[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3174":3,"related-3174":55},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":54},3174,"Remote sensing and geospatial modeling for the detection of war-induced land use abandonment, soil disturbance, and degradation in Sumy region of Ukraine: a systematic review","https:\u002F\u002Fdoi.org\u002F10.31548\u002Fzemleustriy2026.03.015","The full-scale military invasion of Ukraine has caused multidimensional transformations of agricultural landscapes, land cover, and ecosystems. The Sumy region, located along the northeastern border with the Russian Federation, is a critical region that has suffered from intense fighting, prolonged artillery shelling, and ongoing border clashes during the study period from 2022 to May 2026. This paper systematically evaluates the application of remote sensing and geospatial modeling techniques to detect, quantify, and monitor war-induced land abandonment, as well as physical land cover disturbances, chemical contamination, and broader environmental degradation in this theater of war. Combining recent scientific publications on the war in Ukraine and comparative global examples (Syria, Iraq, and Sudan), the effectiveness of integrating multi-sensor data, including optical (Sentinel-2, Landsat), synthetic aperture radar (SAR; Sentinel-1), thermal, and very high resolution (VHR; Maxar, WorldView) platforms, as well as advanced machine and deep learning algorithms (Random Forest, Deep Learning) and time series analysis (FANTA, two-period curve approximation), is assessed. The results of the study show that while geospatial modeling is an indispensable tool for rapid, large-scale, and non-contact damage assessment in active conflict settings, significant critical research gaps remain, namely separating short-term cessation of agricultural use from actual land abandonment caused by war, verifying such facts in the face of a severe shortage of reliable ground data, and managing sensitive geospatial information. This systematic review highlights existing methodological gaps and outlines future perspectives needed to build a harmonized spatial monitoring system capable of guiding post-conflict recovery and environmental remediation based on actual remote sensing data at the regional scale.Received: 01.07.2026;Accepted:26.08.2026;","乌克兰遭受的大规模军事入侵已导致农业景观、土地覆盖和生态系统发生多维转变。苏梅州位于乌克兰与俄罗斯联邦东北部接壤的边境沿线，是一个关键地区，在研究期间（2022年至2026年5月）遭受了激烈战斗、长期炮击以及持续边境冲突的影响。本文系统评估了遥感与地理空间建模技术在探测、量化和监测该战区战争导致的土地撂荒、土地覆盖物理扰动、化学污染及更广泛环境退化方面的应用。结合近期关于乌克兰战争的科学出版物及全球比较案例（叙利亚、伊拉克和苏丹），本文评估了多传感器数据整合的有效性，包括光学（Sentinel-2、Landsat）、合成孔径雷达（SAR；Sentinel-1）、热红外及甚高分辨率（VHR；Maxar、WorldView）平台，以及先进的机器学习和深度学习算法（随机森林、深度学习）和时间序列分析（FANTA、两期曲线近似）。研究结果表明，尽管地理空间建模是在活跃冲突环境中进行快速、大规模、非接触式损害评估不可或缺的工具，但仍存在重要的关键研究空白，即如何区分农业利用的短期中止与战争导致的实际土地撂荒，如何在严重缺乏可靠地面数据的情况下核实此类事实，以及如何管理敏感的地理空间信息。本系统综述指出了现有的方法论空白，并勾勒了未来前景，以构建一个协调的空间监测系统，从而基于区域尺度上的实际遥感数据指导冲突后恢复和环境修复。收稿日期：2026年7月1日；接受日期：2026年8月26日；",null,"Zemleustrìj kadastr ì monìtorìng zemelʹ","2026-09-21T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,12,8,1,"系统综述遥感与地理空间建模在战区耕地撂荒与土壤退化识别中的应用，方法体系与数据源梳理扎实，对农业遥感监测有参考价值，但属境外冲突场景研究，国内落地关联度有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业人工智能","土地退化","遥感监测","耕地撂荒","冲突农业",[33,34],"乌克兰 苏梅州 遥感 耕地撂荒","Sentinel-1 合成孔径雷达 土地退化监测","乌克兰苏梅州遥感耕地撂荒-3174",0,"10.31548\u002Fzemleustriy2026.03.015",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":47,"direction":51,"ingested_from":53},"W7213890485",[41,43,45],{"name":42,"orcid":9},"V. Bogdanets",{"name":44,"orcid":9},"Ye. Berezhniak",{"name":46,"orcid":9},"D. Brovko",{"tldr":48,"method":49,"finding":50,"direction":51,"opportunity":52},"系统综述遥感与地理空间建模在乌克兰苏梅地区检测战争导致的土地弃耕、土壤扰动与退化的应用。","综述多源遥感（Sentinel-2\u002F1、Landsat、VHR）与机器学习、时间","遥感是冲突区快速非接触损害评估的关键工具，但区分短期停耕与真正弃耕、地面数据匮乏及敏感信息管理仍是重","农业遥感与作物表型","可研究多源时序遥感与弱监督学习结合，构建冲突区弃耕识别与地面验证缺失下的不确定性量化框架。","openalex","2026-09-22T23:30:23.289241Z",{"total":56,"page":22,"page_size":56,"items":57},6,[58,90,133,158,204,253],{"id":59,"title":60,"url":61,"summary":62,"summary_zh":9,"content":9,"source_name":63,"source_url":9,"published_at":64,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":65,"score_detail":66,"sources":70,"tags":72,"search_phrases":76,"slug":79,"view_count":36,"doi":9,"paper":80,"created_at":89},3248,"Crop recommendation in precision agriculture: a systematic literature review of methods, trends, and challenges（精准农业中的作物推荐：方法、趋势与挑战系统综述）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37471110","MDPI 发表精准农业作物推荐方法系统综述：从183项研究中严格筛选129篇发表于2020-2026年的文章，使用PRISMA协议进行综合分析。研究表明集成学习方法（特别是随机森林和XGBoost）在各种农业数据集的预测性能上具有强大能力；支持向量机、决策树、k近邻等传统ML方法仍被广泛使用；同时CNN和LSTM被用于遥感和时间相关农业分析。最常用的数据集来源是Kaggle，典型输入包括土壤养分（NPK）、土壤pH、天气条件和NDVI、EVI等卫星指数。研究主要研究空白：有限的实时部署、低多数据源集成、低跨区域验证、低模型可解释性。研究表明可扩展、可解释的AI系统对农业实际应用具有重要意义。","MDPI","2026-09-22T00:00:00Z",81,{"impact":17,"substance":67,"depth":17,"authority":68,"freshness":13,"relevant":22,"comment":69},22,13,"基于PRISMA的129篇文献系统综述，梳理作物推荐主流方法与四大研究空白，对农业AI落地有参考价值。",[71],{"name":63,"url":61},[73,27,74,75,29],"智慧农业","机器学习","作物推荐",[77,78],"精准农业 作物推荐 系统综述","XGBoost 随机森林 作物推荐","精准农业作物推荐系统综述-3248",{"doi":9,"openalex_id":9,"authors":81,"venue":9,"cited_by_count":36,"oa_url":9,"card":82,"direction":86,"ingested_from":88},[],{"tldr":83,"method":84,"finding":85,"direction":86,"opportunity":87},"系统综述129篇2020-2026年文献，梳理精准农业作物推荐的方法、趋势与挑战。","PRISMA协议系统综述，分析183项研究筛选出的129篇文献。","集成学习（随机森林、XGBoost）表现最强，主要空白为实时部署、多源集成、跨区域验证与可解释性。","农业人工智能与决策模型","可探索可解释、可跨区域泛化的实时作物推荐系统，并融合多源遥感与物联网数据。","agent","2026-09-23T00:04:33.331160Z",{"id":91,"title":92,"url":93,"summary":94,"summary_zh":95,"content":9,"source_name":96,"source_url":93,"published_at":97,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":98,"score_detail":99,"sources":102,"tags":104,"search_phrases":107,"slug":110,"view_count":36,"doi":111,"paper":112,"created_at":132},3013,"AI and remote sensing for fungal and oomycete disease surveillance: current landscape and biological integration","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41348-026-01352-w","AI and remote sensing for fungal and oomycete disease surveillance: current landscape and biological integration。Journal of Plant Diseases and Protection","人工智能与遥感在真菌及卵菌病害监测中的应用：现状与生物学整合。《植物病害与保护杂志》","Journal of Plant Diseases and Protection","2026-09-19T00:00:00Z",77,{"impact":17,"substance":18,"depth":19,"authority":68,"freshness":100,"relevant":22,"comment":101},9,"核心期刊综述，系统梳理AI与遥感在真菌及卵菌病害监测中的进展与生物学整合路径，对智慧农业植保方向有参考价值。",[103],{"name":96,"url":93},[73,27,105,29,106],"植物病害","病害预警",[108,109],"AI 遥感 真菌病害 监测","植物病害 遥感 预警","AI遥感真菌病害监测-3013","10.1007\u002Fs41348-026-01352-w",{"doi":111,"openalex_id":113,"authors":114,"venue":96,"cited_by_count":36,"oa_url":9,"card":127,"direction":51,"ingested_from":53},"W7213649225",[115,117,119,121,124],{"name":116,"orcid":9},"Biju Vadakkemukadiyil Chellappan",{"name":118,"orcid":9},"C. L. Biji",{"name":120,"orcid":9},"Vanshika Arun Meda",{"name":122,"orcid":123},"Sajad Ali","https:\u002F\u002Forcid.org\u002F0000-0002-3230-1436",{"name":125,"orcid":126},"Sherif Mohamed El‐Ganainy","https:\u002F\u002Forcid.org\u002F0000-0001-5226-4604",{"tldr":128,"method":129,"finding":130,"direction":51,"opportunity":131},"综述AI与遥感在真菌及卵菌病害监测中的现状，强调生物信息整合。","文献综述，整合AI、遥感与病原生物学数据。","AI与遥感结合可提升病害监测，但需融入病原生物学机制。","可研究将病原生活史与遥感时序特征耦合的病害预警模型。","2026-09-20T23:30:21.177583Z",{"id":134,"title":135,"url":136,"summary":137,"summary_zh":9,"content":138,"source_name":139,"source_url":9,"published_at":140,"category":141,"cover_url":9,"hotness":13,"is_selected":14,"score":142,"score_detail":143,"sources":147,"tags":149,"search_phrases":153,"slug":156,"view_count":36,"doi":9,"paper":9,"created_at":157},2898,"苏垦农发神农慧种农业AI大模型规模化落地:天空地一体化闭环,百万亩自有农田实景数据","https:\u002F\u002Fcaifuhao.eastmoney.com\u002Fnews\u002F20260918101757264727920","苏垦农发9月18日发文,公司依托百万亩自有连片高标准农田,持续产出真实大田数据训练神农慧种农业AI智能体,实现天空地一体化数据闭环:空中多光谱无人机集群常态化农田巡测;地面全域四情监测传感器、北斗智能农机、智能灌溉终端;云端苏垦智云平台+神农慧种AI模型,形成采集数据→AI分析研判→输出水肥植保方案→农机落地执行完整闭环。苏垦智云是全国农林牧渔领域唯一入选工信部信创典型案例的农业数字化平台。","[在东方财富看资讯行情，选东方财富证券一站式开户交易>>](https:\u002F\u002Facttg.eastmoney.com\u002Fpub\u002Fwebtg_hskh_act_zixun_01_01_01_0)\n\n（国内A股找不到第二家，像苏垦农发依托百万亩自有连片高标准农田，持续产出真实大田数据训练神农慧种农业AI智能体；苏垦实现天空地一体化数据闭环，苏垦智云是农林牧渔唯一工信部信创典型案例，智慧农业+低空经济双主线落地。）\n\n- 空中：多光谱无人机集群开展农田巡测；\n\n![Image 1](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FB9B393E75E9CCC55E11A2686662765C9_w1080h720.jpg)\n\n- 地面：农田四情监测传感器、北斗智能农机；\n\n![Image 2](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F4D780511671CDAB50B4A1CCFE057703D_w1339h892.jpg)\n\n- 云端：苏垦智云平台与神农慧种AI模型，形成「采集数据→AI分析研判→输出水肥植保方案→农机落地执行」完整闭环。\n\n![Image 3](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F1059027DFA2A605F6861D7C69D6D4309_w1440h1080.jpg)\n\n百万亩自有连片农田源源不断产出真实田间数据，持续迭代优化AI模型。国内很多农业AI企业仅拥有小片试验田，唯有苏垦农发拥有大规模现代农业实景数据用于农业模型训练。\n\n苏垦智云平台，也是全国农林牧渔领域唯一入选工信部信创典型案例的农业数字化平台。苏垦农发一一智慧农业与低空经济的天空地一体化闭环落地，AI大田规模化实体应用！\n\n苏垦农发打造天空地一体化智慧农业完整体系，AI大田并非实验室试验，而是在百万亩自有连片农田实现规模化落地运行。\n\n1、空中：多光谱无人机集群常态化农田巡测，低空遥感采集作物长势、病虫害、墒情数据；依托农业农村部低空技术创新重点实验室，主攻低空多模态农情感知。\n\n2、地面：全域农田“四情”监测传感器、北斗智能农机、智能灌溉终端，实时采集土壤、苗情、虫情、气象数据。\n\n3、云端：苏垦智云平台 神农慧种农业AI智能体，形成完整闭环：采集田间数据→AI模型分析研判→输出水肥、植保作业处方→下发农机执行落地。\n\n4、核心稀缺壁垒：手握百万亩自有连片高标准农田，源源不断产出真实大田实景数据，持续迭代训练神农慧种AI模型。\n\n国内绝大多数农业AI企业，仅拥有小片试验田做演示；苏垦是少数拥有大规模真实农业场景用于模型训练与生产验证的实体龙头。\n\n5、苏垦智云一体化平台，也是全国农林牧渔领域唯一入选工信部信创典型案例的农业数字化平台，国产化底层架构，是农业数字化可复制的标杆样板。配套全国首个农业农村部农业低空技术创新重点实验室（苏垦为依托单位、河海大学共建），同步布局低空经济与智慧农业新质生产力。\n\n2026-09-18 11:16:07 作者更新了以下内容\n\n全球领先的风险咨询公司Verisk Maplecroft 在周四（9月17日）发布的一份报告中表示，极端天气灾害将加剧亚洲的粮食安全风险，并可能在印度、印尼和菲律宾等脆弱的国家引发动荡。\n\n![Image 4](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F9ACF9C92E7B35A242970CDC4D55B8DA9_w1080h15645.jpg)\n\n![Image 5](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FFE1BF075831D4DAEB94ADAE924123EE9_w1080h2400.jpg)\n\n2026-09-18 21:02:06 作者更新了以下内容\n\n苏垦农发一一 AI赋能农业真实落地案例：临海农场——国内首个10万亩级无人值守巡田农场（核心标杆）\n\n地点：江苏盐城临海农场，苏垦智慧农业科技园\n\n1. 空中低空遥感AI巡田\n\n![Image 6](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FE4E4DDE3ED1EEF061A2D631622A46F73_w1424h800.jpg)\n\n多光谱无人机集群常态化巡航，采集苗情、墒情、病虫害影像数据，AI自动识别长势差异、病斑，生成热力图；替代人工徒步巡田，十几分钟就能完成万亩农田普查。依托农业农村部低空技术创新重点实验室，开展低空多模态农情感知研究。\n\n2. AI智能光伏远程灌溉系统\n\n![Image 7](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F9FF2DAAEBED53D7BB8C825B388C102BD_w1424h800.jpg)\n\n万亩稻田布设太阳能智能闸门，通过土壤墒情传感器采集数据，AI分析土壤缺水程度，手机APP一键远程开关水渠闸门。\n\n量化效果：过去管500亩农田，人工开关闸门半天；现在2分钟完成全部闸门调控，灌溉效率提升20倍，每亩节约管水人工成本约30元。\n\n3. AR眼镜AI虫害识别\n\n![Image 8](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F9C7DFBF3F14BDE7E73309CA953C0C8E7_w1424h800.jpg)\n\n农技人员佩戴AR眼镜在田间巡查，拍摄虫体，AI毫秒级识别稻飞虱等害虫种类、统计虫口密度，识别准确率＞95%，自动推送防治方案，新手农技员也能快速判别田间虫害。\n\n4. AI变量施肥无人机作业\n\n![Image 9](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FC257CCA1B1E8AE1621C403E96DF222EE_w1424h800.jpg)\n\nAI读取水稻营养、长势数据，为每一块条田生成独立追肥处方，无人机分区精准施肥，一地一策，实现肥药双减，农药化肥年均用量下降约3%。\n\n5. 北斗智能农机 AI收割决策\n\n![Image 10](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F00A2AE87C3E170BFBFE610FABE2556A5_w1424h800.jpg)\n\n北斗导航插秧机、无人收割机，AI根据成熟度、含水率数据，指导分块错峰收割，减少粮食收割损耗。\n\n[恭喜解锁12个月手机L2专属领取资格，立即领取>>](https:\u002F\u002Facttg.eastmoney.com\u002Fpub\u002Fwebtg_hskh_act_zixun_01_01_01_0)\n\n暗盘资金榜已更新!这些个股\u002F板块可以关注>\n\n追加内容\n\n本文作者可以追加内容哦 !\n\n**郑重声明：**用户在社区发表的所有信息将由本网站记录保存，仅代表作者个人观点，与本网站立场无关，不对您构成任何投资建议，据此操作风险自担。**请勿相信代客理财、免费荐股和炒股培训等宣传内容，远离非法证券活动。请勿添加发言用户的手机号码、公众号、微博、微信及QQ等信息，谨防上当受骗！**\n\n[![Image 11](https:\u002F\u002Favator.eastmoney.com\u002Fqface\u002F9825094237066000\u002F360)](https:\u002F\u002Fi.eastmoney.com\u002F9825094237066000)\n\n总收益 20日收益 日收益\n------\n\n历史收益率走势(%)\n\nChart\n\n代码 名称 最新价 涨跌幅\n[查看更多](http:\u002F\u002Figuba.eastmoney.com\u002F9825094237066000)\n\n浪客视频\n\n![Image 12](https:\u002F\u002Fnp-newspic.dfcfw.com\u002Fdownload\u002FD25261481966621695940_w340h340.jpg)\n\n![Image 13](https:\u002F\u002Fgbapi.eastmoney.com\u002Fshareopt\u002Fweb\u002Fweb_click.gif?id=20260918101757264727920&type=20&version=200&product=EastMoney&plat=Web&deviceid=caifuhao)\n\n郑重声明：东方财富网发布此信息的目的在于传播更多信息，与本站立场无关。东方财富网不保证该信息（包括但不限于文字、视频、音频、数据及图表）全部或者部分内容的准确性、真实性、完整性、有效性、及时性、原创性等。相关信息并未经过本网站证实，不对您构成任何投资建议，据此操作，风险自担。","东方财富财富号\u002F苏垦农发","2026-09-18T00:00:00Z","报道",69,{"impact":67,"substance":17,"depth":144,"authority":145,"freshness":13,"relevant":22,"comment":146},14,5,"苏垦农发百万亩自有农田上实现天空地一体化AI闭环，含临海农场10万亩无人巡田等量化案例，产业参考价值较高，但来源为财富号自媒体、宣传色彩浓，权威性偏弱。",[148],{"name":139,"url":136},[73,150,27,151,29,152],"低空经济","智能农机","数字农田",[154,155],"苏垦农发 神农慧种 AI大模型","临海农场 无人值守巡田","苏垦农发神农慧种AI大模型-2898","2026-09-19T00:06:07.612319Z",{"id":159,"title":160,"url":161,"summary":162,"summary_zh":163,"content":9,"source_name":164,"source_url":161,"published_at":165,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":166,"score_detail":167,"sources":170,"tags":172,"search_phrases":175,"slug":178,"view_count":36,"doi":179,"paper":180,"created_at":203},2804,"A Novel Data-Driven Framework for Stubble Burning Detection Using NASA FIRMS and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02584-x","A Novel Data-Driven Framework for Stubble Burning Detection Using NASA FIRMS and Machine Learning。Journal of the Indian Society of Remote Sensing","一种基于NASA FIRMS和机器学习的新型数据驱动秸秆焚烧检测框架。《印度遥感学会杂志》","Journal of the Indian Society of Remote Sensing","2026-09-17T00:00:00Z",76,{"impact":168,"substance":18,"depth":17,"authority":68,"freshness":13,"relevant":22,"comment":169},15,"该论文提出基于NASA FIRMS卫星数据与机器学习的秸秆焚烧检测新框架，方法新颖、数据规模明确，对农业遥感监测有实质参考价值，但属细分领域学术进展，影响力限于专业圈层。",[171],{"name":164,"url":161},[27,74,29,173,174],"秸秆焚烧","卫星数据",[176,177],"农业人工智能 卫星数据 机器学习 秸秆焚烧","农业人工智能 卫星数据","农业人工智能卫星数据机器学习秸秆焚烧-2804","10.1007\u002Fs12524-026-02584-x",{"doi":179,"openalex_id":181,"authors":182,"venue":164,"cited_by_count":36,"oa_url":9,"card":198,"direction":86,"ingested_from":53},"W7213455429",[183,186,188,191,193,196],{"name":184,"orcid":185},"Mohit Dua","https:\u002F\u002Forcid.org\u002F0000-0001-7071-8323",{"name":187,"orcid":9},"Oshin Rastogi",{"name":189,"orcid":190},"Ashish Saini","https:\u002F\u002Forcid.org\u002F0000-0003-3061-2342",{"name":192,"orcid":9},"Raviya",{"name":194,"orcid":195},"Nidhi Chakravarty","https:\u002F\u002Forcid.org\u002F0000-0002-5454-1561",{"name":197,"orcid":9},"Shelza Dua",{"tldr":199,"method":200,"finding":201,"direction":51,"opportunity":202},"提出基于NASA FIRMS与机器学习的数据驱动框架，用于检测秸秆焚烧。","使用NASA FIRMS火点数据结合机器学习分类算法。","该框架能有效识别秸秆焚烧事件，提升检测精度。","可结合多源遥感与深度学习，提升小尺度焚烧检测与实时预警能力。","2026-09-17T23:30:59.313408Z",{"id":205,"title":206,"url":207,"summary":208,"summary_zh":209,"content":9,"source_name":210,"source_url":207,"published_at":165,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":98,"score_detail":211,"sources":213,"tags":215,"search_phrases":218,"slug":221,"view_count":36,"doi":222,"paper":223,"created_at":252},2801,"A comprehensive survey on machine vision applications in precision agriculture: current trends and future perspectives","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41060-026-01278-4","A comprehensive survey on machine vision applications in precision agriculture: current trends and future perspectives。International Journal of Data Science and Analytics","精准农业中机器视觉应用的综合综述：当前趋势与未来展望。《国际数据科学与分析杂志》","International Journal of Data Science and Analytics",{"impact":17,"substance":18,"depth":19,"authority":68,"freshness":100,"relevant":22,"comment":212},"核心期刊发表的机器视觉精准农业综述，方法梳理与趋势判断具参考价值，但属综述类论文，产业影响有限。",[214],{"name":210,"url":207},[73,27,216,29,217],"精准农业","机器视觉",[219,220],"农业人工智能 智慧农业 机器视觉 精准农业","农业人工智能 智慧农业","农业人工智能智慧农业机器视觉精准农业-2801","10.1007\u002Fs41060-026-01278-4",{"doi":222,"openalex_id":224,"authors":225,"venue":210,"cited_by_count":36,"oa_url":9,"card":247,"direction":86,"ingested_from":53},"W7213471057",[226,228,230,232,235,237,239,242,245],{"name":227,"orcid":9},"Shirun Gu",{"name":229,"orcid":9},"Xinyuan Fan",{"name":231,"orcid":9},"Lihui Zhu",{"name":233,"orcid":234},"Caixia Song","https:\u002F\u002Forcid.org\u002F0000-0003-3897-7629",{"name":236,"orcid":9},"Lei Mu",{"name":238,"orcid":9},"Zichen Zhang",{"name":240,"orcid":241},"Rui Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-8634-3519",{"name":243,"orcid":244},"Tong Xu","https:\u002F\u002Forcid.org\u002F0000-0001-5564-192X",{"name":246,"orcid":9},"Zhiyuan Zhang",{"tldr":248,"method":249,"finding":250,"direction":86,"opportunity":251},"综述机器视觉在精准农业中的应用现状与未来趋势。","文献综述，梳理机器视觉在精准农业中的技术路线。","机器视觉已广泛用于作物监测、病虫害识别等，但落地仍受数据与算力限制。","可聚焦轻量化模型与边缘部署，解决田间实时性与数据稀缺问题。","2026-09-17T23:30:54.103781Z",{"id":254,"title":255,"url":256,"summary":257,"summary_zh":258,"content":9,"source_name":259,"source_url":256,"published_at":260,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":98,"score_detail":261,"sources":263,"tags":265,"search_phrases":268,"slug":271,"view_count":36,"doi":272,"paper":273,"created_at":298},2791,"Integrating multi-source data and support vector machine to assess the spatio-temporal pattern of land degradation in the Eastern Cape of South Africa","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.indic.2026.101524","Land degradation remains a major environmental challenge, particularly in semi-arid and heterogeneous landscapes, where interactions between vegetation loss and soil exposure are complex and spatially dynamic. This study, therefore, seeks to evaluate the spatial extent of land degradation and drivers over time (2005 - 2025) using Landsat data series and support vector machine (SVM) in the Keiskamma Catchment of South Africa. Degraded land followed a non-monotonic trajectory: it declined from ∼197 km 2 in 2005 to ∼157 km 2 in 2015 (a temporary contraction of 20.3%, consistent with short-term restoration and land-use shifts), before rising sharply and unsustainably to ∼328 km 2 by 2025 (a 108.9% increase relative to 2015, and a net increase of 66.5% over the full two-decade period), largely at the expense of grassland and agricultural land. Furthermore, the findings show that soil-sensitive indicators, particularly BSI and SWIR spectral bands, play a crucial role in determining degraded land. In contrast, vegetation indices such as NDVI contribute less under degraded conditions because degraded areas were severely dominated by exposed soil rather than vegetation. Correlation matrix analysis further reveals a temporal shift from mixed soil–vegetation spectral relationships toward strong soil-dominated reflectance patterns by 2025, indicating advanced degradation stages. Overall, the integration of SVM classification with VIF and SHAP provides a transparent, reliable, and spatially explicit framework for monitoring land degradation. The findings support land degradation neutrality monitoring and provide critical insights for sustainable land-management planning in support of Sustainable Development Goal (SDG) 15.3.","土地退化仍然是一项重大环境挑战，尤其是在半干旱和异质性景观中，植被丧失与土壤裸露之间的相互作用复杂且具有空间动态性。因此，本研究旨在利用Landsat数据序列和支持向量机（SVM），评估南非Keiskamma集水区2005—2025年间土地退化的空间范围及其驱动因素。退化土地呈非单调变化轨迹：从2005年的约197 km²下降至2015年的约157 km²（暂时收缩20.3%，与短期恢复和土地利用变化相一致），随后急剧且不可持续地上升至2025年的约328 km²（较2015年增加108.9%，在整个二十年期间净增加66.5%），且主要以草地和农用地为代价。此外，研究结果表明，土壤敏感指标，尤其是BSI和SWIR光谱波段，在判定退化土地方面发挥着关键作用。相比之下，NDVI等植被指数在退化条件下贡献较小，因为退化区域严重以裸露土壤为主，而非植被。相关矩阵分析进一步揭示，到2025年，光谱关系由土壤—植被混合关系向强烈的土壤主导反射模式发生时间转变，表明退化已进入后期阶段。总体而言，将SVM分类与VIF和SHAP相结合，为监测土地退化提供了一个透明、可靠且具有空间显式性的框架。研究结果支持土地退化零增长监测，并为支持可持续发展目标（SDG）15.3的可持续土地管理规划提供了关键见解。","Environmental and Sustainability Indicators","2026-09-16T00:00:00Z",{"impact":168,"substance":67,"depth":17,"authority":68,"freshness":100,"relevant":22,"comment":262},"基于Landsat时序与SVM\u002FSHAP的南非土地退化监测研究，方法透明可复现，对农业遥感与土地退化中性监测有参考价值，但属区域案例、非国内三农直接政策信息。",[264],{"name":259,"url":256},[266,74,267,28,29],"农业遥感","可持续发展",[269,270],"可持续发展 农业遥感 土地退化 机器学习","可持续发展 农业遥感","可持续发展农业遥感土地退化机器学习-2791","10.1016\u002Fj.indic.2026.101524",{"doi":272,"openalex_id":274,"authors":275,"venue":259,"cited_by_count":36,"oa_url":292,"card":293,"direction":51,"ingested_from":53},"W7213298151",[276,278,281,284,286,289],{"name":277,"orcid":9},"Mandisa Zameko",{"name":279,"orcid":280},"Kgabo Humphrey Thamaga","https:\u002F\u002Forcid.org\u002F0000-0002-2305-9975",{"name":282,"orcid":283},"Mthunzi Mndela","https:\u002F\u002Forcid.org\u002F0000-0002-2384-6856",{"name":285,"orcid":9},"Matthieu Tshanga",{"name":287,"orcid":288},"Nobert Tafadzwa Mukomberanwa","https:\u002F\u002Forcid.org\u002F0009-0003-1896-9813",{"name":290,"orcid":291},"Mohamed Zhran","https:\u002F\u002Forcid.org\u002F0000-0002-1112-387X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2665972726004137\u002Fpdf",{"tldr":294,"method":295,"finding":296,"direction":51,"opportunity":297},"用Landsat与SVM评估南非Keiskamma流域2005-2025年土地退化时空格局。","Landsat时序数据、SVM分类，结合VIF与SHAP做特征解释。","退化面积先降后升，2025年达328km²，土壤光谱指标比NDVI更关键。","可将该SVM-SHAP框架迁移到其他半干旱区，并耦合气候与土地利用驱动做退化预警。","2026-09-17T23:30:36.054539Z"]