{"title":"机械化采伐木材损伤监测系统的研制","authors":"T. Palander, J. Eronen, K. Kärhä, H. Ovaskainen","doi":"10.15287/AFR.2018.1084","DOIUrl":null,"url":null,"abstract":"Cut-to-length harvesting is a cost-efficient method of the wood supply chain. However, it risks causing stem damage in the mechanized process of thinning forest stands, thereby reducing the growth and technical quality of the remaining trees, which would then be exposed on the increased vulnerability to fungal diseases. For these reasons, it is critical to support quality monitoring of harvesting machines. One way to support quality monitoring is through the application of machine vision solutions. In this study, the damaged stems were photographed systematically from a strip road. The success of the stem-damage detection was analyzed to determine the relationships between successful detection, stand condition, and the image-processing technique. Statistically meaningful relationships were identified via logistic regression analysis, which can be used in selection of tailored image processing technique. The study indicated that the quality-monitoring system of mechanized harvesting could be improved by an increased focus on developing the multi-view photogrammetry of stem damages according to different stand conditions. Further, refining the machine learning system would support the need to determine accurate image-processing thresholds of the texture of stem damages. Then, the overall proportion of successful stem-damage detections will be 89%. These improvements of the quality monitoring system will provide the efficient thinning process in the sustainable wood supply from forests to forest industry. The implementation of such a system could be much broader, initially under Nordic conditions and then in other countries as well, given that its development takes into considerations the significant calibration factors of local conditions.","PeriodicalId":48954,"journal":{"name":"Annals of Forest Research","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2018-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Development of a wood damage monitoring system for mechanized harvesting\",\"authors\":\"T. Palander, J. Eronen, K. Kärhä, H. Ovaskainen\",\"doi\":\"10.15287/AFR.2018.1084\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cut-to-length harvesting is a cost-efficient method of the wood supply chain. However, it risks causing stem damage in the mechanized process of thinning forest stands, thereby reducing the growth and technical quality of the remaining trees, which would then be exposed on the increased vulnerability to fungal diseases. For these reasons, it is critical to support quality monitoring of harvesting machines. One way to support quality monitoring is through the application of machine vision solutions. In this study, the damaged stems were photographed systematically from a strip road. The success of the stem-damage detection was analyzed to determine the relationships between successful detection, stand condition, and the image-processing technique. Statistically meaningful relationships were identified via logistic regression analysis, which can be used in selection of tailored image processing technique. The study indicated that the quality-monitoring system of mechanized harvesting could be improved by an increased focus on developing the multi-view photogrammetry of stem damages according to different stand conditions. Further, refining the machine learning system would support the need to determine accurate image-processing thresholds of the texture of stem damages. Then, the overall proportion of successful stem-damage detections will be 89%. These improvements of the quality monitoring system will provide the efficient thinning process in the sustainable wood supply from forests to forest industry. The implementation of such a system could be much broader, initially under Nordic conditions and then in other countries as well, given that its development takes into considerations the significant calibration factors of local conditions.\",\"PeriodicalId\":48954,\"journal\":{\"name\":\"Annals of Forest Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2018-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Forest Research\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.15287/AFR.2018.1084\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"FORESTRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Forest Research","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.15287/AFR.2018.1084","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"FORESTRY","Score":null,"Total":0}
Development of a wood damage monitoring system for mechanized harvesting
Cut-to-length harvesting is a cost-efficient method of the wood supply chain. However, it risks causing stem damage in the mechanized process of thinning forest stands, thereby reducing the growth and technical quality of the remaining trees, which would then be exposed on the increased vulnerability to fungal diseases. For these reasons, it is critical to support quality monitoring of harvesting machines. One way to support quality monitoring is through the application of machine vision solutions. In this study, the damaged stems were photographed systematically from a strip road. The success of the stem-damage detection was analyzed to determine the relationships between successful detection, stand condition, and the image-processing technique. Statistically meaningful relationships were identified via logistic regression analysis, which can be used in selection of tailored image processing technique. The study indicated that the quality-monitoring system of mechanized harvesting could be improved by an increased focus on developing the multi-view photogrammetry of stem damages according to different stand conditions. Further, refining the machine learning system would support the need to determine accurate image-processing thresholds of the texture of stem damages. Then, the overall proportion of successful stem-damage detections will be 89%. These improvements of the quality monitoring system will provide the efficient thinning process in the sustainable wood supply from forests to forest industry. The implementation of such a system could be much broader, initially under Nordic conditions and then in other countries as well, given that its development takes into considerations the significant calibration factors of local conditions.
期刊介绍:
Annals of Forest Research is a semestrial open access journal, which publishes research articles, research notes and critical review papers, exclusively in English, on topics dealing with forestry and environmental sciences. The journal promotes high scientific level articles, by following international editorial conventions and by applying a peer-review selection process.