[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1113":3,"related-1113":81},{"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":29,"secondary_topics":35,"classification":48,"search_phrases":75,"slug":78,"view_count":79,"doi":8,"paper":8,"created_at":80},1113,"江西省农业科学院土壤肥料与资源环境研究所志愿服务队赴上高开展再生稻生产技术指导","https:\u002F\u002Fwww.jxaas.com\u002F","江西省农业科学院土壤肥料与资源环境研究所志愿服务队近日赴上高县开展再生稻生产技术指导,围绕头季收获、留茬高度、肥水管理等关键技术开展实地培训,助力江西再生稻产业发展。",null,"江西省农业科学院","2026-08-27T01:00:00Z","报道",10,false,54,{"impact":16,"substance":17,"depth":18,"authority":17,"freshness":19,"relevant":20,"comment":21},15,12,8,7,1,"省级科研机构开展再生稻技术指导，对地方产业有实际推动，但内容为常规报道，深度一般。",[23],{"name":9,"url":6},[25,26,27,28],"再生稻","科技服务","江西省农科院","土肥与资环所",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[36,42],{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"code":43,"slug":44,"name":45,"path":46,"parent_code":47},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":49,"locked":13,"version":50,"method":51,"evidence":52},"classified","2026-10-22.2","rules",{"candidates":53,"agricultural_evidence":55},[54,59,65,70],{"code":30,"score":17,"supported":55,"title":56,"abstract":57,"tags":58},true,[25],[25],[25],{"code":37,"score":60,"supported":55,"title":61,"abstract":63,"tags":64},11,[62],"肥料",[62],[],{"code":43,"score":60,"supported":55,"title":66,"abstract":68,"tags":69},[67],"土壤",[67],[],{"code":71,"score":20,"supported":13,"title":72,"abstract":73,"tags":74},"G1",[],[],[26],[76,77],"土肥与资环所 江西省农科院 科技服务 再生稻","土肥与资环所 江西省农科院","土肥与资环所江西省农科院科技服务再生稻-1113",3,"2026-08-30T00:11:10.512237Z",{"total":82,"page":20,"page_size":82,"items":83},6,[84,138,222,333,403,480],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":8,"content":89,"source_name":90,"source_url":8,"published_at":91,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":92,"score_detail":93,"sources":99,"tags":101,"primary_topic":106,"secondary_topics":107,"classification":109,"search_phrases":132,"slug":135,"view_count":136,"doi":8,"paper":8,"created_at":137},5288,"赤壁水稻智慧施肥千亩示范片区亩产突破700公斤 化肥减量、耕地地力持续提升","https:\u002F\u002Fnews.cjn.cn\u002Fzjjjdpd\u002Fxn_20064\u002F202610\u002Ft5348946.htm","10月9日湖北省农科院、省农业农村厅测产专家组赴赤壁市赤壁镇九毫村,经严格实测水稻智慧施肥千亩示范片区水稻亩产突破700公斤,片区总面积1154亩;该片区集成应用智慧科学施肥方案,推广绿色肥料、机械侧深施肥、无人机追肥、秸秆还田等先进农业技术,实现粮食稳产增产与农业绿色发展双赢;赤壁市今年水稻总产量预计可达23万吨以上。","10月9日，省农科院、省农业农村厅测产专家组赴赤壁市赤壁镇九毫村，对水稻智慧施肥千亩示范片区开展现场实收测产，经严格实测，片区水稻亩产突破700公斤。\n\n本次实收测产片区总面积1154亩。专家组现场随机选定田块，依次完成水稻脱粒、装袋、称重、籽粒含水率检测等一系列规范流程。\n\n该示范片区集成应用智慧科学施肥方案，推广绿色肥料、机械侧深施肥、无人机追肥、秸秆还田等先进农业技术。技术落地后，农田化肥投入有效减少，农业面源污染得到控制，耕地地力持续提升，实现粮食稳产增产与农业绿色发展双赢，为赤壁市乃至咸宁市秋粮稳产增产打造可复制、可推广的实践样板。\n\n近年来，赤壁市深耕科学施肥推广，以新技术、新产品、新机具“三新”集成推广为抓手，对接省农业科技服务支点建设“五大提质行动”，推动良田、良种、良机、良法深度融合，加快农业关键技术落地。全市水稻常年种植面积稳定在43万亩左右，今年水稻总产量预计可达23万吨以上。（ 见博、王智雨）","长江网(2026-10-14)","2026-10-14T00:00:00Z",57,{"impact":94,"substance":95,"depth":96,"authority":82,"freshness":97,"relevant":20,"comment":98},16,18,13,4,"省级农科院测产验证的智慧施肥千亩示范实收数据，具可复制推广价值，但为地方性报道且已过一周时效。",[100],{"name":90,"url":87},[102,32,103,104,105],"绿色农业","耕地地力","化肥减量","智慧施肥",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[108],{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"status":49,"locked":13,"version":50,"method":51,"evidence":110},{"candidates":111,"agricultural_evidence":55},[112,117,121,127],{"code":37,"score":16,"supported":55,"title":113,"abstract":115,"tags":116},[114],"施肥",[114,62],[104],{"code":30,"score":17,"supported":55,"title":118,"abstract":119,"tags":120},[32],[32],[32],{"code":122,"score":79,"supported":55,"title":123,"abstract":124,"tags":126},"H5",[],[125],"秸秆还田",[],{"code":128,"score":20,"supported":13,"title":129,"abstract":130,"tags":131},"H8",[],[],[102],[133,134],"赤壁 水稻 智慧施肥","九毫村 水稻 测产","赤壁水稻智慧施肥-5288",0,"2026-10-19T00:07:41.890918Z",{"id":139,"title":140,"url":141,"summary":142,"summary_zh":143,"content":8,"source_name":144,"source_url":141,"published_at":145,"category":146,"cover_url":8,"hotness":12,"is_selected":13,"score":147,"score_detail":148,"sources":151,"tags":153,"primary_topic":159,"secondary_topics":160,"classification":168,"search_phrases":193,"slug":196,"view_count":197,"doi":198,"paper":199,"created_at":221},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","论文",62,{"impact":17,"substance":95,"depth":16,"authority":149,"freshness":18,"relevant":20,"comment":150},9,"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。",[152],{"name":144,"url":141},[154,155,156,157,158],"智慧农业","物联网","精准农业","土壤监测","水稻种植",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[161,167],{"code":162,"slug":163,"name":164,"path":165,"parent_code":166},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"code":43,"slug":44,"name":45,"path":46,"parent_code":47},{"status":49,"locked":13,"version":50,"method":51,"evidence":169},{"candidates":170,"agricultural_evidence":55},[171,177,182,187],{"code":162,"score":95,"supported":55,"title":172,"abstract":174,"tags":176},[173],"internet of things",[155,173,175],"IoT",[155],{"code":30,"score":16,"supported":55,"title":178,"abstract":180,"tags":181},[179],"paddy",[32,31],[158],{"code":43,"score":183,"supported":55,"title":184,"abstract":185,"tags":186},14,[44],[67,44],[],{"code":188,"score":18,"supported":55,"title":189,"abstract":190,"tags":192},"I1",[],[156,191],"precision agriculture",[154,156],[194,195],"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",2,"10.56581\u002Fijrerd.11.05.01-08",{"doi":198,"openalex_id":200,"authors":201,"venue":144,"cited_by_count":136,"oa_url":141,"card":214,"direction":218,"ingested_from":220},"W7220841659",[202,204,207,209,212],{"name":203,"orcid":8},"Fariha Laqania",{"name":205,"orcid":206},"Oviyanti Mulyani","https:\u002F\u002Forcid.org\u002F0000-0003-3941-8896",{"name":208,"orcid":8},"Ade Setiawan",{"name":210,"orcid":211},"Pujawati Suryatmana","https:\u002F\u002Forcid.org\u002F0000-0002-4012-8645",{"name":213,"orcid":8},"Wahyu Daradjat",{"tldr":215,"method":216,"finding":217,"direction":218,"opportunity":219},"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。","openalex","2026-10-08T23:30:13.316611Z",{"id":223,"title":224,"url":225,"summary":226,"summary_zh":227,"content":8,"source_name":228,"source_url":225,"published_at":229,"category":146,"cover_url":8,"hotness":12,"is_selected":13,"score":230,"score_detail":231,"sources":234,"tags":236,"primary_topic":242,"secondary_topics":243,"classification":245,"search_phrases":277,"slug":280,"view_count":79,"doi":281,"paper":282,"created_at":332},4292,"Agronomic performance and soil chemical properties following the application of pelletized compost and vermicompost derived from vineyard pruning residues and sewage sludge in lettuce production","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1948663","Introduction Compost and vermicompost are recognized as effective organic amendments for supporting plant growth, promoting nutrient recycling and enhancing soil quality within circular economy strategies. In this context, the present study evaluated the short-term performance of pelletized compost (CC) and vermicompost (VC) produced from vineyard prunings and sewage sludge on lettuce ( Lactuca sativa L.) growth, leaf biochemical traits, mineral composition, and soil chemical properties. The performance of both organic products was compared with a commercial organomineral fertilizer (CF) and an unfertilized control (CT). Methods A completely randomized greenhouse pot experiment was conducted for 55 days comprising four treatments with eight replicates each. . Data were analyzed by one-way ANOVA followed by Tukey´s HSD test at p \u003C 0.05, with non-parametric analysis used when ANOVA assumptions were not met. Results and discussion At the end of the experiment, organic amendments performed comparably to the commercial fertilizer CF for most morphological parameters, despite differences in absolute nutrient inputs among treatments, although shoot fresh weight remained higher under CF. Leaf biochemical responses varied among treatments: CC and CF exhibited high chlorophyll and carotenoid contents, whereas VC and CT showed lower pigment concentrations. Total soluble sugars were highest in CT and lowest in CF, while total protein and phenolic contents were not significantly affected by treatment. Leaf macronutrient concentrations under CC and VC were generally comparable to CF, while for micronutrients Fe and Mn concentrations were higher in CF. Final soil analyses showed higher organic matter, extractable P and K, and cation exchange capacity under organic fertilization, whereas CF showed the lowest soil pH. Trace metal concentration remained low in all treatments. Conclusions These findings support the potential use of compost and vermicompost derived from vineyard pruning residues and sewage sludge as recycled organic amendments for horticultural production. However, further research is recommended to validate these results under long-term field conditions and diverse soil types.","引言 堆肥和蚯蚓粪被认为是有效的有机改良剂，可在循环经济策略中支持植物生长、促进养分循环并提升土壤质量。在此背景下，本研究评估了由葡萄园修剪枝条和污水污泥制成的颗粒化堆肥（CC）和蚯蚓粪（VC）对生菜（Lactuca sativa L.）生长、叶片生化性状、矿质组成及土壤化学性质的短期效果。并将这两种有机产品与一种商品化有机无机复混肥（CF）及未施肥对照（CT）进行了比较。方法 采用完全随机温室盆栽试验，持续55天，包括4个处理，每处理8次重复。数据采用单因素方差分析，随后进行Tukey HSD检验，显著性水平为p \u003C 0.05；当不满足方差分析假设时，采用非参数分析。结果与讨论 试验结束时，尽管各处理间绝对养分投入量存在差异，但有机改良剂在大多数形态指标上与商品化肥料CF表现相当，不过CF下地上部鲜重仍较高。叶片生化响应因处理而异：CC和CF表现出较高的叶绿素和类胡萝卜素含量，而VC和CT的色素浓度较低。可溶性总糖在CT中最高，在CF中最低，而总蛋白和酚类物质含量未受处理的显著影响。CC和VC下叶片大量元素浓度总体与CF相当，而微量元素Fe和Mn浓度在CF中较高。最终土壤分析显示，有机施肥下有机质、可提取P和K以及阳离子交换量较高，而CF下土壤pH最低。所有处理中痕量金属浓度均保持较低水平。结论 这些发现支持将源自葡萄园修剪残体和污水污泥的堆肥和蚯蚓粪作为再生有机改良剂用于园艺生产的潜力。然而，建议进一步研究以在长期田间条件和多种土壤类型下验证这些结果。","Frontiers in Sustainable Food Systems","2026-10-05T00:00:00Z",66,{"impact":18,"substance":232,"depth":94,"authority":96,"freshness":149,"relevant":20,"comment":233},20,"温室盆栽短期试验，验证葡萄修剪废弃物与污泥制成颗粒堆肥和蚯蚓粪的农艺效果，结论有参考价值但需长期田间验证。",[235],{"name":228,"url":225},[237,238,239,240,241],"生菜","土壤改良","有机肥","蚯蚓粪","堆肥",{"code":43,"slug":44,"name":45,"path":46,"parent_code":47},[244],{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"status":49,"locked":13,"version":50,"method":51,"evidence":246},{"candidates":247,"agricultural_evidence":55},[248,252,258,264,271],{"code":43,"score":16,"supported":55,"title":249,"abstract":250,"tags":251},[44],[67,44],[238],{"code":37,"score":12,"supported":55,"title":253,"abstract":254,"tags":257},[],[114,62,255,256],"fertilizer","fertilization",[239],{"code":259,"score":19,"supported":55,"title":260,"abstract":261,"tags":263},"B1",[],[237,262],"萝卜",[237],{"code":265,"score":82,"supported":55,"title":266,"abstract":267,"tags":270},"B7",[],[268,269],"温室","greenhouse",[],{"code":272,"score":79,"supported":55,"title":273,"abstract":274,"tags":276},"B2",[],[275],"葡萄",[],[278,279],"葡萄修剪废弃物 污泥 堆肥 生菜","土壤改良 有机肥 蚯蚓粪 堆肥","葡萄修剪废弃物污泥堆肥生菜-4292","10.3389\u002Ffsufs.2026.1948663",{"doi":281,"openalex_id":283,"authors":284,"venue":228,"cited_by_count":136,"oa_url":225,"card":326,"direction":330,"ingested_from":220},"W7220437154",[285,288,291,294,297,299,302,305,308,311,314,317,320,323],{"name":286,"orcid":287},"Maria Cristina Morais","https:\u002F\u002Forcid.org\u002F0000-0002-6598-7454",{"name":289,"orcid":290},"Elisabete Nascimento-Gonçalves","https:\u002F\u002Forcid.org\u002F0000-0001-6430-1513",{"name":292,"orcid":293},"Lia‐Tânia Dinis","https:\u002F\u002Forcid.org\u002F0000-0002-9612-0909",{"name":295,"orcid":296},"Sandra Pereira","https:\u002F\u002Forcid.org\u002F0000-0003-2998-814X",{"name":298,"orcid":8},"Zélia Branco",{"name":300,"orcid":301},"Henda Lopes","https:\u002F\u002Forcid.org\u002F0000-0002-2830-1783",{"name":303,"orcid":304},"Tiago Azevedo","https:\u002F\u002Forcid.org\u002F0000-0002-3030-4510",{"name":306,"orcid":307},"Catarina Medeiros","https:\u002F\u002Forcid.org\u002F0009-0005-2706-7023",{"name":309,"orcid":310},"Ana Maria Coimbra","https:\u002F\u002Forcid.org\u002F0000-0003-3110-7153",{"name":312,"orcid":313},"João Claro","https:\u002F\u002Forcid.org\u002F0000-0001-5936-1036",{"name":315,"orcid":316},"Guilhermina Marques","https:\u002F\u002Forcid.org\u002F0000-0003-0963-5785",{"name":318,"orcid":319},"João Ricardo Sousa","https:\u002F\u002Forcid.org\u002F0000-0003-4365-9086",{"name":321,"orcid":322},"Paula A. Oliveira","https:\u002F\u002Forcid.org\u002F0000-0001-9519-4044",{"name":324,"orcid":325},"Marta Roboredo","https:\u002F\u002Forcid.org\u002F0000-0002-6177-4988",{"tldr":327,"method":328,"finding":329,"direction":330,"opportunity":331},"评估葡萄修剪物与污泥制成的颗粒堆肥和蚯蚓粪对生菜生长及土壤化学性质的影响。","温室盆栽试验，4处理8重复，55天，方差分析与Tukey检验。","两种有机肥多数形态指标媲美商品肥，且提升土壤有机质、磷钾和阳离子交换量。","农业绿色发展与碳","可开展长期田间试验，验证不同土壤类型下有机肥的稳定效果与重金属累积风险。","2026-10-06T23:30:06.098411Z",{"id":334,"title":335,"url":336,"summary":337,"summary_zh":338,"content":8,"source_name":339,"source_url":336,"published_at":340,"category":146,"cover_url":8,"hotness":12,"is_selected":13,"score":341,"score_detail":342,"sources":345,"tags":347,"primary_topic":351,"secondary_topics":352,"classification":354,"search_phrases":383,"slug":386,"view_count":197,"doi":387,"paper":388,"created_at":402},3920,"Automated Leaf Color Chart (LCC)-BasedNitrogen Fertilizer Recommendation UsingPaddy Leaf Image Color Classification","https:\u002F\u002Fdoi.org\u002F10.70818\u002Fijbass.2026.v07i02.0075","The demand for accurate and timely assessment of crop health is high in precision agriculture to support proper nutrient management. In this work, we present an intelligent deep learning-based solution that evaluates the colour of paddy rice leaves to provide real-time, context-sensitive recommendations regarding the addition of nitrogen fertilizers to grow rice sustainably. For optimum nitrogen management and sustainability in the production of rice crops, an accurate assessment of the color of paddy leaves is required. Herein, we present a deep learning-based framework for paddy leaf color classification with the capability of recommending an appropriate amount of nitrogen fertilizer as needed in real-time. In this research, the Paddy Net dataset containing 12,000 augmented rice leaf images will be used for training the neural network used for classifying the rice leaf's color based on the Leaf Color Chart (LCC) system, which consists of LCC levels from 2 to 5. Additionally, several deep learning architectures were trainedand tested, specifically Custom CNNs, VGG16, ResNet50 and EfficientNetB7, to identify which architecture provided the best classification performance of rice leaf color. In terms of the experimental results, it can be concluded that the Custom CNN with a classification accuracy of 97.55% significantly outperforms the transfer learning models, VGG16 (78.89%) and EfficientNetB7 (78.67%). To also improve practicality, multimodal vision-language model LLa MA 3.2-11B Vision is integrated into the system, allowing it to provide real-time, context-based nitrogen fertilizer recommendations based on visual input images. Additionally, the proposed framework takes into account other agronomic factors such as the growth stage of the crop, type of soil, and fertilizer application history. This allows for more accurate and relevant recommendations. The proposed framework allows for precise and objective nitrogen management without the subjective use of visual assessment methods typically used by farmers. Overall, the proposed system for fertilizer application is significant for the development of precision agriculture because it increases the efficiency of the decision-making process, optimizes the use of fertilizer, and increases the practice of sustainable rice cultivation.","在精准农业中，对作物健康状况进行准确且及时评估的需求很高，以支持合理的养分管理。在这项工作中，我们提出了一种基于深度学习的智能解决方案，通过评估水稻叶片的颜色，实时提供与情境相关的氮肥添加建议，以实现水稻的可持续种植。为了实现水稻作物生产中的优化氮管理和可持续性，需要对水稻叶片颜色进行准确评估。在此，我们提出了一个基于深度学习的稻田叶片颜色分类框架，能够根据实时需求推荐适量的氮肥。在本研究中，包含12,000张增强水稻叶片图像的Paddy Net数据集将用于训练神经网络，该网络基于叶色卡（Leaf Color Chart, LCC）系统对水稻叶片颜色进行分类，LCC等级从2到5。此外，我们训练并测试了多种深度学习架构，特别是自定义CNN、VGG16、ResNet50和EfficientNetB7，以确定哪种架构在水稻叶片颜色分类中表现最佳。从实验结果可以得出结论，自定义CNN以97.55%的分类准确率显著优于迁移学习模型VGG16（78.89%）和EfficientNetB7（78.67%）。为提高实用性，系统还集成了多模态视觉语言模型LLaMA 3.2-11B Vision，使其能够基于视觉输入图像提供实时、基于情境的氮肥推荐。此外，所提出的框架还考虑了其他农艺因素，如作物生长阶段、土壤类型和施肥历史，从而提供更准确和相关的建议。该框架实现了精确、客观的氮管理，避免了农民通常采用的主观视觉评估方法。总体而言，所提出的施肥系统对精准农业的发展具有重要意义，因为它提高了决策过程的效率，优化了肥料使用，并促进了可持续水稻种植实践。","International Journal of Business Arts and Scientific Study","2026-09-30T00:00:00Z",63,{"impact":17,"substance":232,"depth":343,"authority":82,"freshness":18,"relevant":20,"comment":344},17,"用深度学习与视觉语言模型实现水稻叶色分级与氮肥实时推荐，方法新颖、数据规模可观，但期刊影响力有限，属细分领域技术进展。",[346],{"name":339,"url":336},[154,348,349,32,350],"农业人工智能","精准施肥","氮肥管理",{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},[353],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":49,"locked":13,"version":50,"method":51,"evidence":355},{"candidates":356,"agricultural_evidence":55},[357,363,368,372,379],{"code":37,"score":358,"supported":55,"title":359,"abstract":360,"tags":362},19,[255],[114,62,361,255],"养分管理",[350,349],{"code":30,"score":12,"supported":55,"title":364,"abstract":365,"tags":367},[],[32,366,31,179],"稻田",[32],{"code":188,"score":19,"supported":55,"title":369,"abstract":370,"tags":371},[],[156,191],[154],{"code":373,"score":19,"supported":55,"title":374,"abstract":375,"tags":378},"I2",[],[376,377],"深度学习","deep learning",[348],{"code":43,"score":82,"supported":55,"title":380,"abstract":381,"tags":382},[],[67,44],[],[384,385],"水稻 叶片颜色 氮肥推荐","Paddy Net 深度学习 叶色卡","水稻叶片颜色氮肥推荐-3920","10.70818\u002Fijbass.2026.v07i02.0075",{"doi":387,"openalex_id":389,"authors":390,"venue":339,"cited_by_count":136,"oa_url":336,"card":396,"direction":400,"ingested_from":220},"W7214974109",[391,393],{"name":392,"orcid":8},"Rusanara Akhter",{"name":394,"orcid":395},"Md Zahidul Islam","https:\u002F\u002Forcid.org\u002F0000-0002-4868-4945",{"tldr":397,"method":398,"finding":399,"direction":400,"opportunity":401},"用深度学习对水稻叶片颜色分级，并结合多模态大模型实时推荐氮肥用量。","Paddy Net 1.2万张增强叶片图像，训练Custom CNN、VGG16","Custom CNN分类准确率达97.55%，显著优于VGG16（78.89%）和Efficient","农业人工智能与决策模型","可探索轻量化模型部署到手机端，并结合多源农情数据提升跨品种、跨区域推荐泛化能力。","2026-10-01T23:30:59.367113Z",{"id":404,"title":405,"url":406,"summary":407,"summary_zh":408,"content":8,"source_name":409,"source_url":406,"published_at":410,"category":146,"cover_url":8,"hotness":12,"is_selected":13,"score":411,"score_detail":412,"sources":414,"tags":416,"primary_topic":422,"secondary_topics":423,"classification":430,"search_phrases":451,"slug":454,"view_count":197,"doi":455,"paper":456,"created_at":479},3792,"Estimation of soil organic carbon under rice-fallow system using Sentinel-2 derivatives","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.11182","The evaluation of soil properties under a century-old traditional rice-fallow practice in highly fragile topography (i.e., hill slopes and lowlands) of North-east (NE) India in the Eastern Himalaya faces multiple challenges in conventional studies of soil properties. Alternatively, remote sensing offers a viable solution. An attempt was made to study the soil organic carbon (SOC) under century old traditional rice-fallow practice in highly fragile topography (i.e., hill slopes and lowlands) of Bhoirymbong, Meghalaya, NE India in Eastern Himalaya using remote sensing indices derived from Sentinel-2. The objective was to develop a suitable regression model for rice-fallow systems across different hill slopes in northeastern India in the Eastern Himalaya. A total of 100 composite soil samples from the 0–15 cm depth were collected from rice-fallow areas on various slopes (nearly level: 0–3 %, gentle: 3–8 %, moderate: 8–15 %, steep: 15–30 % and very steep: > 30 %) during November 2020. Each composite sample was prepared from 10 randomly collected soil samples within a 10 × 10 m area and analysed for SOC using standard protocols in the analytical laboratory of School of Natural Resource Management, College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University, Imphal. Nearly synchronised, cloud-free (\u003C 10 % cloud cover) Sentinel-2 data consistent with soil sampling period were accessed from the US Geological Survey (USGS) official website to derive 15 indices. Results showed that brightness index (BI), BI2 and the soil bareness index (SBI) were the most influential indices for SOC. The highest and lowest SOC values were found in gently undulating topography (2–5 % slope) and rolling topography (10–15 % slope), at 2.86 % and 1.17 %, respectively. After rice crop harvesting, the red-NIR-based indices did not show a significant difference, whereas the green-NIR-based indices were significantly different at p \u003C 0.05. Hence, it is concluded that green-NIR-based indices are the most suitable for SOC prediction in NE India in the Eastern Himalaya.","在印度东北部东喜马拉雅地区高度脆弱的地形（即山坡和低地）下，对百年传统水稻休耕实践下的土壤性质进行评估，在常规土壤性质研究中面临多重挑战。遥感则提供了一种可行的替代方案。本研究尝试利用Sentinel-2衍生的遥感指数，研究印度东北部东喜马拉雅地区梅加拉亚邦Bhoirymbong在高度脆弱地形（即山坡和低地）下百年传统水稻休耕实践中的土壤有机碳（SOC）。目标是建立一个适用于东喜马拉雅地区印度东北部不同山坡水稻休耕系统的合适回归模型。2020年11月，在不同坡度（近水平：0–3%、缓坡：3–8%、中坡：8–15%、陡坡：15–30%和极陡坡：>30%）的水稻休耕区采集了100个0–15 cm深度的复合土壤样品。每个复合样品由10 × 10 m区域内随机采集的10个土壤样品制备而成，并在Central Agricultural University, Imphal农业科学研究生院自然资源管理学院分析实验室采用标准方法分析SOC。从美国地质调查局（USGS）官方网站获取与土壤采样期基本同步、无云（云量\u003C10%）的Sentinel-2数据，以衍生15个指数。结果表明，亮度指数（BI）、BI2和土壤裸露指数（SBI）是对SOC影响最大的指数。SOC最高值和最低值分别出现在缓起伏地形（2–5%坡度）和波状地形（10–15%坡度），分别为2.86%和1.17%。水稻收获后，基于红-近红外的指数未显示出显著差异，而基于绿-近红外的指数在p \u003C 0.05水平上差异显著。因此，得出结论：基于绿-近红外的指数最适合东喜马拉雅地区印度东北部的SOC预测。","Plant Science Today","2026-09-28T00:00:00Z",69,{"impact":17,"substance":232,"depth":94,"authority":96,"freshness":18,"relevant":20,"comment":413},"基于Sentinel-2遥感指数估算稻作休耕系统土壤有机碳，方法有新意但属区域案例研究，对国内智慧农业遥感应用有参考价值。",[415],{"name":409,"url":406},[417,418,419,420,421],"数字农业","Sentinel-2","土壤有机碳","遥感监测","水稻休耕",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[424,425],{"code":43,"slug":44,"name":45,"path":46,"parent_code":47},{"code":426,"slug":427,"name":428,"path":429,"parent_code":166},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":49,"locked":13,"version":50,"method":51,"evidence":431},{"candidates":432,"agricultural_evidence":55},[433,437,441,447],{"code":43,"score":16,"supported":55,"title":434,"abstract":435,"tags":436},[44],[67,44],[419],{"code":30,"score":183,"supported":55,"title":438,"abstract":439,"tags":440},[31],[32,31],[],{"code":426,"score":18,"supported":55,"title":442,"abstract":443,"tags":446},[],[444,445],"遥感","remote sensing",[418,420],{"code":188,"score":20,"supported":13,"title":448,"abstract":449,"tags":450},[],[],[417],[452,453],"印度东北部 水稻休耕 土壤有机碳","Sentinel-2 遥感 土壤有机碳","印度东北部水稻休耕土壤有机碳-3792","10.14719\u002Fpst.11182",{"doi":455,"openalex_id":457,"authors":458,"venue":409,"cited_by_count":136,"oa_url":406,"card":473,"direction":477,"ingested_from":220},"W7214617000",[459,462,464,467,470],{"name":460,"orcid":461},"Janaki Singh Naorem","https:\u002F\u002Forcid.org\u002F0000-0001-9534-0339",{"name":463,"orcid":8},"D Priya",{"name":465,"orcid":466},"Deepak Kumar Dwivedi","https:\u002F\u002Forcid.org\u002F0009-0003-5408-887X",{"name":468,"orcid":469},"J S Rajkumar","https:\u002F\u002Forcid.org\u002F0009-0003-7863-277X",{"name":471,"orcid":472},"Tamang Basant","https:\u002F\u002Forcid.org\u002F0000-0001-6856-7298",{"tldr":474,"method":475,"finding":476,"direction":477,"opportunity":478},"用Sentinel-2指数估算印度东北部稻作休耕系统不同坡度的土壤有机碳。","采集100个0-15cm土样，结合Sentinel-2的15个遥感指数建立回归模","绿光-近红外指数最适合预测SOC，缓坡SOC最高（2.86%），陡坡最低（1.17%）。","农业遥感与作物表型","可探索多时相Sentinel-2与地形因子耦合，提升复杂地形区SOC制图精度与普适性。","2026-09-29T23:30:28.690009Z",{"id":481,"title":482,"url":483,"summary":484,"summary_zh":485,"content":8,"source_name":486,"source_url":483,"published_at":487,"category":146,"cover_url":8,"hotness":12,"is_selected":13,"score":488,"score_detail":489,"sources":491,"tags":493,"primary_topic":496,"secondary_topics":497,"classification":499,"search_phrases":525,"slug":528,"view_count":197,"doi":529,"paper":530,"created_at":542},3680,"AI-Driven Nutrient Optimization Systems for Precision Agriculture: A Data-Integrated Approach to Reducing Nutrient Runoff and Enhancing Soil Health in Agricultural Systems","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.85286","Nutrients play a fundamental role in plant growth, soil fertility, and overall agricultural productivity. However, inefficient fertilizer application in conventional farming systems often leads to nutrient imbalance, reduced soil quality, and increased environmental degradation. The emergence of precision agriculture and artificial intelligence (AI) has introduced new opportunities for improving nutrient management through data-driven decision-making. This study investigates an AI-driven nutrient optimization system designed to enhance fertilizer efficiency while reducing nutrient runoff and improving soil health. Findings indicate that AI-based systems significantly improve nutrient use efficiency by enabling site-specific fertilizer recommendations and adaptive management strategies. The integration of multi-source agricultural data enhances prediction accuracy for soil nutrient status and crop demand, while also supporting early identification of nutrient loss risks such as runoff and leaching. The study concludes that AI-driven nutrient optimization provides a sustainable and scalable approach for modern agriculture, supporting both productivity enhancement and environmental protection through intelligent, data-integrated farming systems.","养分在植物生长、土壤肥力和农业整体生产力中发挥着基础性作用。然而，传统农业系统中低效的施肥方式往往导致养分失衡、土壤质量下降以及环境退化加剧。精准农业与人工智能（AI）的出现，为通过数据驱动决策改善养分管理带来了新的机遇。本研究探讨了一种AI驱动的养分优化系统，旨在提高肥料利用效率，同时减少养分径流并改善土壤健康。研究结果表明，基于AI的系统通过实现特定地块的施肥建议和适应性管理策略，显著提高了养分利用效率。多源农业数据的整合提升了土壤养分状况和作物需求的预测精度，同时也有助于早期识别径流和淋溶等养分流失风险。研究得出结论，AI驱动的养分优化为现代农业提供了一种可持续且可扩展的方法，通过智能化、数据整合的农业系统，同时支持生产力提升和环境保护。","International Journal For Multidisciplinary Research","2026-09-26T00:00:00Z",61,{"impact":17,"substance":95,"depth":16,"authority":18,"freshness":18,"relevant":20,"comment":490},"论文提出AI驱动的养分优化系统，方法有新意但属学术探讨，产业落地影响有限，可作为智慧农业技术动态收录。",[492],{"name":486,"url":483},[154,348,349,494,495],"土壤健康","养分流失",{"code":43,"slug":44,"name":45,"path":46,"parent_code":47},[498],{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"status":49,"locked":13,"version":50,"method":51,"evidence":500},{"candidates":501,"agricultural_evidence":55},[502,506,510,514,519],{"code":43,"score":16,"supported":55,"title":503,"abstract":504,"tags":505},[44],[67,44],[494],{"code":188,"score":16,"supported":55,"title":507,"abstract":508,"tags":509},[191],[156,191],[154],{"code":37,"score":12,"supported":55,"title":511,"abstract":512,"tags":513},[],[114,62,361,255],[349],{"code":373,"score":97,"supported":55,"title":515,"abstract":516,"tags":518},[],[517],"artificial intelligence",[348],{"code":520,"score":79,"supported":55,"title":521,"abstract":522,"tags":524},"I6",[],[523],"agricultural data",[],[526,527],"AI 养分优化 精准农业","养分流失 土壤健康","AI养分优化精准农业-3680","10.36948\u002Fijfmr.2026.v08i05.85286",{"doi":529,"openalex_id":531,"authors":532,"venue":486,"cited_by_count":136,"oa_url":483,"card":537,"direction":400,"ingested_from":220},"W7214553198",[533,535],{"name":534,"orcid":8},"Olufisayo Obebe",{"name":536,"orcid":8},"Ebenezer Akinola",{"tldr":538,"method":539,"finding":540,"direction":400,"opportunity":541},"研究AI驱动的养分优化系统，通过数据集成实现精准施肥，减少养分流失并改善土壤健康。","集成多源农业数据与AI算法，进行站点特异性施肥推荐和自适应管理。","AI系统显著提高养分利用效率，增强土壤养分与作物需求预测精度，并早期识别流失风险。","可探索多源数据融合的实时决策模型，以及在不同土壤气候条件下AI系统的可扩展性与长期环境效应评估。","2026-09-28T23:30:49.535963Z"]