深度学习在林业中的应用

编号 lyqk009588

中文标题 深度学习在林业中的应用

作者 南玉龙  张慧春  郑加强  杨琨琪 

作者单位 1. 盐城工学院机械工程学院，江苏盐城 224051;
2. 南京林业大学机械电子工程学院，南京 210037

期刊名称 世界林业研究 

年份 2021 

卷号 34

期号 5

栏目名称 专题论述 

中文摘要 林业数据信息的智能处理与深度挖掘分析是智慧林业精准决策与统筹管理的基础。文中主要从树木识别与分类、森林火灾识别与预测、树木病虫害监测和木材检测等4个方面总结深度学习在林业中的应用；从林业数据集、数据标记、特征学习涵盖范围、图像遮挡等方面分析了深度学习在林业领域应用上的缺点与局限性，以充分认识深度学习在林业应用方面亟待解决的问题与突破的方向；从林业领域应用场景、算法改进、数据集共享与研究成果应用的角度，展望深度学习在林业领域应用上亟待加强研究的方面，以提升深度学习解决林业应用问题的广度与深度，促进智能林业的发展。

关键词 深度学习  树木识别  森林火灾预测  病虫害监测  木材检测 

基金项目 盐城工学院校级科研项目资助（xjr2021012）；江苏省现代农机装备与技术示范推广项目（NJ2020-18）；国家自然科学基金项目（32171790）；江苏省六大人才高峰（NY-058）；江苏省青蓝工程项目（苏教201842）

英文标题 Application of Deep Learning to Forestry

作者英文名 Nan Yulong, Zhang Huichun, Zheng Jiaqiang, Yang Kunqi

单位英文名 1. College of Mechanical Engineering, Yancheng Institute of Technology, Yancheng 224051, Jiangsu, China;
2. College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China

英文摘要 The intelligent processing and deep mining analysis of forestry data information are the basis for intelligent and precise forestry decision-makings and integrated management. This paper summarizes the application of deep learning to forestry from four aspects of tree identification and classification, forest fire identification and prediction, tree pest and disease monitoring and wood detection, and analyzes the shortcomings and limitations of deep learning application in forestry in terms of forestry datasets, data labeling, feature learning coverage, and image occlusion, so as to fully understand the problems and breakthrough directions of deep learning application in forestry. From the perspectives of application scenarios in forestry, algorithm improvement, dataset sharing and research results application, the future researches on the application of deep learning to forestry that need to be strengthened are prospected, in order to enhance the breadth and depth to deep learning application in forestry and promote the development of intelligent forestry.

英文关键词 deep learning;tree identification;forest fire prediction;pest monitoring;wood detection

起始页码 87

截止页码 90

投稿时间 2021/1/24

最后修改时间 2021/3/5

作者简介 南玉龙，男，博士，讲师，主要从事农林机械化研究，E-mail：nanyulong2012@163.com

通讯作者介绍 张慧春，博士，教授，博士生导师，主要从事农林信息技术与装备、表型分析平台与技术研究，E-mail：njzhanghc@hotmail.com

E-mail nanyulong2012@163.com;njzhanghc@hotmail.com

分类号 S7, TP391

DOI 10.13348/j.cnki.sjlyyj.2021.0020.y

参考文献 [1] GUO Q H, JIN S C, LI M, et al. Application of deep learning in ecological resource research: theories, methods, and challenges[J]. Science China Earth Sciences, 2020, 63:1457-1474.
[2] KAMILARIS A, PRENAFETA-BOLDú F X. Deep learning in agriculture: a survey[J]. Computers and Electronics in Agriculture, 2018, 147:70-90.
[3] YANG H, HSU H, YANG C, et al. Differentiating between morphologically similar species in genus Cinnamomum (Lauraceae) using deep convolutional neural networks[J]. Computers and Electronics in Agriculture, 2019, 162:739-748.
[4] LIU J, WANG X, WANG T. Classification of tree species and stock volume estimation in ground forest images using Deep Learning[J]. Computers and Electronics in Agriculture, 2019, 166:105012. DOI:10.1016/j.compag.2019.105012
[5] SUN Y, LIU X, YUAN M, et al. Automatic in-trap pest detection using deep learning for pheromone-based Dendroctonus valens monitoring[J]. Biosystems Engineering, 2018, 176:140-150.
[6] 孙红, 李松, 李民赞, 等. 农业信息成像感知与深度学习应用研究进展[J]. 农业机械学报,2020,51(5):1-17.
[7] 傅隆生, 宋珍珍, ZHANG X, 等. 深度学习方法在农业信息中的研究进展与应用现状[J]. 中国农业大学学报,2020,25(2):105-120.
[8] MUBIN N A, NADARAJOO E, SHAFRI H Z M, et al. Young and mature oil palm tree detection and counting using convolutional neural network deep learning method[J]. International Journal of Remote Sensing, 2019, 40(19):7500-7515.
[9] 业巧林, 许等平, 张冬. 基于深度学习特征和支持向量机的遥感图像分类[J]. 林业工程学报,2019,4(2):119-125.
[10] WEINSTEIN B G, MARCONI S, BOHLMAN S A, et al. Cross-site learning in deep learning RGB tree crown detection[J]. Ecological Informatics, 2020, 56:101061. DOI:10.1016/j.ecoinf.2020.101061
[11] GUAN H, YU Y, JI Z, et al. Deep learning-based tree classification using mobile LiDAR data[J]. Remote Sensing Letters, 2015, 6(11):864-873.
[12] PENG Y, WANG Y. Real-time forest smoke detection using hand-designed features and deep learning[J]. Computers and Electronics in Agriculture, 2019, 167:105029. DOI:10.1016/j.compag.2019.105029
[13] CUI F. Deployment and integration of smart sensors with IoT devices detecting fire disasters in huge forest environment[J]. Computer Communications, 2020, 150:818-827.
[14] HAGHIAN P Z. Deep representation learning and prediction for forest wildfires[D]. Waterloo: University of Waterloo, 2019.
[15] 孙立研, 刘美玲, 周礼祥, 等. 基于气象因子深度学习的森林火灾预测方法[J]. 林业工程学报,2019,4(3):132-136.
[16] HU G, YIN C, WAN M, et al. Recognition of diseased Pinus trees in UAV images using deep learning and AdaBoost classifier[J]. Biosystems Engineering, 2020, 194:138-151.
[17] 庞永华, 冀小菊. 基于机器学习的马尾松毛虫发生面积预测模型[J]. 江西农业学报,2019,31(5):55-58.
[18] KURDTHONGMEE W. A comparative study of the effectiveness of using popular DNN object detection algorithms for pith detection in cross-sectional images of parawood[J]. Heliyon, 2020, 6(2):e03480. DOI:10.1016/j.heliyon.2020.e03480
[19] HE T, LIU Y, YU Y, et al. Application of deep convolutional neural network on feature extraction and detection of wood defects[J]. Measurement, 2020, 152:107357. DOI:10.1016/j.measurement.2019.107357
[20] HU J, SONG W, ZHANG W, et al. Deep learning for use in lumber classification tasks[J]. Wood Science and Technology, 2019, 53(2):505-517.
[21] 高琳明, 徐风, 李享, 等. 基于深度学习特征和非线性支持向量机的板材表面缺陷识别方法[J]. 林业工程学报,2019,4(4):99-106.
[22] AFFONSO C, ROSSI A L D, VIEIRA F H A, et al. Deep learning for biological image classification[J]. Expert Systems with Applications, 2017, 85:114-122.
[23] PRZYBY?O J, JAB?O?SKI M. Using Deep Convolutional Neural Network for oak acorn viability recognition based on color images of their sections[J]. Computers and Electronics in Agriculture, 2019, 156:490-499.
[24] HAMRAZ H, JACOBS N B, CONTRERAS M A, et al. Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 158:219-230.
[25] WEINSTEIN B G, MARCONI S, BOHLMAN S, et al. Individual Tree-Crown detection in RGB imagery using Semi-Supervised Deep Learning Neural Networks[J]. Remote Sensing, 2019, 11(11):1309. DOI:10.3390/rs 11111309
[26] 边黎明, 张慧春. 表型技术在林木育种和精确林业上的应用[J]. 林业科学,2020,56(6):113-126.

发布日期 2021-03-30

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