[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3314":3,"related-3314":73},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":8,"secondary_topics":30,"classification":31,"search_phrases":68,"slug":71,"view_count":39,"doi":8,"paper":8,"created_at":72},3314,"《安徽日报》：深耕沃土四十载 智绘粮仓千亿斤——省农科院青年博士团队接过砂姜黑土改造接力棒 研发高产稳产技术套餐","https:\u002F\u002Fwww.aaas.org.cn\u002F4303153\u002F71232014.html","9月19日，安徽省农业科学院农业科技成果转化应用项目砂姜黑土障碍消减与作物周年高产高效技术转化应用观摩培训会在蒙城县举办。蒙城县马店试验站的长期定位试验田里，土壤墒情监测仪无声运转，将地下不同深度的土壤水分、温度数据上传云端；田埂上，青年科研人员轻点平板电脑，一张彩色施肥处方图跃然屏上。1978年起，省农科院土肥所在马店建立研究基站——蒙城马店试验站，是目前全国研究历史最长、数据最为连续的砂姜黑土长期监测定位点。安徽砂姜黑土面积2300余万亩，约占全国总面积的45%。",null,"安徽省农业科学院 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1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-ezhpy3drpa\u002F2f2361f18cc94a13886a009d829e2b54~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791849984&x-signature=l8Il0pGejukmIxi9yISnxYh76jA%3D)\n金秋九月，淮北平原秋收秋种正忙。9月19日，安徽省农科院农业科技成果转化应用项目“砂姜黑土障碍消减与作物周年高产高效技术转化应用”观摩培训会在蒙城县举办，通过技术讲座、展板介绍、实物展示、田间观摩等多元化形式，全面展示砂姜黑土障碍消减、肥沃耕层构建和高效施肥关键技术的研究路径和应用成效。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-ezhpy3drpa\u002F59dbb265baf94778a6389fe26ad11a67~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791849984&x-signature=yDCtyYTDQL%2F5%2FSAcMUaahaHxGA8%3D)\n蒙城县马店试验站的长期定位试验田里，土壤墒情监测仪无声运转，将地下不同深度的土壤水分、温度数据上传云端；田埂上，青年科研人员轻点平板电脑，一张彩色“施肥处方图”跃然屏上，清晰显示哪块地该补氮、哪块地宜加锌。\n\n这里是“十五五”开局之年的江淮粮仓腹地。千亿斤产能从哪里来？安徽用一块土壤的“命运逆袭”作答。安徽省农科院土肥所与蒙城县院地携手四十余年，几代科研人员接力攻关，让曾经“旱、涝、僵、瘠”的砂姜黑土从低产迈向高产；如今，新一代科研人员接过接力棒，将改土培肥技术、高效施肥产品与智能水肥装备集成落地，推动这片土地向“高产且稳产”跃升。一场从“改土增粮”到“稳产增粮”的科技接力，正在为“千亿斤江淮粮仓”夯实最坚实的耕地根基。\n\n**“雨天一包糟，晴天一把刀”：四十年前向“僵土”宣战**\n\n砂姜黑土是我国黄淮海平原特有的中低产田土壤，全国面积约6000万亩，六成以上集中在淮北平原，其中安徽省砂姜黑土面积2300余万亩，约占全国总面积的45%；砂姜黑土土壤黏粒含量高、胀缩性强，干时收缩坚硬、湿时膨胀黏闭，农民形容为“雨天一包糟，晴天一把刀”，旱、涝、僵、瘠四“病”缠身，上世纪六七十年代小麦亩产不足50公斤。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-ezhpy3drpa\u002Fa8b316c6133e4d9bb83e484df14d6717~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791849984&x-signature=LNOveSyA3Ak7pn2g4d8dpTRgdsU%3D)\n为拔掉穷根，1978年起，省农科院土肥所在马店建立研究基站——蒙城马店试验站，是目前全国研究历史最长、数据最为连续的砂姜黑土长期监测定位点。1982年布设的肥料长期定位试验从未中断，几代科研人员采集的土壤样品编号归档、记录的观测数据积累成册，构建起完整的砂姜黑土肥力演变数据库。以此为支撑，“淮北砂姜黑土区综合治理开发配套技术”获1991年安徽省科学技术进步奖一等奖，为黄淮海平原中低产田改良提供了重要科技支撑。随着配方施肥、秸秆还田、深耕深松、“一喷三防”等技术在蒙城全县和沿淮砂姜黑土地区迅速推开，小麦亩产历经两次跃升，八十年代跃升至200公斤，2003年达到450公斤，之后小麦亩产陆续站上500公斤台阶。\n\n**密植田里的新考题：地力“托不住”高产的“胃口”**\n\n站在亩产500kg的新台阶上，新一代科研人员却看到了新隐忧。\n\n“十五五”时期，粮食增产主攻方向是提单产。高产品种持续更新，种植密度不断加密：玉米亩株数从4000株左右增至5500株以上，小麦以大群体、高生物量夺高产。地上部的“胃口”越来越大，地下部的“饭碗”却没有同步变大。\n\n长期以来，大型机械作业与传统旋耕作业并存，导致耕层浅薄、土壤库容减小。砂姜黑土有效耕层普遍不足20厘米，全省平均仅14.6厘米，犁底层坚硬紧实，土壤蓄水保肥能力差、缓冲性能弱。农业生产中重用轻养的土地利用模式，以及连年高产出从土壤中带走大量养分，导致地力损耗大，土壤养分失衡——密植作物一旦遭遇强风或连阴雨，极易发生倒伏；降水量大时，又因土壤通透性差而导致作物渍害。加之密植条件下施肥作业难度加大，肥料损耗较大，肥料利用率持续走低，良种高产潜力受到明显制约，当前地力已难以托举地上部密植群体的高产能需求。\n\n**技术套餐：良田良种良法良机融合**\n\n随着智能化、信息化的快速发展，传统栽培模式、机械装备与农资产品已难以适配精准化、轻简化、绿色化的生产需求；围绕密植群体构建下的高产稳产、韧性农业发展目标，一支以詹林川、汪霄、蒋发辉、惠晓丽、晋林等青年博士为骨干的攻关团队在马店试验站扎下营盘，在老一辈打下的地基上展开新一轮攻关，组合成砂姜黑土高产稳产的“技术套餐”。\n\n**套餐一，“旋松一体”+“苗带旋耕”深松浅旋耕层厚沃技术。**针对砂姜黑土结构不良、耕层浅薄问题，通过“深松浅旋”耕作融合有机物料（秸秆、有机肥）还田增碳扩容，打破犁底层，降低土壤容重，提升耕层蓄水保肥能力。研发的“苗带旋耕播种机”（玉米季）采用行间免耕、苗带局部带状旋耕的少耕模式，集施肥、玉米精量播种于一体，针对性破除砂姜黑土硬土层，解决传统免耕播种质量差、出苗率低的问题，行间保留原有土层结构，为玉米根系穿插提供稳定介质，强化根系锚定、防止雨季倒伏；“旋松一体耕作机”（小麦季）可一次性完成深松、旋耕、碎土清平，较传统深翻一遍加旋耕两遍减少作业次数、降低机械能耗，同时避免重型机械反复碾压导致耕层变浅、亚层土壤紧实等问题。\n\n![Image 4](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-ezhpy3drpa\u002Fa3cdb2ae814c423183ab96c57606faf7~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791849984&x-signature=8FcbMw3U4AoBU1nfXLsP4S7xanY%3D)\n**套餐二，“分层施肥”立体供养一次性轻简施肥技术。**针对密植群体肥料过量“前期烧苗、后期脱肥”的痼疾，将缓控释肥料在耕层土壤中“立体布防”：生物有机肥深施打底，改良土壤、培育地力；高氮缓控失肥料分层施入，苗期少量养分精准提苗，中后期养分按需释放；辅以锌肥、硼肥等功能性肥料结合“一飞三防”无人机叶面喷施，精准补给，缺啥补啥。种肥同播、分层深施、一次完成、不再追肥。示范数据显示：肥料总施用量减少10%～15%，化肥利用率提高10%～20%，玉米增产5%～12%，每亩节本增收约135元；省工、省肥、增产，一举三得。\n\n**套餐三，麦—玉\u002F豆周年水肥一体化技术。**淮北平原水热时空分布不均，旱涝急转频发，传统大水漫灌既浪费水肥，又难以精准匹配作物需求。田间水肥一体化管网整齐排列，让“测土配方施肥”从纸面落到管网；土壤墒情监测仪像“听诊器”，实时感知不同深度土壤的水分与温度；墒情数据叠加气象预报与作物生长模型，由智能算法自动生成灌溉施肥处方——灌多少水、追多少肥、何时执行，种植户在手机上轻轻一点即可远程启停。按需供给、少量多次，水肥一体化把每一滴水、每一粒肥都“算”到作物嘴里，密植群体抗逆稳产能力显著增强。淮北粮田从此有了“旱能精准补、涝能及时控”的主动权。\n\n更深的底气，来自四十余年长期定位试验沉淀的“数据家底”。老一辈采集的土壤样品、记录的肥力数据，如今正被整理成数字化数据库，成为训练智能决策模型、绘制变量施肥处方图的“养料”。老数据“喂”出新算法，长期定位试验在智能农业时代焕发新生。\n\n**套餐四，“锌生物强化”增产提质。**淮北砂姜黑土多为石灰性土壤，锌等微量元素有效性低，作物“隐性饥饿”既拖产量后腿，也拉低品质。科研人员运用生物强化思路，基施与叶面喷施相结合，在小麦生育期精准补锌，既提高籽粒产量，又显著提升籽粒锌含量，让普通麦田产出“富锌小麦”。这项技术一头连着减肥增效、单产提升，一头连着功能农业、优质优价，为种粮农民打开了“提质增收”的第二本账。\n\n**薪火传承、成效斐然，良田沃土交答卷**\n\n四十余年，从“低产”到“高产”，从“改土增粮”到“稳产增粮”，变的是技术、装备与算法，不变的是那句话——良田才能出良粮。经过综合治理，昔日砂姜黑土的“粘僵”属性明显改善：土壤有机质含量提升5%～8%，土壤容重降低7%～9%，田间持水量提高15%以上，膨胀系数下降10%以上，化肥利用率提高10%～20%，粮食周年增产10%以上，实现了砂姜黑土区域耕地质量与周年粮食产能的协同提升，有力服务国家新一轮千亿斤粮食产能提升行动。\n\n“十五五”新征程上，安徽正以科技创新深挖粮食增产潜能，全力建设千亿斤江淮粮仓。而在广袤的淮北平原，那块被三代科研人“驯服”又“武装”起来的砂姜黑土，正以连片翻涌的金黄，给出中国饭碗里的安徽答案。\n\n**来源**：安徽日报\n\n**时间**：2026-9-23\n\n**地点**：安徽亳州\n\n**采编**：姜德惠\n\n**排版**：刘祖尚\n\n![Image 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2026-10-01","2026-10-01T10:53:00Z",75,{"impact":85,"substance":86,"depth":16,"authority":87,"freshness":88,"relevant":20,"comment":89},22,21,9,5,"系统呈现砂姜黑土四十年改良与智能水肥技术集成成效，数据翔实、技术路径清晰，具行业参考价值。",[91],{"name":81,"url":78},[25,93,94,27,28],"水肥一体化","耕地质量",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":97},{"candidates":98,"agricultural_evidence":40,"reason":67},[99,104,108,112,117],{"code":38,"score":100,"supported":40,"title":101,"abstract":102,"tags":103},4,[],[43],[93],{"code":52,"score":100,"supported":40,"title":105,"abstract":106,"tags":107},[],[55],[94],{"code":58,"score":20,"supported":13,"title":109,"abstract":110,"tags":111},[],[],[27],{"code":113,"score":20,"supported":13,"title":114,"abstract":115,"tags":116},"H1",[],[],[94],{"code":63,"score":20,"supported":13,"title":118,"abstract":119,"tags":120},[],[],[25],[122,123],"安徽农科院 砂姜黑土 蒙城","砂姜黑土 水肥一体化 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部署智慧农业与四季接续","https:\u002F\u002Fjx.ifeng.com\u002Fc\u002F8xPB4YCdgGF","《江西省「十五五」农业农村现代化规划》（赣府发〔2026〕16号）围绕32项重点任务展开，明确推进信息技术与农业深度融合，加快农业人工智能技术创新与场景拓展，建设农业农村大数据平台。规划还部署井冈山国家农业科技园区升建国家农高区，推广「村播」「田播」电商直播带货，并开展农产品碳足迹管理与农业碳交易。","（一）提升粮食等重要农产品供给保障水平。坚持产量产能、生产生态、增产增收一起抓，深入实施藏粮于地、藏粮于技战略，落实新一轮粮食产能提升行动，实施粮油作物大面积单产提升行动，建设吨粮片、高产片，促进良田良种良机良法集成增效。优化农产品种植结构，稳定水稻、油菜生产，大力发展油茶产业，提升大豆产能。优化生猪产能综合调控机制，推进牛羊产业扩量提质，促进畜禽养殖节本提质增效。推动农产品品种培优和品质提升，增加营养、健康、风味农产品供给。完善价格、补贴、保险“三位一体”的种粮农民收益保障机制，建立健全与周边省份粮食产销协作机制，强化粮食产购储加销协同保障。\n\n（二）构建多元化食物供给体系。践行大食物观，坚持农林牧渔并举，全方位、多用途开发食物资源。提升“菜篮子”产业质量和效益，推动蔬菜、水果、茶叶、中药材产业优质发展。丰富畜产品供给，发展蜂、兔、肉鸽、鹌鹑等特色养殖。积极发展大水面生态渔业，科学推进稻渔综合种养。发展林下经济，壮大林药和森林食品。发展富硒食品与功能食品，开发新型食品资源。\n\n（三）加强耕地保护与质量提升。严格落实耕地保护党政同责，严守耕地保护红线。统筹农用地布局优化，严格耕地占补平衡管理，规范补充耕地质量验收。科学开展红壤改良利用，推进酸化耕地治理，分阶段实施耕地有机质提升行动。分区分类高质量推进高标准农田建设，对新建和改造提升实行差异化投入，优先在鄱阳湖平原、赣抚平原、吉泰盆地等区域推进整区域建设，逐步把具备条件的永久基本农田建成高标准农田。完善高标准农田立项、建设、验收和管护机制，编制高标准农田建设全流程工作规范，统一建设标准、施工工艺、管理流程，压实管护责任。开展高标准农田整治提升行动，按照“集中修复一批、改造提升一批、疏通灌溉一批、置换退出一批”分类施策，逐步补齐设施短板、消除存量问题。健全完善问题发现响应处置机制。实施大中型灌区现代化建设与改造，推动井冈灌区、赣抚平原灌区等项目建设，强化高标准农田渠系与灌区骨干工程衔接。\n\n（四）提升农业防灾减灾救灾能力。发展韧性农业，完善农业气象、水文、地质灾害等监测预警机制。推进防洪排涝抗旱设施建设，持续开展农田沟渠修复整治、中小河流和平原涝区治理。完善农机应急救灾设施装备条件,健全和落实救灾机具配置、救灾备荒种子储备和应急调用机制，加快补齐烘干、仓储等设施装备短板。强化病虫疫情和重大动物疫病监测预警、联防联控和应急处置，建强县乡动植物疫病防控专业队伍。","凤凰网江西 2026-10-21","2026-10-21T00:00:00Z","政策",80,{"impact":85,"substance":137,"depth":16,"authority":138,"freshness":87,"relevant":20,"comment":139},23,8,"省级「十五五」农业农村现代化规划全文，条款与数字细节充实，涉及智慧农业、高标准农田、粮食产能等实质部署，值得进入每日精选。",[141],{"name":132,"url":129},[25,143,144,145,27],"高标准农田","十五五规划","大食物观",{"code":147,"slug":148,"name":149,"path":150,"parent_code":151},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",[],{"status":154,"locked":13,"version":33,"method":34,"evidence":155},"classified",{"candidates":156,"agricultural_evidence":40},[157,164,168,174,180,184,189,193],{"code":158,"score":159,"supported":40,"title":160,"abstract":162,"tags":163},"J2",11,[161],"农业农村现代化",[161],[],{"code":63,"score":87,"supported":40,"title":165,"abstract":166,"tags":167},[25],[],[25],{"code":147,"score":19,"supported":40,"title":169,"abstract":170,"tags":173},[],[171,172],"农业碳","碳足迹",[],{"code":175,"score":39,"supported":40,"title":176,"abstract":177,"tags":179},"I2",[],[178],"农业人工智能",[],{"code":58,"score":20,"supported":13,"title":181,"abstract":182,"tags":183},[],[],[27],{"code":185,"score":20,"supported":13,"title":186,"abstract":187,"tags":188},"E2",[],[],[145],{"code":113,"score":20,"supported":13,"title":190,"abstract":191,"tags":192},[],[],[143],{"code":194,"score":20,"supported":13,"title":195,"abstract":196,"tags":197},"J1",[],[],[144],[199,200],"江西 十五五 农业农村现代化规划","江西 高标准农田 鄱阳湖平原","江西十五五农业农村现代化规划-5498",0,"2026-10-22T00:06:58.106992Z",{"id":205,"title":206,"url":207,"summary":208,"summary_zh":209,"content":8,"source_name":210,"source_url":207,"published_at":211,"category":212,"cover_url":8,"hotness":12,"is_selected":13,"score":213,"score_detail":214,"sources":220,"tags":222,"primary_topic":226,"secondary_topics":232,"classification":242,"search_phrases":292,"slug":295,"view_count":296,"doi":8,"paper":297,"created_at":309},5013,"REMOTE SENSING AND GIS-BASED ASSESSMENT OF SOIL SALINITY IN IRRIGATED LANDS: METHODS, CHALLENGES AND PROSPECTS FOR PRECISION AGRICULTURE","http:\u002F\u002Fwww.arabianjournalofscience.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F1223","Soil salinization is a major land degradation process affecting the sustainability of irrigated agriculture, particularly in arid and semi-arid regions. Conventional soil sampling provides essential information about salinity levels but may be insufficient for continuous monitoring of large agricultural territories because of spatial variability, sampling costs, and temporal limitations. Remote sensing (RS) and geographic information systems (GIS) offer complementary approaches for identifying spatial patterns of salinity, monitoring land conditions, and supporting agricultural management. This article examines the scientific and methodological foundations of integrating multispectral satellite imagery, spectral indices, geospatial analysis, and machine learning for soil salinity assessment in irrigated lands. Particular attention is given to Landsat and Sentinel-2 imagery, the interpretation of vegetation and soil reflectance indicators, the role of electrical conductivity measurements in model calibration, and the limitations associated with vegetation cover, soil moisture, and seasonal variability. The analysis indicates that reliable salinity assessment requires the integration of satellite observations with field measurements and environmental variables rather than the isolated application of spectral indices. The article proposes a methodological framework for developing spatially explicit salinity assessment systems applicable to irrigated agricultural landscapes, including those of Central Asia. The framework provides a basis for improving monitoring strategies and supporting evidence-based land management without assuming unverified local measurement results.","土壤盐渍化是影响灌溉农业可持续性的主要土地退化过程，在干旱和半干旱地区尤为突出。传统土壤采样可提供盐分水平的基本信息，但由于空间变异性、采样成本和时间限制，可能不足以对大面积农业区域进行持续监测。遥感（RS）和地理信息系统（GIS）为识别盐渍化空间格局、监测土地状况和支持农业管理提供了互补性方法。本文探讨了整合多光谱卫星影像、光谱指数、地理空间分析和机器学习进行灌溉土地土壤盐渍化评估的科学与方法学基础。文章特别关注Landsat和Sentinel-2影像、植被与土壤反射率指标的解译、电导率测量在模型校准中的作用，以及与植被覆盖、土壤湿度和季节变异性相关的局限性。分析表明，可靠的盐渍化评估需要将卫星观测与野外测量和环境变量相结合，而非孤立地应用光谱指数。本文提出了一个方法学框架，用于开发适用于灌溉农业景观（包括中亚地区）的空间显式盐渍化评估系统。该框架为改进监测策略和支持基于证据的土地管理提供了基础，且不以未经核实的当地测量结果为假设前提。","Arabian journal of science.","2026-10-13T00:00:00Z","论文",74,{"impact":215,"substance":216,"depth":217,"authority":218,"freshness":138,"relevant":20,"comment":219},16,20,17,13,"系统综述遥感与GIS在灌溉农田盐渍化评估中的方法框架与挑战，对盐碱地治理和精准农业有参考价值，但属综述性论文，非突破性成果。",[221],{"name":210,"url":207},[25,223,224,225,26],"精准农业","遥感","盐碱地治理",{"code":227,"slug":228,"name":229,"path":230,"parent_code":231},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[233,238],{"code":234,"slug":235,"name":236,"path":237,"parent_code":231},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":52,"slug":239,"name":240,"path":241,"parent_code":151},"soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":154,"locked":13,"version":33,"method":34,"evidence":243},{"candidates":244,"agricultural_evidence":40},[245,251,259,263,268,274,280,286],{"code":227,"score":16,"supported":40,"title":246,"abstract":248,"tags":250},[247],"remote sensing",[224,247,249],"multispectral",[224],{"code":234,"score":217,"supported":40,"title":252,"abstract":254,"tags":258},[253],"GIS",[255,256,253,257],"地理信息","空间分析","geographic information",[],{"code":52,"score":17,"supported":40,"title":260,"abstract":261,"tags":262},[239],[55,239],[225],{"code":63,"score":12,"supported":40,"title":264,"abstract":266,"tags":267},[265],"precision agriculture",[],[25,223],{"code":175,"score":19,"supported":40,"title":269,"abstract":270,"tags":273},[],[271,272],"机器学习","machine learning",[],{"code":275,"score":39,"supported":40,"title":276,"abstract":277,"tags":279},"H3",[],[278],"灌溉",[],{"code":281,"score":39,"supported":40,"title":282,"abstract":283,"tags":285},"H7",[],[284],"干旱",[],{"code":287,"score":39,"supported":40,"title":288,"abstract":289,"tags":291},"I7",[],[290],"methodological framework",[],[293,294],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",2,{"doi":8,"openalex_id":298,"authors":299,"venue":210,"cited_by_count":202,"oa_url":207,"card":302,"direction":306,"ingested_from":308},"W7221843179",[300],{"name":301,"orcid":8},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":303,"method":304,"finding":305,"direction":306,"opportunity":307},"综述遥感与GIS评估灌溉地土壤盐渍化的方法、挑战与前景，并提出空间显式评估框架。","整合多光谱卫星影像、光谱指数、GIS空间分析与机器学习，结合电导率校准。","可靠盐渍化评估需卫星观测与实地测量及环境变量融合，而非孤立使用光谱指数。","农业遥感与作物表型","可探索多源遥感与机器学习融合的盐渍化动态监测，并验证于中亚等干旱灌区。","openalex","2026-10-15T23:30:32.401626Z",{"id":311,"title":312,"url":313,"summary":314,"summary_zh":315,"content":8,"source_name":316,"source_url":313,"published_at":317,"category":212,"cover_url":8,"hotness":12,"is_selected":13,"score":318,"score_detail":319,"sources":321,"tags":323,"primary_topic":326,"secondary_topics":332,"classification":339,"search_phrases":362,"slug":365,"view_count":296,"doi":366,"paper":367,"created_at":388},4427,"Development of Internet of Things-Based Soil Sensors for pH and Phosphorus Monitoring in Paddy Fields: Development, Accuracy, and Implementation Challenges","https:\u002F\u002Fdoi.org\u002F10.56581\u002Fijrerd.11.05.01-08","The development of Internet of Things (IoT) technology provides opportunities for faster and continuous soil monitoring to support precision agriculture.Monitoring soil pH and phosphorus is important in rice cultivation because both parameters are associated with soil chemical conditions and nutrient availability.This review aimed to examine the development of IoT-based soil sensors for monitoring soil pH and phosphorus, including sensor performance, factors affecting measurement results, and potential applications.A literature review was conducted on research and review articles published between 2019 and 2026.The literature was descriptively analyzed and synthesized based on technological development, sensor performance, and environmental and soil characteristics.The results indicate that pH and phosphorus sensors have been developed using various sensing approaches and have demonstrated promising performance under certain conditions; however, measurement results vary across parameters and soil conditions.Sensor performance is influenced by the sensing principle, soil characteristics, environmental conditions, calibration, and validation.IoT-based sensors have the potential to support real-time soil monitoring, but their application as quantitative measurement tools still requires validation and calibration under diverse soil conditions.","物联网（IoT）技术的发展为实现更快、连续性的土壤监测以支持精准农业提供了机遇。监测土壤pH和磷在水稻种植中具有重要意义，因为这两个参数均与土壤化学状况和养分有效性相关。本综述旨在考察用于监测土壤pH和磷的基于物联网的土壤传感器的发展现状，包括传感器性能、影响测量结果的因素以及潜在应用。本文对2019年至2026年间发表的研究论文和综述文章进行了文献综述。基于技术发展、传感器性能以及环境和土壤特征，对文献进行了描述性分析和综合。结果表明，pH和磷传感器已采用多种传感方法进行开发，并在特定条件下展现出良好性能；然而，测量结果因参数和土壤条件的不同而存在差异。传感器性能受传感原理、土壤特性、环境条件、校准和验证的影响。基于物联网的传感器具有支持实时土壤监测的潜力，但其作为定量测量工具的应用仍需在不同土壤条件下进行验证和校准。","International journal of recent engineering research and development.","2026-10-06T00:00:00Z",62,{"impact":18,"substance":16,"depth":17,"authority":87,"freshness":138,"relevant":20,"comment":320},"综述性论文，系统梳理IoT土壤pH与磷传感器的发展、精度与落地挑战，对智慧农业土壤监测方向有参考价值，但非突破性成果。",[322],{"name":316,"url":313},[25,324,223,26,325],"物联网","水稻种植",{"code":327,"slug":328,"name":329,"path":330,"parent_code":331},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[333,338],{"code":334,"slug":335,"name":336,"path":337,"parent_code":231},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":52,"slug":239,"name":240,"path":241,"parent_code":151},{"status":154,"locked":13,"version":33,"method":34,"evidence":340},{"candidates":341,"agricultural_evidence":40},[342,348,353,358],{"code":334,"score":16,"supported":40,"title":343,"abstract":345,"tags":347},[344],"internet of things",[324,344,346],"IoT",[324],{"code":327,"score":17,"supported":40,"title":349,"abstract":351,"tags":352},[350],"paddy",[329,328],[325],{"code":52,"score":354,"supported":40,"title":355,"abstract":356,"tags":357},14,[239],[55,239],[],{"code":63,"score":138,"supported":40,"title":359,"abstract":360,"tags":361},[],[223,265],[25,223],[363,364],"物联网 土壤传感器 稻田","土壤pH 监测","物联网土壤传感器稻田-4427","10.56581\u002Fijrerd.11.05.01-08",{"doi":366,"openalex_id":368,"authors":369,"venue":316,"cited_by_count":202,"oa_url":313,"card":382,"direction":386,"ingested_from":308},"W7220841659",[370,372,375,377,380],{"name":371,"orcid":8},"Fariha Laqania",{"name":373,"orcid":374},"Oviyanti Mulyani","https:\u002F\u002Forcid.org\u002F0000-0003-3941-8896",{"name":376,"orcid":8},"Ade Setiawan",{"name":378,"orcid":379},"Pujawati Suryatmana","https:\u002F\u002Forcid.org\u002F0000-0002-4012-8645",{"name":381,"orcid":8},"Wahyu Daradjat",{"tldr":383,"method":384,"finding":385,"direction":386,"opportunity":387},"综述2019-2026年稻田pH与磷的物联网土壤传感器发展、精度及实施挑战。","文献综述，按技术发展、传感器性能与土壤环境特征归纳分析。","传感器在特定条件下表现良好，但精度受原理、土壤、环境与校准影响，定量应用仍需验证。","智慧农业 \u002F 农业物联网","稻田复杂土壤条件下pH\u002F磷传感器的原位校准与多源数据融合验证是待突破的研究空白。","2026-10-08T23:30:13.316611Z",{"id":390,"title":391,"url":392,"summary":393,"summary_zh":394,"content":8,"source_name":395,"source_url":392,"published_at":317,"category":212,"cover_url":8,"hotness":12,"is_selected":40,"score":396,"score_detail":397,"sources":399,"tags":401,"primary_topic":403,"secondary_topics":404,"classification":406,"search_phrases":432,"slug":435,"view_count":20,"doi":436,"paper":437,"created_at":467},4345,"HyBEAR: a hyperspectral benchmark for bare soil detection","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fessd-18-7301-2026","Detecting bare soil areas is an important step in the analysis of Earth observation data in a variety of Precision Agriculture (PA) applications focused on quantifying soil properties and assessing soil quality. In this paper, we introduce the HyBEAR benchmark – a novel large-scale collection of high-resolution hyperspectral aerial images (with 2 m ground sampling distance) accompanied by manual bare soil annotations verified by domain experts. Usually, the bare soil detection problem is tackled at the pixel level, meaning that detection methods classify all pixels as either bare soil or background. In contrast to this approach, we provide pixel-level annotations for the entire agricultural parcels (if the parcel is labeled as bare soil, then all pixels within that parcel are labeled accordingly), and aim to support the development of methods that identify entire fields with no vegetation. Commonly, such fields undergo further analysis to determine specific soil parameters and characteristics that are important when planning various PA activities, such as fertilization. The HyBEAR benchmark includes (i) the largest-to-date (108 064 591 pixels, corresponding to 43 225 ha) and most heterogeneous dataset for bare soil detection, as well as (ii) the validation procedure (training-test splits and quality metrics) and a set of baseline results, obtained for a set of machine learning bare soil detection models. From the FULL collection of 1954 images in HyBEAR, which we divided into 5 spatially-disjoint folds, we additionally selected a random, stratified subset (MINI) of the images which may be useful for designing and verifying bare soil detection algorithms. Overall, HyBEAR is a step toward standardizing the way the community builds and confronts bare soil detection algorithms in a thorough, reproducible, and unbiased way. The dataset is publicly available at Zenodo: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.17607897 (Wijata et al., 2025).","在多种精准农业（PA）应用中，裸土区域检测是对地观测数据分析的重要步骤，其重点在于量化土壤性质和评估土壤质量。本文介绍了HyBEAR基准——一个全新的大规模高分辨率高光谱航空影像数据集（地面采样距离为2 m），并附有经领域专家验证的人工裸土标注。通常，裸土检测问题在像素层面进行处理，即检测方法将所有像素分类为裸土或背景。与此方法不同，我们为整个农业地块提供像素级标注（如果地块被标记为裸土，则该地块内的所有像素均被相应标记），旨在支持开发能够识别无植被整块田地的方法。通常，此类田地会经过进一步分析以确定特定的土壤参数和特征，这些在规划施肥等各种精准农业活动时至关重要。HyBEAR基准包含：（i）迄今为止规模最大（108 064 591个像素，对应43 225公顷）且最具异质性的裸土检测数据集；（ii）验证流程（训练-测试划分和质量指标）以及一组基线结果，这些结果是在一组机器学习裸土检测模型上获得的。从HyBEAR中全部1954幅图像（我们将其划分为5个空间不相交的折）中，我们额外选择了一个随机分层子集（MINI），该子集可能有助于设计和验证裸土检测算法。总体而言，HyBEAR朝着以全面、可复现且无偏见的方式标准化社区构建和对比裸土检测算法的方向迈出了一步。该数据集已在Zenodo上公开：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.17607897（Wijata et al., 2025）。","Earth system science data",81,{"impact":16,"substance":85,"depth":16,"authority":354,"freshness":87,"relevant":20,"comment":398},"发布迄今最大规模高光谱裸土检测基准数据集，含像素级标注与基线结果，对精准农业土壤分析有实质推动。",[400],{"name":395,"url":392},[25,178,402,224,26],"高光谱",{"code":52,"slug":239,"name":240,"path":241,"parent_code":151},[405],{"code":227,"slug":228,"name":229,"path":230,"parent_code":231},{"status":154,"locked":13,"version":33,"method":34,"evidence":407},{"candidates":408,"agricultural_evidence":40},[409,413,418,423,427],{"code":52,"score":354,"supported":40,"title":410,"abstract":411,"tags":412},[239],[55,239],[],{"code":227,"score":218,"supported":40,"title":414,"abstract":416,"tags":417},[415],"hyperspectral",[415],[224,402],{"code":63,"score":419,"supported":40,"title":420,"abstract":421,"tags":422},7,[],[223,265],[25],{"code":175,"score":419,"supported":40,"title":424,"abstract":425,"tags":426},[],[271,272],[178],{"code":38,"score":19,"supported":40,"title":428,"abstract":429,"tags":431},[],[43,430],"fertilization",[],[433,434],"HyBEAR 裸土检测 高光谱","裸土检测 基准数据集 遥感","HyBEAR裸土检测高光谱-4345","10.5194\u002Fessd-18-7301-2026",{"doi":436,"openalex_id":438,"authors":439,"venue":395,"cited_by_count":202,"oa_url":461,"card":462,"direction":386,"ingested_from":308},"W7220738771",[440,443,446,449,452,455,458],{"name":441,"orcid":442},"Agata M. Wijata","https:\u002F\u002Forcid.org\u002F0000-0001-6180-9979",{"name":444,"orcid":445},"Bogdan Ruszczak","https:\u002F\u002Forcid.org\u002F0000-0003-1089-1778",{"name":447,"orcid":448},"Adriana Niepala","https:\u002F\u002Forcid.org\u002F0009-0005-8274-9852",{"name":450,"orcid":451},"Michał Gumiela","https:\u002F\u002Forcid.org\u002F0009-0000-2501-2786",{"name":453,"orcid":454},"Krzysztof Smykała","https:\u002F\u002Forcid.org\u002F0000-0003-1970-5388",{"name":456,"orcid":457},"Nicolas Longépé","https:\u002F\u002Forcid.org\u002F0000-0002-6832-3274",{"name":459,"orcid":460},"Jakub Nalepa","https:\u002F\u002Forcid.org\u002F0000-0002-4026-1569","https:\u002F\u002Fessd.copernicus.org\u002Farticles\u002F18\u002F7301\u002F2026\u002Fessd-18-7301-2026.pdf",{"tldr":463,"method":464,"finding":465,"direction":306,"opportunity":466},"提出HyBEAR高光谱航空影像基准数据集，用于整田块裸土检测。","2米分辨率高光谱航空影像，专家验证的田块级像素标注，含训练测试划分与基线模型。","构建了迄今最大（43225公顷）且异质的裸土检测数据集，并给出可复现的评估流程与基线结果。","可基于该基准探索田块级裸土检测的迁移学习与多光谱\u002F高光谱融合方法，并关联土壤参数反演。","2026-10-07T23:30:09.147409Z",{"id":469,"title":470,"url":471,"summary":472,"summary_zh":473,"content":8,"source_name":474,"source_url":471,"published_at":475,"category":212,"cover_url":8,"hotness":12,"is_selected":13,"score":476,"score_detail":477,"sources":479,"tags":481,"primary_topic":484,"secondary_topics":485,"classification":487,"search_phrases":504,"slug":507,"view_count":202,"doi":508,"paper":509,"created_at":520},4295,"Smart Soil Monitoring Systems: Challenges and Strategic Solutions for Enhanced Adoption","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fwjimt.v9.no12.2025.pg245.258","Smart Soil Monitoring Systems (SSMS) represent a key innovation in precision agriculture, enabling continuous monitoring of soil parameters such as moisture, pH, nutrient content, temperature, and salinity. These systems integrate Internet of Things (IoT) technologies, wireless sensor networks, and data analytics to provide real-time insights that support data-driven decision-making, optimize resource use, and enhance crop productivity. Despite their potential, the adoption of SSMS in developing institutions, including Federal Polytechnic Bali (FPB), remains limited due to financial constraints, inadequate technical skills, low awareness, and infrastructural challenges such as unreliable internet connectivity and power supply. The high cost of devices, lack of training, and insufficient institutional policies further impede practical implementation in teaching, research, and extension activities. This study employed a descriptive survey design targeting academic and technical staff in FPB’s agricultural departments. Data collected via a structured questionnaire were analyzed using descriptive statistics, with mean scores indicating key challenges and potential strategies. Findings revealed that high device costs, poor network infrastructure, and low technical capacity were the most critical barriers. To address these, respondents emphasized capacity-building workshops, improved digital infrastructure, awareness campaigns, funding support, and policy frameworks as strategic interventions. The study concludes that enhancing SSMS adoption requires integrated solutions addressing economic, technical, and institutional barriers. Implementation of these strategies is expected to strengthen hands-on learning, improve research outputs, and promote sustainable, technology-driven agriculture at FPB. The study contributes to understanding practical adoption challenges of smart agricultural technologies in resource-constrained educational settings.","智能土壤监测系统（Smart Soil Monitoring Systems, SSMS）是精准农业的一项关键创新，能够对土壤湿度、pH值、养分含量、温度和盐度等参数进行持续监测。这些系统集成了物联网（IoT）技术、无线传感器网络和数据分析，可提供实时洞察，支持数据驱动的决策、优化资源利用并提高作物生产力。尽管潜力巨大，但包括巴厘联邦理工学院（Federal Polytechnic Bali, FPB）在内的发展中机构对SSMS的采用仍然有限，原因在于资金约束、技术技能不足、认知度低以及互联网连接和电力供应不可靠等基础设施挑战。设备成本高昂、缺乏培训以及制度政策不完善，进一步阻碍了其在教学、研究和推广活动中的实际应用。本研究采用描述性调查设计，以FPB农业部门的学术和技术人员为研究对象。通过结构化问卷收集的数据采用描述性统计进行分析，以平均分表明主要挑战和潜在策略。研究发现，设备成本高、网络基础设施差和技术能力低是最关键的障碍。为应对这些问题，受访者强调能力建设研讨会、改善数字基础设施、宣传活动、资金支持和政策框架是战略性干预措施。研究得出结论，提升SSMS的采用需要综合解决方案，以应对经济、技术和制度层面的障碍。这些策略的实施有望加强实践学习、提高研究产出，并推动FPB可持续的、技术驱动的农业发展。本研究有助于理解资源受限教育环境中智能农业技术的实际采用挑战。","WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY","2026-10-05T00:00:00Z",52,{"impact":138,"substance":215,"depth":354,"authority":19,"freshness":138,"relevant":20,"comment":478},"聚焦资源受限院校智慧土壤监测落地障碍的实证调查，结论实用但样本局限于单一院校，影响力有限。",[480],{"name":474,"url":471},[482,25,324,26,483],"数字农业","农业传感器",{"code":52,"slug":239,"name":240,"path":241,"parent_code":151},[486],{"code":334,"slug":335,"name":336,"path":337,"parent_code":231},{"status":154,"locked":13,"version":33,"method":34,"evidence":488},{"candidates":489,"agricultural_evidence":40},[490,494,500],{"code":52,"score":354,"supported":40,"title":491,"abstract":492,"tags":493},[239],[55,239],[],{"code":334,"score":159,"supported":40,"title":495,"abstract":496,"tags":499},[],[324,497,344,346,498],"无线传感","wireless sensor",[483,324],{"code":63,"score":138,"supported":40,"title":501,"abstract":502,"tags":503},[],[223,265],[482,25],[505,506],"农业传感器 土壤监测 数字农业 智慧农业","农业传感器 土壤监测","农业传感器土壤监测数字农业智慧农业-4295","10.56201\u002Fwjimt.v9.no12.2025.pg245.258",{"doi":508,"openalex_id":510,"authors":511,"venue":474,"cited_by_count":202,"oa_url":514,"card":515,"direction":386,"ingested_from":308},"W7220426775",[512],{"name":513,"orcid":8},"NUHU ABDULLAHI","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FWJIMT\u002FVOL. 9 NO. 12 2025\u002FSmart Soil Monitoring Systems 245-258.pdf",{"tldr":516,"method":517,"finding":518,"direction":386,"opportunity":519},"调查尼日利亚某院校智能土壤监测系统采纳障碍并提出策略。","描述性问卷调研农业院系教职工，用描述统计与均值分析。","设备成本高、网络差、技术能力低是主要障碍，需培训与政策支持。","可研究资源受限院校中低成本物联网土壤监测方案与采纳机制。","2026-10-06T23:30:07.776821Z"]