[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2603":3,"related-2603":74},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":30,"secondary_topics":35,"classification":36,"search_phrases":68,"slug":71,"view_count":72,"doi":8,"paper":8,"created_at":73},2603,"第31届中国农学会计算机农业应用分会学术年会在哈尔滨成功举办","https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Fce25177e9d4049a5a2eb98536b35abbd.htm","9月4日至6日由中国农学会计算机农业应用分会主办，黑龙江省农业科学院农业遥感与信息研究所承办，农业大数据学报协办。会议围绕人工智能+农业主线安排了17场专题学术报告。来自全国40家单位的131位专家学者、科研人员和企业代表参会。",null,"[![Image 2](https:\u002F\u002Faii.caas.cn\u002Fimages2022\u002Flogo_01.png)](https:\u002F\u002Faii.caas.cn\u002Findex.htm)[![Image 3](https:\u002F\u002Faii.caas.cn\u002Fimages2022\u002Flogo_02.png)](https:\u002F\u002Faii.caas.cn\u002Findex.htm)\n\n[中国农业科学院](https:\u002F\u002Fwww.caas.cn\u002F)∣ [数字农科院](https:\u002F\u002Fi.caas.cn\u002F)∣ [邮箱](https:\u002F\u002Fmail.caas.cn\u002F)∣ [](javascript:void(0))\n\n*   [首页](https:\u002F\u002Faii.caas.cn\u002Findex.htm)\n*   [新闻动态](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Findex.htm)\n\n[图片新闻](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Fce25177e9d4049a5a2eb98536b35abbd.htm)[更多 >](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Ftpxw\u002Findex.htm) \n    *   [![Image 4](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2026-08\u002F5cc2b429f798427b8914f0689aa40634.png)信息所举办APEC“数智赋能亚太农食系统转型”项目研讨会](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Ftpxw\u002Faa30c89abd494614a1485fc6ead419e9.htm)\n    *   [![Image 5](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2026-07\u002Fbb5f61d4cd9146108354f6baeb1c1048.png)中非现代农业发展经验交流研修班顺利举办](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Fedcdf966eef94ff78ff467fb2bf9caca.htm)\n\n[综合动态](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Fce25177e9d4049a5a2eb98536b35abbd.htm)[更多 >](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Findex.htm)\n\n    *   [09-09 凝聚产学研智慧 赋能奶业高质量发展](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Fe2bf3408476243bab208f43609910f43.htm)\n    *   [09-09 中国农业大学图书馆赴中国农科院信息所调研交流](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Ff18938e1558d4405825b26edaa1061b2.htm)\n    *   [09-08 信息所召开国有资产清查盘点工作宣讲会](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Fa441333180454342b1562746958b3962.htm)\n    *   [09-07 第四届农业科学数据研讨会在宁夏银川召开](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002F3150673eb41e480591419610e8ec8645.htm)\n    *   [08-28 福建省农科院数字农业研究所赴信息所交流座谈](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002F2bb1098d98e74a3b87cd61f9079b8bbc.htm)\n    *   [08-27 中国热科院科技信息所赴中国农科院信息所调研交流](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002F433bb0bd6a184a949a29889f57ab8f19.htm)\n\n    *   [科研进展](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fkyjz\u002Findex.htm)\n    *   [合作交流](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fhzjl\u002Findex.htm)\n    *   [支撑服务](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzcfw\u002Findex.htm)\n    *   [党政工作](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fdzgz\u002Findex.htm)\n\n*   [本所概况](https:\u002F\u002Faii.caas.cn\u002Fbsgk\u002Findex.htm)\n\n[本所简介](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Fce25177e9d4049a5a2eb98536b35abbd.htm)[更多 >](https:\u002F\u002Faii.caas.cn\u002Fbsgk\u002Fbsjj\u002Findex.htm) ![Image 6](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-05\u002Fa071044471af4ed98e58d330b9ce0d30.jpg) **一、历史沿革与发展定位**\n\n中国农业科学院农业信息研究所（简称信息所）由原中国农业科学院科技情报研究所（1957年成立）、中国农业科学院图书馆（1957年成立）、中国农业科学院计算中心（1981年成立）、中国农业科学院农业宏观研究室（1992年成立）等独立机构逐步整合而成。是以农业信息科技创新和农业科技信息服务为主要任务的国家级非营利性科研机构。\n\n信息所按照“面向世界科技前沿、面向经济主战场、面向国家重大需求、面向人民生命健康”的要求，秉承 “服务立所、创新强所”的宗旨，以信息化支撑农业现代化发展的需求为导向，以学科建设、人才团队建设、条件平台建设为手段，明确研究方向，优化学科布局，整合科技资源，全面提升科技创新能力，引领农业信息科技发展，为国家现代农业发展提供科技支撑与服务。\n\n**二、机构设置与人员情况**\n\n信息所内设机构34个，包括21个业务部门，6个专项工作办公室、6个职能部门、1个后勤保障部门。拥有国家农业图书馆、国家农业科学数据中心，公开出版学术和行业类期刊12种。\n\n信息所现有人员873人，包括在职职工308人、离退休职工225余人、长期聘用人员184人、在读研究生156人（含留学生）。在职职工中，高级职称占40%，拥有博硕士学位人员占81%。\n\n**三、学科建设与科技创新**\n\n信息所关注农业信息分析、农业信息技术、农业信息管理等学科发展，致力于实现农业信息领域的公益服务、决策咨询、科研创新能力的整体提升。主要包括以下三大学科领域：\n\n（一）农业信息分析研究。面向农业发展战略研究需求，聚焦海外农业研究、信息监测预警、农业风险分析、农业科技情报等方面的重大科学问题，建设具有全球视野、学科融合、专业化手段的高端农业智库，为科学智能决策和应急服务提供支撑。\n\n（二）农业信息技术研究。面向智慧农业重大需求，聚焦智能管控、视觉感知、物联网、空间数据、区块链等领域关键技术问题，开展智慧农业技术创新及在农业农村全产业链中的集成应用，为现代农业建设提供系统技术与产品。\n\n（三）农业信息管理研究。面向农业农村大数据应用需求，聚焦机器学习、人工智能、知识组织、云计算等大数据关键技术，搭建农业领域大数据并行计算环境及实时计算服务平台，建立基于大数据驱动的信息技术体系，为农业科技发展、农业农村经济建设提供及时、准确、科学的数据与技术服务。\n\n**四、文献与信息化支撑服务**\n\n信息所肩负着国家农业图书馆建设与服务的重任，是国家科技图书文献中心农业分馆，牵头全国农业科研系统电子资源共建共享体系建设工作，为全院及全国的农业科技创新、管理决策和经济发展提供高质量科技文献保障和知识服务。国家农业图书馆现有馆藏文献210万余册，其中图书31万余册，古农书、地方志等15000余册。订购中外文科技期刊4000余种，其中1100余种是全国的孤本；引进中外文电子文献数据库80余种，涵盖12000余种中文全文期刊，9500余种外文电子期刊。\n\n信息所作为中国农业科学院信息化基础网络及公共服务平台技术支撑部门，承担京区院机关和各研究所的网络接入、院所两级“1+36”门户网站群系统开发与运维、电子邮件系统管理与维护、ARP系统建设等工作。通过建设国内（外）一流的科研信息生态环境系统，为农业科研管理、科技创新提供安全、稳定、高效的农业信息化基础设施和技术服务。\n\n信息所共编辑出版12种科技期刊，包括《中国农业科学》、《Journal of Integrative Agriculture》（英文）、《生物技术通报》、《aBIOTECH》（英文）、《农业展望》、《中国乳业》、《农业科技通讯》、《中国猪业》、《智慧农业》、《农业图书情报学报》、《农业大数据学报》、《中国畜禽种业》，其中《中国农业科学》、《Journal of Integrative Agriculture》、《生物技术通报》入选中国科协“卓越计划”。上述期刊全面反映现代农业发展、农业科学研究及其相关领域的最新进展和最新科技成果，在促进农业科技创新和国际交流中发挥着重要作用。\n\n**五、科研与教育条件**\n\n信息所现有省部级重点实验室4个，包括农业农村部农业监测预警技术重点实验室、农业农村部农业大数据重点实验室、国家新闻出版署农业融合出版知识挖掘与知识服务重点实验室、农业农村部区块链农业应用重点实验室。4个全国性学术机构挂靠信息所，包括中国农学会农业图书情报、中国农学会计算机农业应用分会、中国农学会中国农业现代化研究会、中国农学会农业监测预警分会。 牵头“农业大数据与信息服务联盟”，该联盟是“国家农业科技创新联盟”框架下的专业联盟。研究所充分发挥这些学术平台的作用，紧密围绕科技创新和公益服务两大中心任务，联合同行专家开展了一系列学术和宣传培训活动，影响力不断增强。\n\n国际合作方面，信息所先后与英国、加拿大、荷兰、美国、韩国、日本、俄罗斯、FAO、CABI等10多个国家和国际组织进行了农业信息科技合作，“十三五”期间签订国际信息科技合作协议20余份。信息所连续多年为我院参与APEC、G20、金砖国家等国际会议机制提供研究支撑，与WFP、FAO、IFAD、CGIAR等国际组织开展项目合作，与AGRA、FARA等区域组织建立了长效合作机制。\n\n研究生培养方面，作为中国农业科学院研究生院培养单位之一，承担农业信息学科体系的教学与研究生培养工作。现有农业信息分析学、农业经济管理、农业信息管理、信息技术与数字农业4个博士学位授予点，管理科学与工程、农业经济管理、图书情报（专业硕士）、农业管理（专业硕士）、农业工程与信息技术（专业学位）5个硕士学位授予点，建立了博士后流动站。每年在读硕、博士研究生150余人，包括近10个国家的留学生。经过多年的发展，为我国农业信息与管理学科领域培养了大批优秀人才，极大地推动了农业信息学科建设与发展。  \n\n[现任领导](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Fce25177e9d4049a5a2eb98536b35abbd.htm)[更多 >](https:\u002F\u002Faii.caas.cn\u002Fbsgk\u002Fxrld\u002Findex.htm) \n    *   [![Image 7](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2025-03\u002Fbdc69e2cbe204d0f9c7fd3f80fa4b2fd.jpg)代辛 副所长、党委副书记](https:\u002F\u002Faii.caas.cn\u002Fbsgk\u002Fxrld\u002Fb5ea3ac130424ecb86d39ff9039f58e0.htm)\n    *   [![Image 8](https:\u002F\u002Faii.caas.cn\u002Fimages\u002Fcontent\u002F2019-09\u002F20190920164045033705.jpg)张蕙杰 党委副书记、纪委书记](https:\u002F\u002Faii.caas.cn\u002Fbsgk\u002Fxrld\u002F31ea3eef81ff45869321b7afedb0c494.htm)\n    *   [![Image 9](https:\u002F\u002Faii.caas.cn\u002Fimages\u002Fcontent\u002F2019-09\u002F20190920164610271896.jpg)赵瑞雪 党委委员、副所长](https:\u002F\u002Faii.caas.cn\u002Fbsgk\u002Fxrld\u002F9b64b70012a9442eacdeddb31b47eec1.htm)\n    *   [![Image 10](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2026-07\u002F63ee738b6c4d44feab5f8c36668f16fb.jpg)李慧慧 副所长](https:\u002F\u002Faii.caas.cn\u002Fbsgk\u002Fxrld\u002Fa9b895c6d52b46589596eb17bbb31a8b.htm)\n\n    *   [本所章程](https:\u002F\u002Faii.caas.cn\u002Fbsgk\u002Fbszc\u002Findex.htm)\n    *   [历届领导](https:\u002F\u002Faii.caas.cn\u002Fbsgk\u002Fljld\u002Findex.htm)\n    *   [组织机构](https:\u002F\u002Faii.caas.cn\u002Fbsgk\u002Fzzjg\u002Findex.htm)\n    *   [学术团体](https:\u002F\u002Faii.caas.cn\u002Fbsgk\u002Fxstt\u002Findex.htm)\n\n*   [科学研究](https:\u002F\u002Faii.caas.cn\u002Fkxyj\u002Findex.htm)\n\n[学科建设](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Fce25177e9d4049a5a2eb98536b35abbd.htm)[更多 >](https:\u002F\u002Faii.caas.cn\u002Fkxyj\u002Fxkjs\u002Findex.htm) ![Image 11](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-05\u002F26e5e90c69c34149962aa549bbfd8271.jpg)\n\n中国农业信息研究所是以农业信息学科研创新、产品开发、应用推广为主体科研任务的公益性单位。经过多年的科研实践和研究积累，特别是近十年来，在农业信息分析、农业信息技术和农业信息管理三大学科领域加强了科研创新，取得一批的科研成果，在全国农业信息科研领域处于领导地位。  \n\n[创新团队](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Fce25177e9d4049a5a2eb98536b35abbd.htm)[更多 >](https:\u002F\u002Faii.caas.cn\u002Fkxyj\u002Fcxtd\u002Findex.htm) \n    *   [![Image 12](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-06\u002F0f67069ff6aa41e2888d0068f7a5a6fe.jpg) 农业监测预警创新团队](https:\u002F\u002Faii.caas.cn\u002Fkxyj\u002Fcxtd\u002F4c070fa671264c2ba890d27bfd1d9d18.htm)\n    *   [![Image 13](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-06\u002F3908e8e07ff94cb78b06cbdb7231ab1b.jpg) 海外农业与国际情报创新团队](https:\u002F\u002Faii.caas.cn\u002Fkxyj\u002Fcxtd\u002F5a9f982c0bc7497e9108cd489c04d1e9.htm)\n    *   [![Image 14](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-05\u002Fdea114e8767743cfbca1ae58ee4ed132.jpg) 大数据与知识服务创新团队](https:\u002F\u002Faii.caas.cn\u002Fkxyj\u002Fcxtd\u002F39c7cbc0cef6414db1c91e20e3808445.htm)\n    *   [![Image 15](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-05\u002F9d63c728ee594ec6be72b5c6f9f45737.jpg) 科技情报分析与评估创新团队](https:\u002F\u002Faii.caas.cn\u002Fkxyj\u002Fcxtd\u002Fa3d155e75e354bcf9dbdb4c689e92803.htm)\n    *   [![Image 16](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-05\u002Fce7e288bb7f942578422c6e4fbe7b3c1.jpg) 农业物联网技术与服务创新团队](https:\u002F\u002Faii.caas.cn\u002Fkxyj\u002Fcxtd\u002Fc9c1ced4342841c782249656e859fe07.htm)\n    *   [![Image 17](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-05\u002F770f72cadc274dc5a25aff5c01609cf2.jpg) 农业认知计算创新团队](https:\u002F\u002Faii.caas.cn\u002Fkxyj\u002Fcxtd\u002Fa3861464dda846eb9768d31f54a73578.htm)\n    *   [![Image 18](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-06\u002F33e1b115825648ee895b020a40b999bf.jpg) 农业智能管控与区块链应用创新团队](https:\u002F\u002Faii.caas.cn\u002Fkxyj\u002Fcxtd\u002Fbfdde067c11b4722ada08587975331f6.htm)\n    *   [![Image 19](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-05\u002Fdebc3b2f50064ab89f074f2beb3ed2ab.jpg) 机器视觉与农业机器人创新团队](https:\u002F\u002Faii.caas.cn\u002Fkxyj\u002Fcxtd\u002Ff6ef243a5a4a4c4d88e96ea291aeca60.htm)\n    *   [![Image 20](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-05\u002Fe044f3472bcc4212ab0bb500e4dc7f63.jpg) 农业农村资源数字化管理创新团队](https:\u002F\u002Faii.caas.cn\u002Fkxyj\u002Fcxtd\u002Fda4c6cb6b2d14c6b817197c45a1f705a.htm)\n\n[科研平台](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Fce25177e9d4049a5a2eb98536b35abbd.htm)[更多 >](https:\u002F\u002Faii.caas.cn\u002Fkxyj\u002Fkypt\u002Findex.htm)\n\n    *   [![Image 21](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-07\u002F86256245c3a24553af5c77d2391b48b0.jpg)国家农业科学数据中心](https:\u002F\u002Faii.caas.cn\u002Fkxyj\u002Fkypt\u002F636955e4bde84a408c85038448c7014b.htm)\n    *   [![Image 22](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-07\u002F0d3a39ac0a6d4b57ba2052355cfa5a9d.jpg)北京市农业监测预警工程技术研究中心](https:\u002F\u002Faii.caas.cn\u002Fkxyj\u002Fkypt\u002F6276870f5ce747e5b5be8abaf6e3ec2f.htm)\n    *   [![Image 23](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-07\u002F2f5317d9b5924ba994fafb59cc3f8ac1.jpg)农业农村部区块链农业应用重点实验室](https:\u002F\u002Faii.caas.cn\u002Fkxyj\u002Fkypt\u002F0f4863ee057d4eafae3ec28e60cf9f56.htm)\n    *   [![Image 24](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-07\u002Fcef25edab2a64e7cab6617b7551a2173.jpg)农业农村部农业监测预警技术重点实验室](https:\u002F\u002Faii.caas.cn\u002Fkxyj\u002Fkypt\u002F860fedf215b14dde8c641553f8d1ed2f.htm)\n    *   [![Image 25](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-05\u002F05dcf6f2fcef48378df4eefc071026d6.jpg)出版业科技与标准重点实验室](https:\u002F\u002Faii.caas.cn\u002Fkxyj\u002Fkypt\u002Fe246a5f5adf6447b94db3599b9fb93bb.htm)\n    *   [![Image 26](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-05\u002F96da6399eb344111bbc9ae6f56200a4e.jpg)农业农村部农业大数据重点实验室](https:\u002F\u002Faii.caas.cn\u002Fkxyj\u002Fkypt\u002F4f7cdcd5d5d945babb9ab9a9b1ba0486.htm)\n\n*   [成果转化](https:\u002F\u002Faii.caas.cn\u002Fcgzh\u002Findex.htm)\n\n[概要介绍](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Fce25177e9d4049a5a2eb98536b35abbd.htm) 中国农业科学院农业信息研究所是以农业信息学科研创新、产品开发、应用推广为主体科研任务的公益性单位。经过多年的科研实践和研究积累，特别是近十年来，在农业信息分析、农业信息技术和农业信息管理三大学科领域加强了科研创新，取得一批的科研成果，在全国农业信息科研领域处于领先地位。  \n\n    *   [![Image 27](https:\u002F\u002Faii.caas.cn\u002Fimages2022\u002Ficon12.png)获奖成果](https:\u002F\u002Faii.caas.cn\u002Fcgzh\u002Fhjcg\u002Findex.htm)\n    *   [![Image 28](https:\u002F\u002Faii.caas.cn\u002Fimages2022\u002Ficon13.png)专利标准](https:\u002F\u002Faii.caas.cn\u002Fcgzh\u002Fzlbz\u002Findex.htm)\n    *   [![Image 29](https:\u002F\u002Faii.caas.cn\u002Fimages2022\u002Ficon14.png)产品展示](https:\u002F\u002Faii.caas.cn\u002Fcgzh\u002Fcpzs\u002Findex.htm)\n    *   [![Image 30](https:\u002F\u002Faii.caas.cn\u002Fimages2022\u002Ficon15.png)规划咨询](https:\u002F\u002Faii.caas.cn\u002Fcgzh\u002Fghzx\u002Findex.htm)\n\n*   [人才队伍](https:\u002F\u002Faii.caas.cn\u002Frcdw\u002Findex.htm)\n\n    *   [杰出人才](https:\u002F\u002Faii.caas.cn\u002Frcdw\u002Fgjrc\u002Findex.htm)\n    *   [首席专家](https:\u002F\u002Faii.caas.cn\u002Frcdw\u002Fsxzj\u002Findex.htm)\n    *   [高级职称](https:\u002F\u002Faii.caas.cn\u002Frcdw\u002Fgjzc\u002Findex.htm)\n\n*   [国际合作](https:\u002F\u002Faii.caas.cn\u002Fgjhz\u002Findex.htm)\n\n![Image 31](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-07\u002F46dd0e7fc1654e0fab8f59ce50da71ce.jpg) 中国农业科学院农业信息研究所围绕国家重大需求，紧密结合世界信息学科发展趋势，在农业信息关键技术、农业宏观战略研究、国家高端智库、图书文献信息、网络支撑服务、期刊出版与信息传播等领域开展国际科技合作与交...  \n\n    *   [国际交流](https:\u002F\u002Faii.caas.cn\u002Fgjhz\u002Fgjhzgjjl\u002Findex.htm)\n    *   [合作伙伴](https:\u002F\u002Faii.caas.cn\u002Fgjhz\u002Fgjhzhzhb\u002Findex.htm)\n    *   [海外农业研究中心](https:\u002F\u002Faii.caas.cn\u002Fgjhz\u002Fgjhzhwnyyjzx\u002Findex.htm)\n\n*   [研究生培养](https:\u002F\u002Faii.caas.cn\u002Fyjspy\u002Findex.htm)\n\n[总体介绍](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Fce25177e9d4049a5a2eb98536b35abbd.htm)[更多 >](https:\u002F\u002Faii.caas.cn\u002Fyjspy\u002Fztjs\u002Findex.htm) ![Image 32](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-05\u002F71806c377cc64ef387bbe26e1e887ef8.jpg)\n\n中国农业科学院农业信息研究所(以下简称信息所)，是以大数据驱动下的农业信息科技创新与服务为主要方向的国家级非营利性科研机构，确立了数据科学、农业信息管理、农业信息技术、农业信息分析四大学科领域的农业信息学科体系，是全国农业信息技术创新中心、国家农业科学数据中心。  \n\n[招生信息](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Fce25177e9d4049a5a2eb98536b35abbd.htm)[更多 >](https:\u002F\u002Faii.caas.cn\u002Fyjspy\u002Fzsxx\u002Findex.htm) \n    *   [07-31 中国农业科学院农业信息研究所2027年接收推荐免试研究生公告](https:\u002F\u002Faii.caas.cn\u002Fyjspy\u002Fzsxx\u002F3023e1309e3b4e29aaf9b38819ce4888.htm)\n    *   [07-02 中国农业科学院信息所关于举办2026年优秀大学生云端夏令营活动的通知](https:\u002F\u002Faii.caas.cn\u002Fyjspy\u002Fzsxx\u002F804ed31cbef34bf09eac5904b6ec4f7c.htm)\n    *   [06-10 中国农业科学院农业信息研究所2026年博士“申请-考核制”招生拟录取名单公示（第一轮、第二轮补招）](https:\u002F\u002Faii.caas.cn\u002Fyjspy\u002Fzsxx\u002F895eba3e82a44df8b7e00c64eb0aca16.htm)\n    *   [06-04 中国农业科学院农业信息研究所2026年博士招生第二轮补充报名拟进入学科复核人员名单](https:\u002F\u002Faii.caas.cn\u002Fyjspy\u002Fzsxx\u002Fee565febd0cc45e6b794be92b4a7a02a.htm)\n    *   [05-13 中国农业科学院农业信息研究所2026年博士招生第一轮补充报名拟进入学科复核人员名单](https:\u002F\u002Faii.caas.cn\u002Fyjspy\u002Fzsxx\u002F2f9c13f1d5bf40f689382b6dd2da4cb6.htm)\n    *   [05-09 农业信息研究所2026年上半年研究生学位论文答辩安排公示](https:\u002F\u002Faii.caas.cn\u002Fyjspy\u002Fzsxx\u002F4345d49894984b8b80c560b8586e970d.htm)\n    *   [04-30 农业信息研究所2026年博士研究生“申请-考核制”拟录取名单公示](https:\u002F\u002Faii.caas.cn\u002Fyjspy\u002Fzsxx\u002F4c4038f5f6724c898d5ba2c4d9935041.htm)\n    *   [04-22 农业信息研究所2026年博士研究生“申请-考核制”学科复核名单](https:\u002F\u002Faii.caas.cn\u002Fyjspy\u002Fzsxx\u002Faecba35a998543669f7e30e0e29fa83a.htm)\n\n    *   [导师介绍](https:\u002F\u002Faii.caas.cn\u002Fyjspy\u002Fdsjs\u002Findex.htm)\n    *   [学科方向](https:\u002F\u002Faii.caas.cn\u002Fyjspy\u002Fxkfx\u002Findex.htm)\n\n  \n\n*   [国家农业图书馆](https:\u002F\u002Fwww.nais.net.cn\u002F)\n*   [科技期刊](https:\u002F\u002Faii.caas.cn\u002Fkjqk\u002Findex.htm)\n\n    *   [![Image 33](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2022-02\u002Fbf539bcf7ced44bc97bd86e3ed994b63.jpg)《中国农业科学》](https:\u002F\u002Faii.caas.cn\u002Fkjqk\u002Fzgnykx\u002Findex.htm)\n    *   [![Image 34](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2025-12\u002F6af6c6d8e8c84503985d9cf5500fc340.png)Journal of Integrative Agriculture](https:\u002F\u002Faii.caas.cn\u002Fkjqk\u002Fnykxxb\u002Findex.htm)\n    *   [![Image 35](https:\u002F\u002Faii.caas.cn\u002Fimages\u002Fcontent\u002F2013-07\u002F20130705161002593440.png)《生物技术通报》](https:\u002F\u002Faii.caas.cn\u002Fkjqk\u002Fswjstb\u002Findex.htm)\n    *   [![Image 36](https:\u002F\u002Faii.caas.cn\u002Fimages\u002Fcontent\u002F2020-09\u002F20200917142930997146.jpg)aBIOTECH](https:\u002F\u002Faii.caas.cn\u002Fkjqk\u002Fswjstb_yw\u002Findex.htm)\n    *   [![Image 37](https:\u002F\u002Faii.caas.cn\u002Fimages\u002Fcontent\u002F2013-07\u002F20130705155738593144.png)《中国乳业》](https:\u002F\u002Faii.caas.cn\u002Fkjqk\u002Fzgry\u002Findex.htm)\n    *   [![Image 38](https:\u002F\u002Faii.caas.cn\u002Fimages\u002Fcontent\u002F2020-09\u002F20200903161155122527.png)《智慧农业（中英文）》](https:\u002F\u002Faii.caas.cn\u002Fkjqk\u002Fzhny\u002Findex.htm)\n    *   [![Image 39](https:\u002F\u002Faii.caas.cn\u002Fimages\u002Fcontent\u002F2019-12\u002F20191216144924836486.jpg)《农业大数据学报》](https:\u002F\u002Faii.caas.cn\u002Fkjqk\u002Fnydsjxb\u002Findex.htm)\n    *   [![Image 40](https:\u002F\u002Faii.caas.cn\u002Fimages\u002Fcontent\u002F2020-05\u002F20200513091311501680.png)《农业图书情报学报》](https:\u002F\u002Faii.caas.cn\u002Fkjqk\u002Fnytsqbxb\u002Findex.htm)\n    *   [![Image 41](https:\u002F\u002Faii.caas.cn\u002Fimages\u002Fcontent\u002F2013-07\u002F20130704150849828882.png)《中国猪业》](https:\u002F\u002Faii.caas.cn\u002Fkjqk\u002Fzgzy\u002Findex.htm)\n    *   [![Image 42](https:\u002F\u002Faii.caas.cn\u002Fimages\u002Fcontent\u002F2013-07\u002F20130705162712171943.png)《农业展望》](https:\u002F\u002Faii.caas.cn\u002Fkjqk\u002Fnyzw\u002Findex.htm)\n    *   [![Image 43](https:\u002F\u002Faii.caas.cn\u002Fimages\u002Fcontent\u002F2013-07\u002F20130705161505265536.jpg)《农业科技通讯》](https:\u002F\u002Faii.caas.cn\u002Fkjqk\u002Fnykjtx\u002Findex.htm)\n    *   [![Image 44](https:\u002F\u002Faii.caas.cn\u002Fimages\u002Fcontent\u002F2013-07\u002F20130704150423906717.png)《中国畜禽种业》](https:\u002F\u002Faii.caas.cn\u002Fkjqk\u002Fzgcqzy\u002Findex.htm)\n\n*   [党群园地](https:\u002F\u002Faii.caas.cn\u002Fdqyd\u002Findex.htm)\n\n[党团活动](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Fce25177e9d4049a5a2eb98536b35abbd.htm)[更多 >](https:\u002F\u002Faii.caas.cn\u002Fdqyd\u002Fdthd\u002Findex.htm) \n    *   [10-11 中国农科院党组成员、纪检组组长周若辉一行到信息所调研](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Ff331e19a098f4f4888dd4d18c51c2139.htm)\n    *   [10-07 支部结对共建 赋能产业振兴](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Fa2f0ec25bd6d438b94414f4545832419.htm)\n    *   [09-28 信息所2023年度研究生会换届选举顺利完成](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002F96a125c051404d1195a3e365bb8af129.htm)\n    *   [09-21 加强党性锤炼，筑牢理想信念](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Fcec6d1d21d6b4dc49d21e701794f20ff.htm)\n    *   [08-17 铸魂筑牢根本 胸怀国之大者](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002F80ff57c449a14809aa18892a78917cc9.htm)\n    *   [07-26 走进红色基地 淬炼党性修养](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002F8a15df628f5742278358facc2c2c2ed6.htm)\n\n[文化建设](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Fce25177e9d4049a5a2eb98536b35abbd.htm)[更多 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_小_】\n\n2026年9月4日至6日，第31届中国农学会计算机农业应用分会学术年会在黑龙江哈尔滨举行。会议以发展农业新质生产力，推进“人工智能+农业”为主题，来自全国40家单位的131位专家学者、科研人员和企业代表齐聚一堂，围绕农业人工智能前沿技术、农业数据要素体系建设、智慧农业应用实践等议题深入研讨，共商人工智能与农业深度融合的路径与方向。\n\n本次年会由中国农学会计算机农业应用分会主办，黑龙江省农业科学院农业遥感与信息研究所承办，农业大数据学报协办。会议深入贯彻落实中央关于发展农业新质生产力的战略部署，直面智慧农业落地过程中技术瓶颈、场景适配等现实难题，积极推进“人工智能+”行动在农业领域落地见效。\n\n突出学术引领，研判行业前沿。会议围绕“人工智能+农业”主线安排了17场专题学术报告，系统呈现了智慧农场、智能育种、土壤AI诊断、病虫害智能识别与监测预警、面向作物管理的农业世界模型等前沿技术方向的最新进展，深入探讨了农业高质量数据集建设、数据要素流通与价值发现、算力高效配置等基础性、前沿性课题，为农业人工智能领域的学术研究和学科发展提供了重要交流平台。\n\n强化科研创新，展示最新成果。来自全国科研院所、涉农高校和企业的科技工作者，交流了覆盖农业生产全链条的科研进展、技术成果与应用案例，内容涵盖智能感知与识别、多模态农业世界模型与知识服务、智慧种植养殖、算力与平台支撑等多个层面，充分展现了我国计算机农业应用领域科研创新的活跃态势。\n\n深化行业指导，服务产业发展。会议聚焦数据要素价值化、农事综合服务、智慧农企建设等产业发展中的现实问题，交流了智慧化现代农事综合服务中心建设、AI赋能的农业智能服务与场景打造、新型生产关系与新质生产力协同推进智慧农业等实践经验，为农业生产经营主体数字化转型提供了可借鉴、可复制的路径。会议还组织代表实地参观黑龙江省农业科学院大数据中心、黑龙江省土壤样本库黑土馆。\n\n会议期间召开分会常务委员会会议，会议传达学习了习近平总书记在“科技三会”上的重要讲话精神，学习了民政部、中国科协关于社会团体分支机构管理的文件要求，总结了分会年度工作，研究讨论了下一阶段重点任务。与会委员一致表示，将以此次年会为契机，深化协同创新与成果共享，推动人工智能与农业深度融合，为农业新质生产力发展和乡村全面振兴贡献科技力量。\n\n![Image 50: 31届新闻图片1000px.png](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2026-09\u002F0ff9ea56921c4ae3b97a837c859056c9.png)\n\n[打印本页](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Fzhdt\u002Fce25177e9d4049a5a2eb98536b35abbd.htm)[关闭本页](javascript:window.close();)\n\n分享：[](javascript:void(0) \"weChat\")[](javascript:void(0);)[](javascript:(function(){window.open('http:\u002F\u002Fservice.weibo.com\u002Fshare\u002Fshare.php?url='+encodeURIComponent(location.href)+'&title='+encodeURIComponent(document.getElementById('shareTitle').innerHTML)+'&source=bookmark')})() \"分享到新浪微博\")\n\n图片新闻[更多 >](https:\u002F\u002Faii.caas.cn\u002Fxwdt\u002Ftpxw\u002Findex.htm)\n\n*   [![Image 51](https:\u002F\u002Faii.caas.cn\u002Fimages\u002F2026-08\u002F5cc2b429f798427b8914f0689aa40634.png)信息所举办APEC“数智赋能亚太农食系统转型”项目研讨会 24 八月](https:\u002F\u002F","中国农科院农业信息研究所 2026-09-06","2026-09-06T00:00:00Z","报道",10,false,56,{"impact":17,"substance":18,"depth":13,"authority":19,"freshness":20,"relevant":21,"comment":22},16,12,14,4,1,"国家级学会分会学术年会在哈尔滨举办，属农业信息化领域常规学术动态，时效偏旧且正文信息有限，可作为主题页聚合素材但不宜进入每日精选。",[24],{"name":10,"url":6},[26,27,28,29],"智慧农业","农业人工智能","农业信息化","学术会议",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":37,"locked":14,"version":38,"method":39,"evidence":40},"partial","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":45,"reason":67},[42,50,56,62],{"code":43,"score":44,"supported":45,"title":46,"abstract":47,"tags":49},"I3",3,true,[],[48],"遥感",[],{"code":51,"score":44,"supported":45,"title":52,"abstract":53,"tags":55},"I6",[],[54],"农业大数据",[],{"code":57,"score":58,"supported":14,"title":59,"abstract":60,"tags":61},"I1",2,[],[],[28,26],{"code":63,"score":21,"supported":14,"title":64,"abstract":65,"tags":66},"I2",[],[],[27],"一级领域明确，二级方向需要细分",[69,70],"农业人工智能 农业信息化 学术会议 智慧农业","农业人工智能 农业信息化","农业人工智能农业信息化学术会议智慧农业-2603",0,"2026-09-16T00:03:50.992492Z",{"total":75,"page":21,"page_size":75,"items":76},6,[77,157,249,320,381,441],{"id":78,"title":79,"url":80,"summary":81,"summary_zh":82,"content":8,"source_name":83,"source_url":80,"published_at":84,"category":85,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":86,"sources":90,"tags":92,"primary_topic":96,"secondary_topics":97,"classification":98,"search_phrases":138,"slug":141,"view_count":72,"doi":142,"paper":143,"created_at":156},5563,"AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531525","Abstract: Crop diseases and pest infestations remain persistent threats to plantation productivity, with pests estimated to destroy up to 40% of global crop production and plant diseases and invasive insects imposing substantial economic losses worldwide (Food and Agriculture Organization of the United Nations [FAO], 2019). Climate change is intensifying these risks by altering the geographic range, seasonality, and population dynamics of pathogens and pests (Gullino et al., 2021; Skendžić et al., 2021). This paper examines how artificial intelligence (AI) — including convolutional neural networks (CNNs), vision transformers (ViTs), unmanned aerial vehicle (UAV)-based multispectral and hyperspectral sensing, and Internet of Things (IoT)-enabled monitoring — can strengthen the early detection of biotic stress in plantation crops and thereby support plantation risk management. Drawing on recent peer-reviewed and industry literature, the paper synthesises evidence on detection performance, deployment modalities, and pathways for integration into enterprise risk-management systems. It proposes a conceptual framework in which AI-enabled early-warning systems operate as a continuous, spatially explicit risk-sensing layer linking agronomic observations with operational, financial, and insurance decision-making. AI can improve the timeliness, spatial precision, and consistency of surveillance; however, its risk-management value depends on field validation, representative training data, reliable infrastructure, affordable deployment, and appropriate governance. AI should therefore currently be positioned as decision support that augments, rather than replaces, agronomic expertise and ground-truthing. Keywords: artificial intelligence; plant disease detection; pest infestation; precision agriculture; plantation risk management; machine learning; remote sensing. Title: AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management Author: Dr. Geetha Govindan K Gopalakrishnan International Journal of Management and Commerce Innovations ISSN 2348-7585 (Online) Vol. 14, Issue 2, October 2026 - March 2027 Page No: 54-59 Research Publish Journals Website: www.researchpublish.com Published Date: 21-October-2026 DOI: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526 Paper Download Link (Source) https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-management","摘要：作物病害与虫害侵染始终是种植园生产力的持续威胁，据估计，害虫每年可摧毁全球高达40%的作物产量，植物病害与入侵昆虫在全球范围内造成重大经济损失（联合国粮食及农业组织[FAO]，2019）。气候变化通过改变病原体与害虫的地理分布范围、季节性规律及种群动态，正在加剧这些风险（Gullino等，2021；Skendžić等，2021）。本文探讨人工智能（AI）——包括卷积神经网络（CNN）、视觉变换器（ViT）、基于无人机（UAV）的多光谱与高光谱遥感，以及物联网（IoT）赋能的监测技术——如何强化种植园作物生物胁迫的早期检测，从而支撑种植园风险管理。本文基于近期同行评审文献与行业文献，综合梳理了检测性能、部署模式及其融入企业风险管理系统的路径等方面的证据。文章提出一个概念框架，其中AI赋能的预警系统作为持续运行、空间显性的风险感知层，将农艺观测与运营、财务及保险决策相衔接。AI能够提升监测的及时性、空间精度与一致性；然而，其风险管理价值取决于田间验证、具有代表性的训练数据、可靠的基础设施、可负担的部署成本以及适当的治理。因此，当前应将AI定位为决策支持工具，用以增强而非替代农艺专业知识与实地核验。关键词：人工智能；植物病害检测；虫害侵染；精准农业；种植园风险管理；机器学习；遥感。题目：AI驱动的作物病害与虫害侵染早期检测：种植园风险管理的新范式。作者：Geetha Govindan K Gopalakrishnan博士。《国际管理与商业创新期刊》ISSN 2348-7585（在线）第14卷第2期，2026年10月—2027年3月，第54-59页。研究出版期刊网站：www.researchpublish.com。发布日期：2026年10月21日。DOI：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526。论文下载链接（来源）：https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-ma","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z","论文",{"impact":18,"substance":17,"depth":87,"authority":75,"freshness":88,"relevant":21,"comment":89},13,9,"综述性论文，系统梳理AI与遥感在作物病虫害早期预警中的应用并提出风险管理框架，有参考价值但方法创新与数据规模有限，信源为普通出版平台。",[91],{"name":83,"url":80},[26,27,93,94,95],"遥感监测","风险管理","病虫害监测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":99},{"candidates":100,"agricultural_evidence":45,"reason":67},[101,109,116,124,131],{"code":63,"score":13,"supported":45,"title":102,"abstract":103,"tags":108},[],[104,105,106,107],"机器学习","machine learning","artificial intelligence","decision support",[27],{"code":43,"score":13,"supported":45,"title":110,"abstract":111,"tags":115},[],[48,112,113,114],"remote sensing","multispectral","hyperspectral",[93],{"code":117,"score":88,"supported":45,"title":118,"abstract":119,"tags":123},"I5",[],[120,121,122],"物联网","internet of things","IoT",[],{"code":57,"score":125,"supported":45,"title":126,"abstract":127,"tags":130},7,[],[128,129],"精准农业","precision agriculture",[26],{"code":132,"score":75,"supported":45,"title":133,"abstract":134,"tags":137},"H7",[],[135,136],"气候变化","climate change",[],[139,140],"AI 作物病害 早期检测","无人机 多光谱 病虫害监测","AI作物病害早期检测-5563","10.5281\u002Fzenodo.23531525",{"doi":142,"openalex_id":144,"authors":145,"venue":83,"cited_by_count":72,"oa_url":80,"card":148,"direction":154,"ingested_from":155},"W7223085400",[146],{"name":147,"orcid":8},"Dr. Geetha Govindan K Gopalakrishnan",{"tldr":149,"method":150,"finding":151,"direction":152,"opportunity":153},"综述AI（CNN、ViT、无人机遥感、物联网）用于作物病虫害早期检测并融入种植园风险管理。","文献综述，整合CNN、ViT、无人机多\u002F高光谱与IoT监测的检测性能与部署方式。","AI可提升监测及时性与空间精度，但价值取决于田间验证、代表性数据与治理，应作决策支持而非替代专家。","农业人工智能与决策模型","可将AI预警层与保险、金融风控系统对接，研究跨域数据融合与可解释风险定价的空白。","智慧农业 \u002F 农业物联网","openalex","2026-10-22T11:20:24.428080Z",{"id":158,"title":159,"url":160,"summary":161,"summary_zh":162,"content":8,"source_name":163,"source_url":160,"published_at":84,"category":85,"cover_url":8,"hotness":13,"is_selected":14,"score":164,"score_detail":165,"sources":171,"tags":173,"primary_topic":175,"secondary_topics":176,"classification":177,"search_phrases":235,"slug":238,"view_count":72,"doi":239,"paper":240,"created_at":248},5556,"AI Powered IoT-Based System for Weed and Pest Control in Agriculture Using Deep Learning","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.76s.32","Weeds, pest attacks, labour shortage and over-dependence on chemical pesticides are the serious challenges faced by the agricultural sector in the wake of ever growing weed infestation.Manual weed and pest control has potential negative environmental impacts, increased production costs and reduced yields.The application of Artificial Intelligence (AI), Internet of Things (IoT), Deep Learning, robotics, and laser technologies are a promising way of developing sustainable and autonomous agricultural systems.This study introduces an intelligent agricultural system based on Artificial Intelligence (AI) and Internet of Things (IoT) for weed identification, harmful insect identification, crop condition monitoring and automatic weed removal using a laser beam.The system will be based on Internet of Things (IoT) sensors, Computer vision modules, Convolutional neural networks (CNN), Unmanned robotic platforms, Environmental sensing systems, and Electronic Swarm Suppression (ESS) mechanisms to enhance agricultural productivity and reduce crop losses.The system uses deep learning algorithms for real time weed, crop, beneficial and harmful insect classification.High-resolution cameras and sensing devices collect environmental and crop data continuously and feed the data into an IoT system for processing in a cloud-edge computing system.High precision weed elimination with a robotic platform containing laser emitters which do not harm adjacent crops.Further, computer vision and environmental monitoring are employed to identify aerial insect swarms and to enable automatic attacks of insect swarms by means of a laser beam and ESS technology.Multiple agricultural blocks were used for experimental evaluation of the maize, wheat, soybean and vegetable crops.The results show substantial increases in the accuracy of weed detection, pest identification, crop protection and cost savings for operations over current practices.The proposed system achieved a 97.6% accuracy in the weed classification, 96.8% accuracy in the detection of the insect and a 42.3% reduction in pesticide use.In addition, the crop yield rose by 18.7% and the operational costs dropped by 28.4%.Overall, the results suggest that AI-powered IoT can significantly enhance precision agriculture and promote sustainable food production.","在杂草侵扰日益加剧的背景下，杂草、病虫害侵袭、劳动力短缺以及过度依赖化学农药是农业部门面临的严峻挑战。人工除草和病虫害防治具有潜在的环境负面影响，会增加生产成本并降低产量。人工智能（AI）、物联网（IoT）、深度学习、机器人技术及激光技术的应用，是发展可持续自主农业系统的有前景的途径。本研究提出一种基于人工智能（AI）和物联网（IoT）的智能农业系统，用于杂草识别、有害昆虫识别、作物状况监测以及利用激光束自动除草。该系统将基于物联网（IoT）传感器、计算机视觉模块、卷积神经网络（CNN）、无人机器人平台、环境感知系统及电子蜂群抑制（ESS）机制，以提高农业生产力并减少作物损失。该系统采用深度学习算法对杂草、作物、有益昆虫和有害昆虫进行实时分类。高分辨率摄像头和传感设备持续采集环境与作物数据，并将数据输入物联网系统，在云边计算系统中进行处理。通过配备激光发射器的机器人平台实现高精度除草，且不伤害邻近作物。此外，利用计算机视觉和环境监测来识别空中昆虫蜂群，并通过激光束和ESS技术实现对昆虫蜂群的自动攻击。研究采用多个农业区块对玉米、小麦、大豆和蔬菜作物进行了实验评估。结果表明，与当前做法相比，杂草检测、病虫害识别、作物保护的准确性显著提高，作业成本得以节省。所提出的系统在杂草分类中达到97.6%的准确率，昆虫检测准确率为96.8%，农药使用量减少42.3%。此外，作物产量提高18.7%，作业成本下降28.4%。总体而言，结果表明人工智能驱动的物联网能够显著增强精准农业并促进可持续粮食生产。","International Journal of Drug Delivery Technology",75,{"impact":166,"substance":167,"depth":168,"authority":169,"freshness":13,"relevant":21,"comment":170},18,22,17,8,"论文给出97.6%杂草识别率、42.3%农药减量等量化结论，方法新颖且数据完整，但期刊权威性一般，属细分领域技术进展。",[172],{"name":163,"url":160},[26,27,120,174,95],"杂草识别",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":178},{"candidates":179,"agricultural_evidence":45,"reason":67},[180,189,194,203,207,214,221,228],{"code":63,"score":166,"supported":45,"title":181,"abstract":183,"tags":188},[182],"deep learning",[184,185,186,182,106,187],"深度学习","计算机视觉","病虫害识别","computer vision",[27],{"code":117,"score":166,"supported":45,"title":190,"abstract":191,"tags":193},[122],[120,121,122,192],"edge computing",[120],{"code":195,"score":13,"supported":45,"title":196,"abstract":197,"tags":202},"F4",[],[198,199,200,201],"农药","除草","pesticide","crop protection",[174],{"code":57,"score":125,"supported":45,"title":204,"abstract":205,"tags":206},[],[128,129],[26],{"code":208,"score":75,"supported":45,"title":209,"abstract":210,"tags":213},"A2",[],[211,212],"小麦","wheat",[],{"code":215,"score":75,"supported":45,"title":216,"abstract":217,"tags":220},"A3",[],[218,219],"玉米","maize",[],{"code":222,"score":75,"supported":45,"title":223,"abstract":224,"tags":227},"A4",[],[225,226],"大豆","soybean",[],{"code":229,"score":75,"supported":45,"title":230,"abstract":231,"tags":234},"B1",[],[232,233],"蔬菜","vegetable",[],[236,237],"AI IoT 激光除草 病虫害识别","深度学习 精准农业 农药减量","AIIoT激光除草病虫害识别-5556","10.25258\u002Fijddt.16.76s.32",{"doi":239,"openalex_id":241,"authors":242,"venue":163,"cited_by_count":72,"oa_url":160,"card":243,"direction":154,"ingested_from":155},"W7223145687",[],{"tldr":244,"method":245,"finding":246,"direction":154,"opportunity":247},"提出AI+IoT系统，用深度学习和激光机器人实现杂草与害虫识别及自动清除。","IoT传感器、CNN视觉分类、无人机\u002F机器人平台与激光除草，云边协同处理。","杂草分类准确率97.6%、害虫识别96.8%，农药减42.3%，增产18.7%。","可探索激光除草对非靶标生物与土壤的长期生态影响，及多作物跨域模型泛化。","2026-10-22T11:20:18.540359Z",{"id":250,"title":251,"url":252,"summary":253,"summary_zh":254,"content":8,"source_name":255,"source_url":252,"published_at":256,"category":85,"cover_url":8,"hotness":13,"is_selected":14,"score":257,"score_detail":258,"sources":260,"tags":262,"primary_topic":264,"secondary_topics":265,"classification":266,"search_phrases":301,"slug":304,"view_count":21,"doi":305,"paper":306,"created_at":319},5074,"QISHLOQ XO‘JALIGI OLIY TA’LIM MUASSASALARIDA MAXSUS FANLARNI O‘QITISHDA “SMART AGRICULTURE” TEXNOLOGIYALARIDAN FOYDALANISH METODIKASI","https:\u002F\u002Fdoi.org\u002F10.67547\u002Ffanistiqbollari.2026.876","The article examines a methodology for integrating Smart Agriculture technologies into the teaching of specialized subjects at agricultural higher education institutions. The study analyzes the didactic potential of the Internet of Things, sensor monitoring, GIS, remote sensing, artificial intelligence, smart irrigation and digital data analytics.","本文探讨了将智慧农业技术融入农业高等院校专业课程教学的方法论。研究分析了物联网、传感器监测、地理信息系统、遥感、人工智能、智能灌溉和数字数据分析在教学中的潜力。","Ta limda Istiqbolli Izlanishlar xalqaro ilmiy metodik jurnal","2026-10-15T00:00:00Z",49,{"impact":169,"substance":19,"depth":87,"authority":75,"freshness":169,"relevant":21,"comment":259},"乌兹别克语论文探讨智慧农业技术融入农业高校专业教学的路径，属农业信息化人才培养方向，但为境外一般期刊、影响面有限，暂不宜进入每日精选。",[261],{"name":255,"url":252},[26,27,120,48,263],"农业教育",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":267},{"candidates":268,"agricultural_evidence":45,"reason":67},[269,275,279,283,290,297],{"code":57,"score":270,"supported":45,"title":271,"abstract":273,"tags":274},15,[272],"smart agriculture",[26,272],[26],{"code":43,"score":125,"supported":45,"title":276,"abstract":277,"tags":278},[],[48,112],[48],{"code":117,"score":125,"supported":45,"title":280,"abstract":281,"tags":282},[],[120,121],[120],{"code":284,"score":75,"supported":45,"title":285,"abstract":286,"tags":289},"H3",[],[287,288],"灌溉","irrigation",[],{"code":291,"score":75,"supported":45,"title":292,"abstract":293,"tags":296},"I4",[],[294,295],"地理信息","GIS",[],{"code":63,"score":20,"supported":45,"title":298,"abstract":299,"tags":300},[],[106],[27],[302,303],"农业人工智能 农业教育 智慧农业 物联网","农业人工智能 农业教育","农业人工智能农业教育智慧农业物联网-5074","10.67547\u002Ffanistiqbollari.2026.876",{"doi":305,"openalex_id":307,"authors":308,"venue":255,"cited_by_count":72,"oa_url":312,"card":313,"direction":154,"ingested_from":155},"W7221997102",[309],{"name":310,"orcid":311},"O'G'ILOY SADULLO QIZI SHAMSIYEV","https:\u002F\u002Forcid.org\u002F0009-0003-4706-6488","https:\u002F\u002Ffanistiqbollari.uz\u002Fstorage\u002Farticle876.pdf",{"tldr":314,"method":315,"finding":316,"direction":317,"opportunity":318},"研究将智慧农业技术融入农业高校专业课程教学的方法论。","分析物联网、传感器、GIS、遥感、AI等技术的教学潜力。","智慧农业技术可有效支撑农业专业课程的教学方法设计。","数字乡村与农业信息化","可实证检验该教学法对学生技能与就业的影响，并开发配套课程资源。","2026-10-16T23:30:07.890026Z",{"id":321,"title":322,"url":323,"summary":324,"summary_zh":325,"content":8,"source_name":326,"source_url":323,"published_at":327,"category":85,"cover_url":8,"hotness":13,"is_selected":14,"score":328,"score_detail":329,"sources":332,"tags":334,"primary_topic":336,"secondary_topics":337,"classification":338,"search_phrases":358,"slug":361,"view_count":21,"doi":362,"paper":363,"created_at":380},4825,"Multi-Source Remote Sensing Fusion and Deep Learning for Intelligent Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206434","The fusion of multi-source remote sensing data and deep learning technology has been increasingly applied in precision agriculture. This review focuses on the perception–decision–execution framework and summarizes the current application status, technical routes, and development trends of multi-source remote sensing data fusion and deep learning in precision agriculture. It examines the complementary advantages of multimodal data such as optical, thermal infrared (TIR), Light Detection and Ranging (LiDAR), and synthetic aperture radar (SAR) in characterizing crop physiological traits and structural characteristics. At the algorithmic level, this review analyzes the evolution of deep learning architectures from single convolutional neural networks (CNNs) to spatiotemporal fusion networks (STFNs), Transformer-based architectures, and emerging state-space models, with emphasis on their capabilities for heterogeneous feature extraction and cross-modal information integration. This review discusses the transformation mechanism from intelligent perception to executable decision-making and explores the application effectiveness of prescription maps and variable-rate task technology in improving resource utilization. This review proposes forward-looking views on core bottlenecks such as model generalization, physical interpretability, and edge computing. This review not only provides a theoretical analysis of precision agriculture but also provides academic recommendations for the development of intelligent agricultural equipment in the future.","多源遥感数据与深度学习技术的融合已在精准农业中得到日益广泛的应用。本文以“感知—决策—执行”框架为主线，综述了多源遥感数据融合与深度学习在精准农业中的应用现状、技术路线与发展趋势。文章考察了光学、热红外（TIR）、激光雷达（LiDAR）和合成孔径雷达（SAR）等多模态数据在表征作物生理性状与结构特征方面的互补优势。在算法层面，本文分析了深度学习架构从单一卷积神经网络（CNN）向时空融合网络（STFN）、基于Transformer的架构以及新兴状态空间模型的演进，重点探讨了其在异构特征提取与跨模态信息整合方面的能力。本文讨论了从智能感知到可执行决策的转化机制，并探究了处方图与变量作业技术在提升资源利用率方面的应用效果。针对模型泛化能力、物理可解释性与边缘计算等核心瓶颈，本文提出了前瞻性观点。本文不仅为精准农业提供了理论分析，也为未来智能农业装备的发展提供了学术建议。","Sensors","2026-10-12T00:00:00Z",78,{"impact":166,"substance":330,"depth":166,"authority":19,"freshness":169,"relevant":21,"comment":331},20,"系统综述多源遥感融合与深度学习在精准农业中的感知—决策—执行框架，方法脉络清晰、前瞻性强，对智慧农业装备研发有参考价值。",[333],{"name":326,"url":323},[26,27,128,48,335],"多源数据融合",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":339},{"candidates":340,"agricultural_evidence":45,"reason":67},[341,345,349,353],{"code":57,"score":17,"supported":45,"title":342,"abstract":343,"tags":344},[129],[128,129],[26,128],{"code":63,"score":270,"supported":45,"title":346,"abstract":347,"tags":348},[182],[184,182],[27],{"code":43,"score":270,"supported":45,"title":350,"abstract":351,"tags":352},[112],[48,112],[48],{"code":117,"score":125,"supported":45,"title":354,"abstract":355,"tags":357},[],[356,192],"边缘计算",[335],[359,360],"多源遥感 深度学习 精准农业","Transformer 遥感 作物表型","多源遥感深度学习精准农业-4825","10.3390\u002Fs26206434",{"doi":362,"openalex_id":364,"authors":365,"venue":326,"cited_by_count":72,"oa_url":323,"card":374,"direction":378,"ingested_from":155},"W7221565940",[366,369,372],{"name":367,"orcid":368},"Zhen Ma","https:\u002F\u002Forcid.org\u002F0000-0003-4377-9651",{"name":370,"orcid":371},"Xiyuan Chen","https:\u002F\u002Forcid.org\u002F0000-0002-1770-8529",{"name":373,"orcid":8},"Cundeng Wang",{"tldr":375,"method":376,"finding":377,"direction":378,"opportunity":379},"综述多源遥感融合与深度学习在精准农业感知-决策-执行框架中的应用与趋势。","综述光学、热红外、LiDAR、SAR融合及CNN到Transformer、状态空","多模态融合与先进深度学习架构可提升作物感知与处方图决策，但泛化性、可解释性和边缘计算仍是瓶颈。","农业遥感与作物表型","可探索轻量化状态空间模型与物理约束融合，提升跨区域泛化及边缘端实时决策能力。","2026-10-13T23:30:24.008972Z",{"id":382,"title":383,"url":384,"summary":385,"summary_zh":386,"content":8,"source_name":387,"source_url":384,"published_at":388,"category":85,"cover_url":8,"hotness":13,"is_selected":14,"score":389,"score_detail":390,"sources":392,"tags":394,"primary_topic":397,"secondary_topics":398,"classification":399,"search_phrases":420,"slug":423,"view_count":72,"doi":424,"paper":425,"created_at":440},4564,"Enhancing Long-Tailed Macaque Pest Identification Using a Two-Stage YOLO-EfficientNet Pipeline","https:\u002F\u002Fdoi.org\u002F10.36456\u002F1hrtgc63","Long-tailed macaque (Macaca fascicularis) infestations have reduced corn production from a potential 7–9 tons to only 4–5 tons per growing season (±40%), while conventional deterrence methods such as loud noise and shooting have proven ineffective. This study developed an Internet of Things (IoT)-based pest repellent system using audiosonic waves, an HC-SR501 sensor, and an ESP32-CAM module, incorporating YOLOv8 and EfficientNet-B0 to replace the previously used YOLOv3 Tiny model, with the aim of improving detection accuracy and distinguishing long-tailed macaques from humans. Since population density at the partner's farmland was insufficient to construct an adequate dataset, image data were collected at Kembang Island, South Kalimantan, which serves as a natural habitat supporting a large and observable population of long-tailed macaques, thereby enabling the acquisition of visually representative and diverse training data. A total of 3,083 images were annotated using Label Studio under two classes (human and long-tailed macaque), then divided at an 80:10:10 ratio without augmentation. The YOLOv8n model was trained for 50 epochs, after which validation detections were cropped into 920 images to fine-tune EfficientNet-B0 over 10 epochs. Evaluation results show that YOLOv8 achieved a precision of 0.959, recall of 0.965, mAP50 of 0.979, and mAP50-95 of 0.723, while EfficientNet-B0 reached validation accuracies of 98.4–100% with no significant overfitting. The two-stage detection-classification pipeline was successfully tested end-to-end on 121 field images, demonstrating substantial performance improvements over the prior model and contributing toward IoT and AI-based smart agriculture solutions for effective, sustainable corn field protection.","长尾猕猴（Macaca fascicularis）的侵害使玉米产量从潜在的每生长季7–9吨降至仅4–5吨（±40%），而诸如大声喧哗和射击等传统驱赶方法已被证明无效。本研究开发了一种基于物联网（IoT）的驱虫系统，采用声波、HC-SR501传感器和ESP32-CAM模块，并引入YOLOv8和EfficientNet-B0以替代先前使用的YOLOv3 Tiny模型，旨在提高检测精度并区分长尾猕猴与人类。由于合作农田的种群密度不足以构建足够的数据集，图像数据采集于南加里曼丹的Kembang岛，该岛作为自然栖息地，拥有大量且可观察的长尾猕猴种群，从而能够获取具有视觉代表性和多样性的训练数据。使用Label Studio对共计3,083张图像进行标注，分为两个类别（人类和长尾猕猴），然后按80:10:10的比例划分，未进行数据增强。YOLOv8n模型训练了50个epoch，之后将验证检测结果裁剪为920张图像，用于对EfficientNet-B0进行10个epoch的微调。评估结果表明，YOLOv8达到了0.959的精确率、0.965的召回率、0.979的mAP50和0.723的mAP50-95，而EfficientNet-B0达到了98.4–100%的验证准确率，且无明显过拟合。该两阶段检测-分类流水线在121张实地图像上成功进行了端到端测试，展示出相较于先前模型的显著性能提升，为基于物联网和人工智能的智慧农业解决方案做出了贡献，可有效、可持续地保护玉米田。","BEST Journal of Applied Electrical Science & Technology","2026-10-09T00:00:00Z",69,{"impact":18,"substance":167,"depth":168,"authority":88,"freshness":88,"relevant":21,"comment":391},"论文提出YOLOv8+EfficientNet-B0两阶段猕猴识别与声波驱避系统，数据规模与指标扎实，对智慧农业AI应用有参考价值。",[393],{"name":387,"url":384},[26,27,120,395,396],"目标检测","玉米病虫害",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":400},{"candidates":401,"agricultural_evidence":45,"reason":67},[402,407,411,415],{"code":63,"score":13,"supported":45,"title":403,"abstract":405,"tags":406},[404],"YOLO",[],[27,395],{"code":117,"score":13,"supported":45,"title":408,"abstract":409,"tags":410},[],[120,121,122],[120],{"code":57,"score":125,"supported":45,"title":412,"abstract":413,"tags":414},[],[26,272],[26],{"code":215,"score":75,"supported":45,"title":416,"abstract":417,"tags":419},[],[218,418],"corn",[],[421,422],"长尾猕猴 玉米 识别","农业人工智能 玉米病虫害 智慧农业 目标检测","长尾猕猴玉米识别-4564","10.36456\u002F1hrtgc63",{"doi":424,"openalex_id":426,"authors":427,"venue":387,"cited_by_count":72,"oa_url":384,"card":435,"direction":154,"ingested_from":155},"W7221187629",[428,430,433],{"name":429,"orcid":8},"Hammami Rifqi Darmawan",{"name":431,"orcid":432},"Finki Dona Marleny","https:\u002F\u002Forcid.org\u002F0000-0002-3437-5594",{"name":434,"orcid":8},"Ayu Ahadi Ningrum",{"tldr":436,"method":437,"finding":438,"direction":154,"opportunity":439},"用YOLOv8+EfficientNet-B0两阶段流水线识别长尾猕猴，提升玉米田驱猴系统检测精度。","3083张标注图像，YOLOv8n检测后裁剪920张微调EfficientNet","YOLOv8 mAP50达0.979，EfficientNet-B0验证准确率98.4–100%，1","可探索将两阶段检测-分类流水线轻量化部署到边缘设备，并扩展至多物种、多作物场景的实时驱避决策。","2026-10-10T23:30:07.974290Z",{"id":442,"title":443,"url":444,"summary":445,"summary_zh":446,"content":8,"source_name":447,"source_url":444,"published_at":448,"category":85,"cover_url":8,"hotness":13,"is_selected":14,"score":449,"score_detail":450,"sources":453,"tags":455,"primary_topic":458,"secondary_topics":459,"classification":460,"search_phrases":501,"slug":504,"view_count":58,"doi":505,"paper":506,"created_at":524},4297,"IoT -Based Smart Agriculture Using Machine Learning","https:\u002F\u002Fdoi.org\u002F10.47392\u002Firjaeh.2026.0711","Agriculture plays a crucial role in economic development and food security. Traditional farming methods often face challenges such as inefficient water usage, unpredictable environmental conditions, and limited real-time monitoring. This paper proposes an IoT-Based Smart Agriculture system integrated with Machine Learning to enhance farm productivity and resource management. The system employs IoT sensors to collect data related to soil moisture, temperature, humidity, and light intensity. These data are transmitted to a cloud platform where machine learning algorithms analyze environmental conditions, predict crop requirements, and generate intelligent recommendations for irrigation and crop management. The proposed solution enables continuous monitoring, reduces water wastage, improves crop health assessment, and supports data-driven decision-making. By combining IoT and Machine Learning, the system offers a cost-effective and scalable approach to precision farming, helping farmers increase yield, reduce operational costs, and promote sustainable agricultural practices.","农业在经济发展和粮食安全中发挥着至关重要的作用。传统耕作方式往往面临水资源利用效率低、环境条件不可预测以及实时监测能力有限等挑战。本文提出了一种基于物联网（IoT）并与机器学习相结合的智慧农业系统，以提升农业生产率和资源管理水平。该系统采用物联网传感器采集与土壤湿度、温度、湿度和光照强度相关的数据。这些数据被传输至云平台，由机器学习算法分析环境条件、预测作物需求，并生成灌溉和作物管理的智能建议。所提出的解决方案能够实现持续监测、减少水资源浪费、改进作物健康评估，并支持数据驱动的决策。通过将物联网与机器学习相结合，该系统为精准农业提供了一种具有成本效益且可扩展的方法，帮助农民提高产量、降低运营成本并促进可持续农业实践。","International Research Journal on Advanced Engineering Hub (IRJAEH)","2026-10-05T00:00:00Z",46,{"impact":169,"substance":18,"depth":451,"authority":75,"freshness":88,"relevant":21,"comment":452},11,"论文提出IoT与机器学习结合的智慧农业系统，思路常规、缺乏实测数据与规模验证，学术权威性一般，仅具参考价值。",[454],{"name":447,"url":444},[26,27,456,457,128],"农业物联网","智能灌溉",{"code":31,"slug":32,"name":33,"path":34,"parent_code":8},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":461},{"candidates":462,"agricultural_evidence":45,"reason":67},[463,468,472,476,481,488,495],{"code":57,"score":464,"supported":45,"title":465,"abstract":466,"tags":467},19,[272],[26,128,272],[26,128],{"code":63,"score":270,"supported":45,"title":469,"abstract":470,"tags":471},[105],[104,105],[27],{"code":117,"score":270,"supported":45,"title":473,"abstract":474,"tags":475},[122],[120,122],[456],{"code":284,"score":13,"supported":45,"title":477,"abstract":478,"tags":480},[],[287,479,288],"水资源",[457],{"code":482,"score":75,"supported":45,"title":483,"abstract":484,"tags":487},"A8",[],[485,486],"粮食安全","food security",[],{"code":489,"score":75,"supported":45,"title":490,"abstract":491,"tags":494},"H2",[],[492,493],"土壤","soil",[],{"code":496,"score":44,"supported":45,"title":497,"abstract":498,"tags":500},"H8",[],[499],"可持续农业",[],[502,503],"IoT 智慧农业 机器学习","土壤墒情 智能灌溉 云平台","IoT智慧农业机器学习-4297","10.47392\u002Firjaeh.2026.0711",{"doi":505,"openalex_id":507,"authors":508,"venue":447,"cited_by_count":72,"oa_url":444,"card":519,"direction":154,"ingested_from":155},"W7220440757",[509,511,513,515,517],{"name":510,"orcid":8},"Priyanka Behera",{"name":512,"orcid":8},"Satyabrata Satapathy",{"name":514,"orcid":8},"Nakshytrajeet Kar",{"name":516,"orcid":8},"Sucharita NathSharma",{"name":518,"orcid":8},"Priyanka Shit",{"tldr":520,"method":521,"finding":522,"direction":154,"opportunity":523},"提出物联网结合机器学习的智慧农业系统，实现环境监测与智能灌溉决策。","部署土壤湿度、温湿度、光照等IoT传感器，云端机器学习分析预测作物需求。","系统可连续监测、减少水资源浪费、改善作物健康评估并支持数据驱动决策。","可探索边缘计算与轻量模型在田间实时决策中的应用，降低云端依赖与延迟。","2026-10-06T23:30:08.015155Z"]