{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T13:30:37Z","timestamp":1775827837265,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"2025 Open Project of Key Laboratory of Agricultural Monitoring and Early Warning Technology","award":["JCYJKFKT2501"],"award-info":[{"award-number":["JCYJKFKT2501"]}]},{"name":"Forestry Public Welfare Scientific Research Project of China","award":["201504303"],"award-info":[{"award-number":["201504303"]}]},{"name":"Zhejiang Provincial Soft Science Research Project","award":["2026C35055"],"award-info":[{"award-number":["2026C35055"]}]},{"name":"Guangdong Province Key Construction Discipline Research Capacity Enhancement Project","award":["2024ZDJS101"],"award-info":[{"award-number":["2024ZDJS101"]}]},{"name":"Guangdong Province Key Research Project of Colleges and Universities","award":["2025ZDZX3054"],"award-info":[{"award-number":["2025ZDZX3054"]}]},{"name":"Key Project for Industrial Upgrading in Longwan Modern Agricultural Industry Research Institute","award":["2025LWYJY20"],"award-info":[{"award-number":["2025LWYJY20"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Plants"],"abstract":"<jats:p>The double hidden-layer neural network has increasingly been applied in tree height modeling due to its superior performance. To improve the precision of tree height estimation, this study compared the performance of a double hidden-layer neural network with that of a nonlinear mixed-effects model, aiming to provide a new method for tree height prediction. Taking the Larix olgensis forest plantation in Jilin Province as the research object, a double hidden-layer back propagation (BP) neural network was established for tree height prediction by adopting trial and error, k-fold cross-validation, and near-domain optimization strategies. In constructing the nonlinear mixed-effects model, the overall and local differences in forest growth data, as well as the autocorrelation among the various levels of data, were considered. Accordingly, after determining the base model, random effects were introduced, the correlation variance\u2013covariance matrix was calculated, and random parameters were estimated to compare the predictive performance of the two aforementioned models. For the mixed-effects model, the coefficient of determination R2 was 0.8590, the root mean square error (RMSE) was 1.6230, and the mean absolute error (MAE) was 2.2658. For the double hidden-layer BP neural network, the R2 reached 0.9068 (an increase of 5.56%), the RMSE was 1.3197 (a decrease of 18.69%), and the MAE was 1.2736 (a decrease of 43.79%). The results demonstrate that the double hidden-layer BP neural network is superior to the nonlinear mixed-effects model for tree height prediction. Therefore, the results provide a more accurate method for tree height prediction.<\/jats:p>","DOI":"10.3390\/plants15081176","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T12:01:52Z","timestamp":1775822512000},"page":"1176","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Tree Height Prediction Using a Double Hidden-Layer Neural Network and a Mixed-Effects Model"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8495-5725","authenticated-orcid":false,"given":"Jianbo","family":"Shen","sequence":"first","affiliation":[{"name":"Wenzhou Key Laboratory of AI Agents for Agriculture, Wenzhou Vocational College of Science and Technology, Wenzhou 325006, China"},{"name":"Institute of Resources Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6582-587X","authenticated-orcid":false,"given":"Xiangdong","family":"Lei","sequence":"additional","affiliation":[{"name":"Institute of Resources Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yutang","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Forestry Inventory and Planning of Jilin Province, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1142-1105","authenticated-orcid":false,"given":"Yuehong","family":"Pan","sequence":"additional","affiliation":[{"name":"Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0466-0888","authenticated-orcid":false,"given":"Gongming","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Intelligent Collaborative Innovation Studio, Guangzhou Institute of Science and Technology, Guangzhou 510540, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.foreco.2015.08.001","article-title":"Comparing parametric and non-parametric methods of predicting Site Index for radiata pine using combinations of data derived from environmental surfaces, satellite imagery and airborne laser scanning","volume":"357","author":"Watt","year":"2015","journal-title":"For. 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