100110700"]}]},{"name":"Key Grant Technology Project of Henan","award":["2022YFD1900404"],"award-info":[{"award-number":["2022YFD1900404"]}]},{"name":"Intelligent Irrigation Water and Fertilizer Digital Decision System and Regulation Equipment","award":["2021ZD0110901"],"award-info":[{"award-number":["2021ZD0110901"]}]},{"name":"Intelligent Irrigation Water and Fertilizer Digital Decision System and Regulation Equipment","award":["221100110700"],"award-info":[{"award-number":["221100110700"]}]},{"name":"Intelligent Irrigation Water and Fertilizer Digital Decision System and Regulation Equipment","award":["2022YFD1900404"],"award-info":[{"award-number":["2022YFD1900404"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The effectiveness of supervised ML heavily depends on having a large, accurate, and diverse annotated dataset, which poses a challenge in applying ML for yield prediction. To address this issue, we developed a self-training random forest algorithm capable of automatically expanding the annotated dataset. Specifically, we trained a random forest regressor model using a small amount of annotated data. This model was then utilized to generate new annotations, thereby automatically extending the training dataset through self-training. Our experiments involved collecting data from over 30 winter wheat varieties during the 2019\u20132020 and 2021\u20132022 growing seasons. The testing results indicated that our model achieved an R2 of 0.84, RMSE of 627.94 kg\/ha, and MAE of 516.94 kg\/ha in the test dataset, while the validation dataset yielded an R2 of 0.81, RMSE of 692.96 kg\/ha, and MAE of 550.62 kg\/ha. In comparison, the standard random forest resulted in an R2 of 0.81, RMSE of 681.02 kg\/ha, and MAE of 568.97 kg\/ha in the test dataset, with validation results of an R2 of 0.79, RMSE of 736.24 kg\/ha, and MAE of 585.85 kg\/ha. Overall, these results demonstrate that our self-training random forest algorithm is a practical and effective solution for expanding annotated datasets, thereby enhancing the prediction accuracy of ML models in winter wheat yield forecasting.<\/jats:p>","DOI":"10.3390\/rs16244723","type":"journal-article","created":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T03:40:56Z","timestamp":1734493256000},"page":"4723","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Use Self-Training Random Forest for Predicting Winter Wheat Yield"],"prefix":"10.3390","volume":"16","author":[{"given":"Yulin","family":"Shen","sequence":"first","affiliation":[{"name":"Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China"},{"name":"TERRA, Biosystems Dynamics and Exchanges, Gembloux Agro-Bio Tech, University of Li\u00e8ge, 5030 Gembloux, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3188-4772","authenticated-orcid":false,"given":"Beno\u00eet","family":"Mercatoris","sequence":"additional","affiliation":[{"name":"TERRA, Biosystems Dynamics and Exchanges, Gembloux Agro-Bio Tech, University of Li\u00e8ge, 5030 Gembloux, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2621-9222","authenticated-orcid":false,"given":"Qingzhi","family":"Liu","sequence":"additional","affiliation":[{"name":"Information Technology Group, Wageningen University and Research, 6704 Wageningen, The Netherlands"}]},{"given":"Hongxun","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Zongpeng","family":"Li","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Water-Saving Agriculture, Farmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2847-0042","authenticated-orcid":false,"given":"Zhen","family":"Chen","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Water-Saving Agriculture, Farmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China"}]},{"given":"Wensheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, S., Li, F., Gao, M., Li, Z., Leng, P., Duan, S., and Ren, J. (2021). A New Method for Winter Wheat Mapping Based on Spectral Reconstruction Technology. Remote Sens., 13.","DOI":"10.3390\/rs13091810"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Meraj, G., Kanga, S., Ambadkar, A., Kumar, P., Singh, S.K., Farooq, M., Johnson, B.A., Rai, A., and Sahu, N. (2022). Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling. Remote Sens., 14.","DOI":"10.3390\/rs14133005"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wu, Y., Xu, W., Huang, H., and Huang, J. (2022). Bayesian Posterior-Based Winter Wheat Yield Estimation at the Field Scale through Assimilation of Sentinel-2 Data into WOFOST Model. Remote Sens., 14.","DOI":"10.3390\/rs14153727"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mezera, J., Lukas, V., Hornia\u010dek, I., Smutn\u00fd, V., and Elbl, J. (2022). Comparison of Proximal and Remote Sensing for the Diagnosis of Crop Status in Site-Specific Crop Management. Sensors, 22.","DOI":"10.3390\/s22010019"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.isprsjprs.2022.03.008","article-title":"Field-level crop yield estimation with PRISMA and Sentinel-2","volume":"187","author":"Marshall","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106616","DOI":"10.1016\/j.compag.2021.106616","article-title":"Improving wheat yield estimates using data augmentation models and remotely sensed biophysical indices within deep neural networks in the Guanzhong Plain, PR China","volume":"192","author":"Zhang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Prey, L., Hanemann, A., Ramgraber, L., Seidl-Schulz, J., and Noack, P.O. (2022). UAV-Based Estimation of Grain Yield for Plant Breeding: Applied Strategies for Optimizing the Use of Sensors, Vegetation Indices, Growth Stages, and Machine Learning Algorithms. Remote Sens., 14.","DOI":"10.3390\/rs14246345"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Feng, H., Tao, H., Li, Z., Yang, G., and Zhao, C. (2022). Comparison of UAV RGB imagery and hyperspectral remote-sensing data for monitoring winter-wheat growth. Remote Sens., 14.","DOI":"10.3390\/rs14153811"},{"key":"ref_9","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_10","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_11","doi-asserted-by":"crossref","unstructured":"Pang, A., Chang, M.W., and Chen, Y. (2022). Evaluation of Random Forests (RF) for Regional and Local-Scale Wheat Yield Prediction in Southeast Australia. Sensors, 22.","DOI":"10.3390\/s22030717"},{"key":"ref_12","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 Crop. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"108640","DOI":"10.1016\/j.fcr.2022.108640","article-title":"Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India","volume":"287","author":"Nayak","year":"2022","journal-title":"Field Crop. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1093\/nsr\/nwx106","article-title":"A brief introduction to weakly supervised learning","volume":"5","author":"Zhou","year":"2018","journal-title":"Natl. Sci. Rev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/S0168-1923(99)00030-1","article-title":"Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling","volume":"95","author":"Jones","year":"1999","journal-title":"Agric. For. Meteorol."},{"key":"ref_16","unstructured":"Mukherjee, S., and Awadallah, A.H. (2020). Uncertainty-aware Self-training for Text Classification with Few Labels. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4723\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:54:13Z","timestamp":1760115253000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4723"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,17]]},"references-count":16,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16244723"],"URL":"https:\/\/doi.org\/10.3390\/rs16244723","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,17]]}}}