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Extracting components\u2019 information from individual fruit trees is critical for analyzing and understanding their general growth. This study proposes a method to classify persimmon tree components based on hyperspectral LiDAR data. We extracted nine spectral feature parameters from the colorful point cloud data and performed preliminary classification using random forest, support vector machine, and backpropagation neural network methods. However, the misclassification of edge points with spectral information reduced the accuracy of the classification. To address this, we introduced a reprogramming strategy by fusing spatial constraints with spectral information, which increased the overall classification accuracy by 6.55%. We completed a 3D reconstruction of classification results in spatial coordinates. The proposed method is sensitive to edge points and shows excellent performance for classifying persimmon tree components.<\/jats:p>","DOI":"10.3390\/s23063286","type":"journal-article","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T02:36:22Z","timestamp":1679366182000},"page":"3286","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Feasibility Study on the Classification of Persimmon Trees\u2019 Components Based on Hyperspectral LiDAR"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6988-931X","authenticated-orcid":false,"given":"Hui","family":"Shao","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Anhui Jianzhu University, Hefei 230601, China"},{"name":"Anhui International Joint Research Center for Ancient Architecture Intellisencing and Multi-Dimensional Modeling, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuyu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Anhui Jianzhu University, Hefei 230601, China"},{"name":"Anhui International Joint Research Center for Ancient Architecture Intellisencing and Multi-Dimensional Modeling, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Unmanned System, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peilun","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, 02150 Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Anhui Jianzhu University, Hefei 230601, China"},{"name":"Anhui International Joint Research Center for Ancient Architecture Intellisencing and Multi-Dimensional Modeling, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chong","family":"Xu","sequence":"additional","affiliation":[{"name":"Ji Hua Laboratory, Foshan 528200, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changhui","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, 02150 Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0148-3609","authenticated-orcid":false,"given":"Yuwei","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, 02150 Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., and McCool, C. 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