[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120701-en":3,"doc-seo-120701-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120701,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Machine learning assisted remote forestry health assessment - a comprehensive state of the art review","Forests increasingly experience water stress and heat extremes under climate change, driving greater interest in remote, data-driven forestry health assessment. Machine learning methods integrated with robotic platforms and artificial vision enable monitoring of key vegetation and forest-health indicators, including moisture content, chlorophyll and nitrogen estimation, canopy characteristics, and degradation patterns. This review consolidates recent advances in remote monitoring, emphasizing critical structural and morphological vegetation parameters, synthesizing 108 studies from the last five years and highlighting emerging AI tool directions for the near future.","TYPE Review  \nPUBLISHED 02 June 2023  \nDOI 10.3389/fpls.2023.1139232  \nOPEN ACCESS  \nEDITED BY Long He,  \nThe Pennsylvania State University (PSU), United States  \nREVIEWED BY Yongxin Liu,  \nEmbry–Riddle Aeronautical University, United States  \nCostas A Varotsos,  \nNational and Kapodistrian University of Athens, Greece  \nYanqiu Yang,  \nThe Pennsylvania State University (PSU), United States  \n*CORRESPONDENCE Fernando Auat Cheein  \n [fernando.auat@usm.cl](fernando.auat@usm.cl)  \nRECEIVED 06 January 2023  \nACCEPTED 08 May 2023  \nPUBLISHED 02 June 2023  \nCITATION  \nEstrada JS, Fuentes A, Reszka P and Auat Cheein F (2023) Machine learning  \nassisted remote forestry health assessment: a comprehensive state of the art review. Front. Plant Sci. 14:1139232 .  \ndoi: 10.3389/fpls.2023.1139232  \nCOPYRIGHT  \n© 2023 Estrada, Fuentes, Reszka and Auat Cheein. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning assisted remote forestry health assessment: a comprehensive state of the art review  \nJuan Sebasti´an Estrada 1, Andr´es Fuentes 2, Pedro Reszka 3 and Fernando Auat Cheein 1*  \n1 Department of Electronic Engineering, Universidad Tecnica Federico, Santamaria, Valparaíso, Chile, 2 Department of Industrial Engeneering, Universidad Tecnica Federica, Santamaria, Valparaíso, Chile, 3 Faculty on Engineering and Science, Universidad Adolfo Ibáñez, Santiago, Chile  \nForests are suffering water stress due to climate change; in some parts of the globe, forests are being exposed to the highest temperatures historically recorded. Machine learning techniques combined with robotic platforms and artificial vision systems have been used to provide remote monitoring of the health of the forest, including moisture content, chlorophyll, and nitrogen estimation, forest canopy, and forest degradation, among others. However, artificial intelligence techniques evolve fast associated with the computational resources; data acquisition, and processing change accordingly. This article is aimed at gathering the latest developments in remote monitoring of the health of the forests, with special emphasis on the most important vegetation parameters (structural and morphological), using machine learning techniques. The analysis presented here gathered 108 articles from the last 5 years, and we conclude by showing the newest developments in AI tools that might be used in the near future.  \nKEYWORDS  \nforestry health assessment, remote sensing, machine learning, vision system, spectral information  \n1 Introduction  \nClimate change has increased the frequency and duration of droughts around the world (Cook et al., 2014) . This has a special impact on ecosystems, where it is estimated by the United Nations Convention to Combat Desertification (UNCCD) that in the last 40 years the percentage of vegetated areas affected by droughts has doubled, and around 12 million hectares of agricultural land have been lost due to desertification (UNCCD, 2022) . Another issue caused by intense droughts is the increase in wildfires. According to UNCCD (2022) more than 84% of terrestrial ecosystems are in danger due to more frequent and intensive fires. Forests are particularly affected by longer droughts due to water stress; the relationship between forestry health and posterior forest recovery is still being studied (Xu et al., 2018) .  \nFrontiers in Plant Science 01 [frontiersin.org](frontiersin.org)  \nForest management plays a fundamental role in the analysis of forest health. Its main target is to reduce risks or negative impacts derived from external disturbances (Migliavacca et al.","cbCaimILrKFz03EF","https://ap.wps.com/l/cbCaimILrKFz03EF","pdf",17920195,1,25,"English","en",105,"# Introduction\n## Climate change impacts on forests and drought\n## Forest management and health indicators\n## Remote sensing technologies and UAV-based monitoring","[{\"question\":\"How is machine learning used in remote forestry health assessment?\",\"answer\":\"Machine learning techniques combined with robotic platforms and artificial vision systems support remote monitoring of forest health indicators such as moisture, chlorophyll, nitrogen, canopy traits, and degradation patterns.\"},{\"question\":\"Which vegetation parameters does the review emphasize?\",\"answer\":\"The review emphasizes key vegetation parameters, particularly structural and morphological attributes, used to assess forest health through remote sensing.\"},{\"question\":\"What scope and timeframe does the review cover?\",\"answer\":\"The analysis synthesizes 108 articles published over the last five years and concludes with newer developments in AI tools expected to be used soon.\"}]","Machine learning assisted remote forestry health assessment - 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