
The status and prospects of smart forestry
CAO Lin, ZHOU Kai, SHEN Xin, YANG Xiaoming, CAO Fuliang, WANG Guibin
JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2022, Vol. 46 ›› Issue (6) : 83-95.
The status and prospects of smart forestry
Information technology is an important driving force for the development of human civilization, and it is also one of the fastest growing and most influential high-techs in the world today. With the wide application of modern information technology in the field of forestry, smart forestry has become the route one must take for the development of modern forestry. Smart forestry is the deep integration of new generation information technologies such as Internet of Things, big data, cloud computing, artificial intelligence, mobile Internet and 3S technology, intelligent equipment as well as forestry production and management businesses such as and forest breeding, forest cultivation, forest management and forest protection. The development of smart forestry in China is a milestone in the development of modern forestry. The author introduces the background, connotation, characteristics, theoretical basis and research methods of smart forestry, as well as top-level design, project deployment and implementation, scientific research platform construction and talent training for smart forestry development; the research status of key core technologies of intelligent forestry, such as forestry intelligent perception, spatial information technology, big data and cloud computing, virtual reality and intelligent equipment technology, are systematically introduced. The application progress of smart forestry in forest tree genetics and breeding, forest precision silviculture, forest resource monitoring and management decision-making, forest fire monitoring and prediction, pest control, and wildlife protection were further introduced. Finally, the development goals of smart forestry in the future are pointed out, and the main development directions of smart forestry technology system are prospected. The author believes that the development of smart forestry needs to further promote the research, development and application of intelligent algorithms and hardware, as well as to strengthen the research of its theoretical basis. At the same time, it is also necessary to integrate modern data mining, model simulation and intelligent analysis technologies into the forestry production business process on the basis of obtaining accurate multi-source data, to provide services for the whole industry chain of forestry production and lead the high-quality development of forestry.
smart forestry / forestry informatization / artificial intelligence / phenotyping of forest trees / forest silviculture and monitoring / forest management decision / forest protection and disaster prevention
[1] |
褚君浩. 科技创新开拓智能时代[J]. 科技导报, 2019, 37(2):31-33.
|
[2] |
江泽慧. 论林业新科技革命[J]. 世界林业研究, 1999, 12(4):1-5.
|
[3] |
李世东. 论第六次信息革命[J]. 中国新通信, 2014, 16(14):3-6.
|
[4] |
陈述彭. 数字地球:挑战与思考[J]. 遥感信息, 1999, 14(2):2-4.
|
[5] |
冯仲科, 张晓勤. 发展我国的数字林业体系[J]. 北京林业大学学报, 2000, 22(5):102-103.
|
[6] |
陈述彭, 程维明. 世界森林的数字地球监测[J]. 遥感学报, 2001, 5(5):321-326,401.
|
[7] |
李增元, 张怀清, 陆元昌. 数字林业建设与进展[J]. 中国农业科技导报, 2003, 5(2):7-9.
|
[8] |
李德仁. 展望大数据时代的地球空间信息学[J]. 测绘学报, 2016, 45(4):379-384.
|
[9] |
李世东. 中国智慧林业路线图[J]. 林业经济, 2014, 36(10):54-57.
|
[10] |
徐国祯. 森林的系统观与林业系统工程[J]. 林业资源管理, 1988(4):16-22.
|
[11] |
|
[12] |
|
[13] |
|
[14] |
SFI. SmartForest Bringing Industry 4.0 to the Norwegian forest sector annual report 2020[R]. HØGSKOLEVEIEN, Norway: SFI SmartForest, 2020.
|
[15] |
恭映璧. 推进以智慧林业为主导的林业创新——战后日本林业发展及其启示(十二)[J]. 林业与生态, 2021(5):22-26.
|
[16] |
中国林草年度盘点:改革创新,变局中开新局[N].2021-01-23(07).
|
[17] |
李世东. 智慧林业几个基本问题的探讨[J]. 林草政策研究, 2021, 1(3):24-31.
|
[18] |
吴振江, 李俊枝, 李顺龙. “互联网+”智慧林业的发展策略[J]. 东北林业大学学报, 2019, 47(5):105-107,117.
|
[19] |
国家林业局. 关于加快中国林业大数据发展的指导意见[EB/OL].[2016-07-13](2022-09-20). http://www.forestry.gov.cn/.
|
[20] |
国家林业和草原局. 国家林业和草原局关于促进林业和草原人工智能发展的指导意见[J]. 自然资源通讯, 2019(23):11-14.
|
[21] |
杨秀好, 秦江林, 李有海, 等. 林业有害生物灾害监测预警与应急防控关键技术研究与应用[Z]. 南宁:广西壮族自治区林业有害生物防治检疫站, 2016.
|
[22] |
贵阳市林业局. 林业+大数据!贵阳智慧林业云平台实现智能管理[N]. 潇湘晨报,2021-12-02(1).
|
[23] |
“5G+智慧林业”上线全力守护绿水青山[Z]. 广西移动, 2022-08-02.
|
[24] |
教育部办公厅. 关于公布新农科研究与改革实践项目的通知[Z].(2020-09-08)[2022-09-04]. http://www.edu.gov.cn.
|
[25] |
教育部. 关于公布2021年度普通高等学校本科专业备案和审批结果的通知[Z].(2021-12-10)[2022-09-04]. http://www.edu.gov.cn.
|
[26] |
|
[27] |
|
[28] |
|
[29] |
|
[30] |
|
[31] |
|
[32] |
|
[33] |
高德民, 林海峰, 刘云飞, 等. 基于无线传感网的森林火灾FWI系统分析[J]. 林业科技开发, 2015, 29(1):105-109.
|
[34] |
刘海洋, 于新文, 张旭. 基于无线传感网的森林环境因子采集系统性能测试[J]. 林业科技通讯, 2016(1):68-70.
|
[35] |
周成虎. 全空间地理信息系统展望[J]. 地理科学进展, 2015, 34(2):129-131.
|
[36] |
骆剑承, 周成虎, 梁怡, 等. 时空数据智能化处理与分析的理论和方法探讨[J]. 中国图象图形学报, 2001, 6(9):836-841.
|
[37] |
庞丽峰, 黄水生, 李万里, 等. 全球卫星系统在我国林业中的应用[J]. 世界林业研究, 2019, 32(5):41-46.
|
[38] |
杨元喜, 汤静. 智慧城市与北斗卫星导航系统[J]. 卫星应用, 2014(2):7-10.
|
[39] |
杨必胜, 陈驰, 董震. 面向智能化测绘的城市地物三维提取[J]. 测绘学报, 2022, 51(7):1476-1484.
|
[40] |
刘春, 贾守军, 吴杭彬, 等. 点云场景认知模式:泛化点云[J]. 测绘学报, 2022, 51(4):556-567.
|
[41] |
黄华国. 林业定量遥感研究进展和展望[J]. 北京林业大学学报, 2019, 41(12):1-14.
|
[42] |
|
[43] |
|
[44] |
|
[45] |
|
[46] |
|
[47] |
|
[48] |
吴保国, 温亮宝. 森林重大病虫害诊治专家咨询系统的研究[J]. 北京林业大学学报, 2006, 28(6):113-118.
|
[49] |
张钹. 人工智能进入后深度学习时代[J]. 智能科学与技术学报, 2019, 1(1):4-6.
|
[50] |
|
[51] |
周焱, 刘文萍, 骆有庆, 等. 基于深度学习的小目标受灾树木检测方法[J]. 林业科学, 2021, 57(3):98-107.
|
[52] |
余茂源. 智慧林业的关键技术及其应用策略研究[J]. 林业调查规划, 2017, 42(5):121-123,131.
|
[53] |
|
[54] |
刘海, 张怀清, 林辉. 森林经营可视化管理系统设计与实现[J]. 科技资讯, 2010, 8(17):240-242.
|
[55] |
王万富, 王琢, 刘佳鑫, 等. 基于Qt/Embedded的农林智能装备导航定位算法研究及软件设计[J]. 国外电子测量技术, 2022, 41(3):63-68.
|
[56] |
刘延鹤, 傅万四, 张彬, 等. 林业机器人发展现状与未来趋势[J]. 世界林业研究, 2020, 33(1):38-43.
|
[57] |
罗梅, 刘延鹤, 蒋鹏飞, 等. 我国林业装备的发展现状及未来趋势[J]. 林业机械与木工设备, 2021, 49(1):8-11,17.
|
[58] |
|
[59] |
|
[60] |
|
[61] |
|
[62] |
|
[63] |
蔡硕, 邢艳秋, 端木嘉龙. 背包式激光雷达滤除低强度点云提取林木胸径[J]. 森林工程, 2021, 37(5):12-19.
|
[64] |
|
[65] |
张慧颖. 现场环境智能巡检机器人设计[J]. 现代电子技术, 2016, 39(18):135-138.
|
[66] |
姜树海, 张楠. 六足仿生森林消防机器人机构设计与分析[J]. 机械设计与制造, 2015(12):208-212.
|
[67] |
魏占国, 刘晋浩. 轮式林木联合采伐机底盘的设计与研究[J]. 广西大学学报(自然科学版), 2010, 35(2):263-268.
|
[68] |
|
[69] |
|
[70] |
|
[71] |
|
[72] |
|
[73] |
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.
|
[74] |
|
[75] |
|
[76] |
|
[77] |
|
[78] |
|
[79] |
|
[80] |
|
[81] |
|
[82] |
|
[83] |
|
[84] |
张峰, 周广胜. 植被含水量高光谱遥感监测研究进展[J]. 植物生态学报, 2018, 42(5):517-525.
|
[85] |
|
[86] |
|
[87] |
师志刚, 刘群昌, 白美健, 等. 基于物联网的水肥一体化智能灌溉系统设计及效益分析[J]. 水资源与水工程学报, 2017, 28(3):221-227.
|
[88] |
陈尔学, 刘健, 王晓慧. 重点防护林工程监测技术[M]. 北京: 中国林业出版社, 2012.
|
[89] |
李硕明. 一种基于物联网技术的森林资源监测系统[J]. 物联网技术, 2016, 6(5):11-13,16.
|
[90] |
冷天熙, 钱发斌, 胡文萍. 基于人工智能深度学习的卫星影像分类研究[J]. 林业调查规划, 2021, 46(1):1-4.
|
[91] |
尹丽丽. 黑龙江省森林资源监测遥感判读平台建设[J]. 林业勘查设计, 2021, 50(1):67-70.
|
[92] |
张民侠, 郑怀兵. 基于智慧感知、分析、处置方法的森林智慧防火[J]. 南京理工大学学报, 2016, 40(6):694-699.
|
[93] |
|
[94] |
|
[95] |
|
[96] |
|
[97] |
|
[98] |
张宇, 李丽, 吴巩胜, 等. 基于生境斑块的滇金丝猴景观连接度分析[J]. 生态学报, 2016, 36(1):51-58.
|
[99] |
张超. 基于“3S”技术的大熊猫潜在栖息地研究:以平武县小河沟自然保护区为例[D]. 成都: 成都理工大学, 2016.
|
[100] |
申凤伟. 基于物联网构架的野生动物监测信息管理系统研究[D]. 武汉: 武汉理工大学, 2012.
|
[101] |
宫一男, 谭孟雨, 王震, 等. 基于深度学习的红外相机动物影像人工智能识别:以东北虎豹国家公园为例[J]. 兽类学报, 2019, 39(4):458-465.
|
[102] |
骆沙鸣. 应进一步提高我国森林质量和森林生态服务功能[J]. 中国产业, 2011(7):7.
|
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〈 |
|
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