[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1981":3,"related-1981":108},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":73,"slug":76,"view_count":21,"doi":77,"paper":78,"created_at":107},1981,"Hydrochemistry and modeling nitrate concentration in farmland groundwater under different hydrological seasons by integrating hybrid quantum-classical ML, virtual sample generation and AlphaEarth Foundation","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fhess-30-5647-2026","Abstract. Precise seasonal prediction of groundwater nitrate concentrations in intensive agricultural areas faces challenges such as data sparsity, strong spatiotemporal heterogeneity, and complex hydro-biogeochemical processes. To address these issues, this study proposes an integrated prediction framework combining hybrid quantum-classical machine learning, advanced virtual sample generation (t-SNE-GMM-KNN), and remote sensing foundation model semantic embedding (AEF). Modeling was conducted across the 2022–2023 normal, dry, and wet seasons in Xiong'an New Area. Hydrochemical types were dominated by Ca-Mg-HCO3−, controlled by mineral dissolution and evaporation. Nitrate concentrations were highest in the dry season (mean 42.93 mg L−1), driven by evaporative concentration. Spatially, high-value zones shifted: southeast (normal), central (dry), and northwest (wet). MixSIAR modeling based on isotopes indicated domestic sewage and livestock manure (74.1 %) as dominant sources. The t-SNE-GMM-KNN strategy mitigated small-sample bias while preserving nonlinear structure. When virtual samples were augmented to 10-fold, the Random Forest R2 in the dry season increased from 0.284 to > 0.85. Furthermore, a hybrid quantum-classical Random Forest exhibited superior robustness for data sparsity, achieving peak performance in the normal season (R2 = 0.962, RMSE = 5.73 mg L−1). Additionally, using only AEF embeddings achieved screening-level accuracy (R2 up to 0.860), providing a feasible rapid survey scheme for extensive unmonitored regions. Correlation analysis identified TDS and EC as persistent top predictors (r > 0.8). This comprehensive framework offers a robust solution for seasonal nitrate prediction and sustainable water management.","摘要：集约化农业区地下水硝酸盐浓度的精准季节预测面临数据稀疏、时空异质性强及水文生物地球化学过程复杂等挑战。针对上述问题，本研究提出了一种集成预测框架，融合了混合量子-经典机器学习、先进虚拟样本生成技术（t-SNE-GMM-KNN）及遥感基础模型语义嵌入（AEF）。研究在雄安新区2022–2023年平水期、枯水期和丰水期开展了建模分析。水化学类型以Ca-Mg-HCO₃⁻为主，受矿物溶解和蒸发作用控制。硝酸盐浓度在枯水期最高（均值42.93 mg L⁻¹），主要受蒸发浓缩作用驱动。空间上，高值区呈迁移特征：平水期位于东南部，枯水期移至中部，丰水期则分布于西北部。基于同位素的MixSIAR模型表明，生活污水和畜禽粪便（74.1%）为主要污染源。t-SNE-GMM-KNN策略在保持非线性结构的同时缓解了小样本偏差；当虚拟样本扩充至10倍时，枯水期随机森林模型的R²从0.284提升至0.85以上。此外，混合量子-经典随机森林模型在数据稀疏条件下表现出优异的鲁棒性，在平水期达到最佳性能（R² = 0.962，RMSE = 5.73 mg L⁻¹）。仅使用AEF嵌入特征即可达到筛选级精度（R²最高达0.860），为广域无监测区域提供了可行的快速调查方案。相关性分析识别出TDS和EC为持续主导预测因子（r > 0.8）。该综合框架为季节性硝酸盐预测和可持续水资源管理提供了稳健的解决方案。",null,"Hydrology and earth system sciences","2026-09-08T00:00:00Z","论文",10,false,73,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,2,1,"研究提出结合量子机器学习与遥感基础模型的农田地下水硝酸盐预测框架，方法新颖，数据详实，对农业面源污染治理有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业人工智能","机器学习","遥感","地下水","硝酸盐",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,58,66],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},8,true,[],[27,49],"machine learning",[26,27],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":57},"I3",7,[],[28,56],"remote sensing",[28],{"code":59,"score":60,"supported":46,"title":61,"abstract":62,"tags":65},"C",6,[],[63,64],"畜禽","livestock",[],{"code":67,"score":68,"supported":46,"title":69,"abstract":70,"tags":72},"H3",4,[],[71],"水资源",[29],[74,75],"农业人工智能 机器学习 地下水 硝酸盐","农业人工智能 机器学习","农业人工智能机器学习地下水硝酸盐-1981","10.5194\u002Fhess-30-5647-2026",{"doi":77,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":98,"oa_url":6,"card":99,"direction":105,"ingested_from":106},"W7126035353",[81,83,86,88,91,93,95],{"name":82,"orcid":9},"Junjie Xu",{"name":84,"orcid":85},"Xin Wei","https:\u002F\u002Forcid.org\u002F0000-0002-5207-6141",{"name":87,"orcid":9},"Yilei Yu",{"name":89,"orcid":90},"Lihu Yang","https:\u002F\u002Forcid.org\u002F0000-0002-4580-4972",{"name":92,"orcid":9},"Yuanzheng Zhai",{"name":94,"orcid":9},"Cuicui Lv",{"name":96,"orcid":97},"Xinzhe Song","https:\u002F\u002Forcid.org\u002F0000-0002-6638-7805",0,{"tldr":100,"method":101,"finding":102,"direction":103,"opportunity":104},"提出集成混合量子-经典机器学习、虚拟样本生成和遥感基础模型的框架，预测雄安农田地下水硝酸盐浓度。","混合量子-经典随机森林、t-SNE-GMM-KNN虚拟样本、AlphaEarth","干季硝酸盐最高（42.93 mg\u002FL），虚拟样本扩增10倍使R2从0.284升至>0.85，混合模型","农业人工智能与决策模型","可探索将虚拟样本生成与基础模型嵌入结合，应用于其他数据稀疏的农业环境监测，如土壤养分或农药残留预测。","智慧农业 \u002F 农业物联网","openalex","2026-09-09T23:30:08.416152Z",{"total":60,"page":21,"page_size":60,"items":109},[110,195,253,318,410,459],{"id":111,"title":112,"url":113,"summary":114,"summary_zh":115,"content":9,"source_name":116,"source_url":113,"published_at":117,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":118,"score_detail":119,"sources":123,"tags":125,"primary_topic":126,"secondary_topics":131,"classification":133,"search_phrases":177,"slug":179,"view_count":21,"doi":180,"paper":181,"created_at":194},1643,"Random Forest-Based Groundwater Potential Zone Mapping in Latur District, Maharashtra, India","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84777","Groundwater is a major source of water for agriculture, domestic use, and rural development in semi-arid regions of India. In Latur District, Maharashtra, groundwater occurrence is spatially variable because the Deccan basalt terrain has low primary porosity and depends mainly on weathered material, fractures, joints, vesicular zones, lineaments, and favourable surface conditions. This research applies Remote Sensing, Geographic Information System (GIS), and Random Forest (RF) machine learning to delineate groundwater potential zones (GWPZs) in Latur District. Seven spatial conditioning parameters— lithology, geomorphology, soil, slope, drainage density, lineament density, and land use\u002Fland cover (LULC)—were integrated with groundwater-well observations. A total of 109 wells were classified using a 12.8 m mean-depth threshold. Seventy percent of the observations were used for RF training and 30% were retained for independent validation. All input maps were standardized to WGS 84\u002FUTM Zone 43N and a 30 m raster grid. The RF probability output was classified into very-low, low, moderate, high, and very-high groundwater-potential zones. The model produced an ROC-AUC value of 0.8192, showing good discriminative capability between groundwater occurrence and non-occurrence conditions. The final GWPZ map provides a district-scale planning tool for groundwater exploration, recharge planning, conservation measures, and future field investigation.","地下水是印度半干旱地区农业、生活用水及农村发展的主要水源。在马哈拉施特拉邦拉图尔县，由于德干玄武岩地形原生孔隙度低，地下水赋存主要依赖于风化层、裂隙、节理、气孔带、线性构造及有利的地表条件，因此其空间分布具有显著差异性。本研究运用遥感、地理信息系统（GIS）及随机森林（RF）机器学习方法，对拉图尔县地下水潜力区（GWPZs）进行圈定。研究整合了七个空间条件参数——岩性、地貌、土壤、坡度、排水密度、线性构造密度及土地利用\u002F土地覆盖（LULC），并与地下水井观测数据相结合。以12.8米平均水深为阈值，对总计109口井进行了分类。其中70%的观测数据用于随机森林模型训练，其余30%保留用于独立验证。所有输入图件均统一至WGS 84\u002FUTM 43N带坐标系，并采用30米栅格分辨率。随机森林模型输出的概率结果被划分为极低、低、中、高和极高五个地下水潜力等级。模型ROC-AUC值为0.8192，显示出对地下水赋存与非赋存条件的良好判别能力。最终生成的地下水潜力区图件为区域尺度的地下水勘探、补给规划、保护措施及未来野外调查提供了规划工具。","International Journal for Research in Applied Science and Engineering Technology","2026-09-03T00:00:00Z",49,{"impact":45,"substance":120,"depth":121,"authority":45,"freshness":20,"relevant":21,"comment":122},15,16,"利用随机森林与遥感GIS绘制地下水潜力图，方法可靠，但区域局限，时效性低。",[124],{"name":116,"url":113},[26,27,28,29],{"code":127,"slug":128,"name":129,"path":130,"parent_code":36},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[132],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":134},{"candidates":135,"agricultural_evidence":46},[136,144,148,152,159,166,173],{"code":127,"score":137,"supported":46,"title":138,"abstract":139,"tags":143},9,[],[140,141,142],"地理信息","GIS","geographic information",[],{"code":32,"score":45,"supported":46,"title":145,"abstract":146,"tags":147},[],[27,49],[26,27],{"code":52,"score":53,"supported":46,"title":149,"abstract":150,"tags":151},[],[28,56],[28],{"code":153,"score":60,"supported":46,"title":154,"abstract":155,"tags":158},"G",[],[156,157],"农村发展","rural development",[],{"code":160,"score":60,"supported":46,"title":161,"abstract":162,"tags":165},"H2",[],[163,164],"土壤","soil",[],{"code":167,"score":168,"supported":46,"title":169,"abstract":170,"tags":172},"H7",3,[],[171],"干旱",[],{"code":67,"score":21,"supported":14,"title":174,"abstract":175,"tags":176},[],[],[29],[178,75],"农业人工智能 机器学习 地下水 遥感","农业人工智能机器学习地下水遥感-1643","10.22214\u002Fijraset.2026.84777",{"doi":180,"openalex_id":182,"authors":183,"venue":116,"cited_by_count":98,"oa_url":113,"card":188,"direction":192,"ingested_from":106},"W7207838377",[184,186],{"name":185,"orcid":9},"Suchivrutta R. Kamble",{"name":187,"orcid":9},"Ganesh K. Patil",{"tldr":189,"method":190,"finding":191,"direction":192,"opportunity":193},"利用随机森林结合遥感与GIS绘制印度Latur地区地下水潜力图。","随机森林模型，集成7个空间参数与109口井数据。","模型ROC-AUC为0.8192，能有效区分地下水潜力区。","农业遥感与作物表型","可扩展至其他干旱区，结合实时水文数据与深度学习提升精度。","2026-09-04T23:30:32.476791Z",{"id":196,"title":197,"url":198,"summary":199,"summary_zh":9,"content":9,"source_name":200,"source_url":9,"published_at":201,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":202,"score_detail":203,"sources":206,"tags":208,"primary_topic":211,"secondary_topics":212,"classification":217,"search_phrases":240,"slug":243,"view_count":68,"doi":9,"paper":244,"created_at":252},4286,"Applications of Artificial Intelligence in Soil Characterization and Agriculture: A Systematic Review","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37386408","Navarro Rubio等系统综述2018-2026年196项AI土壤特性与农业应用研究，遵循PRISMA 2020框架。综述发现研究明显向数据驱动方法转变，机器学习和深度学习模型主导近期研究；随机森林、支持向量机、梯度提升方法、人工神经网络、卷积神经网络、长短期记忆架构最常被报告；主要数据源包括原位传感、实验室测量、遥感影像和环境协变量，常通过多源数据融合框架集成；树集成模型在不同土壤特性上提供稳健性能。","Agronomy 2026 16(13):1241 DOI:10.3390\u002Fagronomy16131241","2026-10-02T00:00:00Z",78,{"impact":17,"substance":18,"depth":17,"authority":204,"freshness":60,"relevant":21,"comment":205},14,"PRISMA框架下196项研究的系统综述，方法学扎实、结论可靠，对农业AI土壤应用具较高参考价值。",[207],{"name":200,"url":198},[209,26,27,28,210],"智慧农业","土壤监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[213],{"code":160,"slug":164,"name":214,"path":215,"parent_code":216},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":218},{"candidates":219,"agricultural_evidence":46},[220,226,231,235],{"code":32,"score":121,"supported":46,"title":221,"abstract":223,"tags":225},[222],"artificial intelligence",[224,27],"深度学习",[26,27],{"code":160,"score":227,"supported":46,"title":228,"abstract":229,"tags":230},11,[164],[163],[],{"code":52,"score":68,"supported":46,"title":232,"abstract":233,"tags":234},[],[28],[28],{"code":236,"score":21,"supported":14,"title":237,"abstract":238,"tags":239},"I1",[],[],[209],[241,242],"AI 土壤特性 系统综述","机器学习 土壤 遥感","AI土壤特性系统综述-4286",{"doi":9,"openalex_id":9,"authors":245,"venue":9,"cited_by_count":98,"oa_url":9,"card":246,"direction":103,"ingested_from":251},[],{"tldr":247,"method":248,"finding":249,"direction":103,"opportunity":250},"系统综述2018-2026年196项AI用于土壤特性与农业的研究。","PRISMA 2020框架，分析机器学习与深度学习模型及多源数据融合。","研究转向数据驱动，树集成模型在不同土壤特性上表现稳健。","可探索可解释AI与多源数据融合在土壤特性预测中的泛化能力。","agent","2026-10-06T00:05:04.976398Z",{"id":254,"title":255,"url":256,"summary":257,"summary_zh":258,"content":9,"source_name":259,"source_url":256,"published_at":201,"category":12,"cover_url":9,"hotness":260,"is_selected":14,"score":261,"score_detail":262,"sources":265,"tags":269,"primary_topic":271,"secondary_topics":272,"classification":273,"search_phrases":302,"slug":305,"view_count":20,"doi":306,"paper":307,"created_at":317},4238,"Comparative Performance of Machine Learning and Deep Learning Algorithms for Agricultural Land Mapping in Anambra State, Nigeria","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23106955","Abstract - Accurate delineation of agricultural land is fundamental to agricultural planning, food-security assessment, land-resource management, and monitoring of land-use transformation. However, the heterogeneous and fragmented character of agricultural landscapes in southeastern Nigeria presents substantial challenges for satellite-image classification. This study comparatively evaluated Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) for agricultural land mapping in Anambra State, Nigeria. Remotely sensed imagery was classified into waterbody, dense forest, agricultural land, built-up area, and bare ground using the four algorithms. Model performance was evaluated using 210 validation samples and multiple confusion-matrix metrics, including sensitivity, precision, specificity, negative predictive value, overall accuracy, balanced accuracy, and Matthew’s correlation coefficient. Agricultural land estimates varied from 1,000.26 km² (21.78%) for SVM to 1,099.12 km² (23.93%) for ANN. SVM achieved the strongest overall performance, recording 88.5% sensitivity, 85.2% precision, 75.0% specificity, 83.3% overall accuracy, 81.7% balanced accuracy, and a Matthews correlation coefficient of 0.643. CNN produced the lowest overall accuracy of 74.3%. The findings demonstrate that increased model complexity does not necessarily translate into superior classification performance and establish SVM as an effective approach for regional agricultural land delineation under heterogeneous tropical landscape conditions. Keywords: agricultural land mapping; machine learning; deep learning; Support Vector Machine; Random Forest; artificial neural network; convolutional neural network; remote sensing","摘要——准确划定农业用地是农业规划、粮食安全评估、土地资源管理和土地利用变化监测的基础。然而，尼日利亚东南部农业景观的异质性和破碎化特征给卫星图像分类带来了巨大挑战。本研究比较评估了随机森林（RF）、支持向量机（SVM）、人工神经网络（ANN）和卷积神经网络（CNN）在尼日利亚阿南布拉州农业用地制图中的表现。利用这四种算法将遥感影像分类为水体、密林、农业用地、建成区和裸地。使用210个验证样本和多种混淆矩阵指标评估模型性能，包括灵敏度、精确率、特异度、阴性预测值、总体精度、平衡精度和马修斯相关系数。农业用地估计值从SVM的1,000.26 km²（21.78%）到ANN的1,099.12 km²（23.93%）不等。SVM取得了最强的总体表现，灵敏度为88.5%，精确率为85.2%，特异度为75.0%，总体精度为83.3%，平衡精度为81.7%，马修斯相关系数为0.643。CNN的总体精度最低，为74.3%。研究结果表明，模型复杂度的增加并不必然带来更优的分类性能，并确立了SVM作为异质性热带景观条件下区域农业用地划定的有效方法。关键词：农业用地制图；机器学习；深度学习；支持向量机；随机森林；人工神经网络；卷积神经网络；遥感","International Journal of Emerging Trends in Engineering and Development",25,61,{"impact":45,"substance":263,"depth":121,"authority":45,"freshness":137,"relevant":21,"comment":264},20,"尼日利亚区域案例研究，方法对比扎实但影响范围限于学术圈，可作为遥感与农业AI主题的参考素材。",[266,267],{"name":259,"url":256},{"name":259,"url":268},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23106954",[209,26,27,28,270],"农地制图",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":274},{"candidates":275,"agricultural_evidence":46},[276,282,286,292,298],{"code":32,"score":277,"supported":46,"title":278,"abstract":280,"tags":281},27,[49,279],"deep learning",[224,27,49,279],[26,27],{"code":52,"score":53,"supported":46,"title":283,"abstract":284,"tags":285},[],[28,56],[28],{"code":287,"score":168,"supported":46,"title":288,"abstract":289,"tags":291},"A8",[],[290],"粮食安全",[],{"code":293,"score":168,"supported":46,"title":294,"abstract":295,"tags":297},"J1",[],[296],"农业规划",[],{"code":236,"score":21,"supported":14,"title":299,"abstract":300,"tags":301},[],[],[209],[303,304],"农业人工智能 农地制图 智慧农业 机器学习","农业人工智能 农地制图","农业人工智能农地制图智慧农业机器学习-4238","10.5281\u002Fzenodo.23106955",{"doi":306,"openalex_id":308,"authors":309,"venue":259,"cited_by_count":98,"oa_url":256,"card":312,"direction":192,"ingested_from":106},"W7219591984",[310],{"name":311,"orcid":9},"J.I. Igbokwe, C.E Ejikeme, J.O and Obi",{"tldr":313,"method":314,"finding":315,"direction":192,"opportunity":316},"比较RF、SVM、ANN、CNN在尼日利亚农业用地制图中的表现","遥感影像分类，210个验证样本，多种混淆矩阵指标评估","SVM总体精度最高（83.3%），CNN最低（74.3%），模型复杂度不保证性能","在碎片化热带景观中，可探索轻量级集成学习或迁移学习以平衡精度与效率","2026-10-05T23:30:27.987196Z",{"id":319,"title":320,"url":321,"summary":322,"summary_zh":323,"content":9,"source_name":324,"source_url":321,"published_at":325,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":326,"score_detail":327,"sources":330,"tags":332,"primary_topic":334,"secondary_topics":335,"classification":345,"search_phrases":384,"slug":387,"view_count":68,"doi":388,"paper":389,"created_at":409},2659,"Application of Remote Sensing and Machine Learning in Sustainable Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18189472","Global agriculture is undergoing a period of profound transformation, driven by the need to increase food production in a context characterized by climate change, the degradation of natural resources and increasing pressure on agricultural ecosystems 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